Body Area Network Challenges and Solutions

This book provides a novel solution for existing challenges in wireless body sensor networks (WBAN) such as network lifetime, fault tolerant approaches, reliability, security, and privacy. The contributors first discuss emerging trends of WBAN in the present health care system. They then provide possible solutions to challenges inherent in WBANs. Finally, they discuss results in working environments. Topics include communication protocols of implanted, wearable and nano body sensor networks; energy harvesting methodologies and experimentation for WBAN; reliability analysis and fault tolerant architecture for WBAN; and handling network failure during critical duration. The contributors consist of researchers and practitioners in WBAN around the world.


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EAI/Springer Innovations in Communication and Computing

R. Maheswar G. R. Kanagachidambaresan R. Jayaparvathy Sabu M. Thampi Editors

Body Area Network Challenges and Solutions

EAI/Springer Innovations in Communication and Computing Series Editor Imrich Chlamtac, CreateNet, Trento, Italy

Editor’s Note The impact of information technologies is creating a new world yet not fully understood. The extent and speed of economic, life style and social changes already perceived in everyday life is hard to estimate without understanding the technological driving forces behind it. This series presents contributed volumes featuring the latest research and development in the various information engineering technologies that play a key role in this process. The range of topics, focusing primarily on communications and computing engineering include, but are not limited to, wireless networks; mobile communication; design and learning; gaming; interaction; e-health and pervasive healthcare; energy management; smart grids; internet of things; cognitive radio networks; computation; cloud computing; ubiquitous connectivity, and in mode general smart living, smart cities, Internet of Things and more. The series publishes a combination of expanded papers selected from hosted and sponsored European Alliance for Innovation (EAI) conferences that present cutting edge, global research as well as provide new perspectives on traditional related engineering fields. This content, complemented with open calls for contribution of book titles and individual chapters, together maintain Springer’s and EAI’s high standards of academic excellence. The audience for the books consists of researchers, industry professionals, advanced level students as well as practitioners in related fields of activity include information and communication specialists, security experts, economists, urban planners, doctors, and in general representatives in all those walks of life affected ad contributing to the information revolution. About EAI EAI is a grassroots member organization initiated through cooperation between businesses, public, private and government organizations to address the global challenges of Europe’s future competitiveness and link the European Research community with its counterparts around the globe. EAI reaches out to hundreds of thousands of individual subscribers on all continents and collaborates with an institutional member base including Fortune 500 companies, government organizations, and educational institutions, provide a free research and innovation platform. Through its open free membership model EAI promotes a new research and innovation culture based on collaboration, connectivity and recognition of excellence by community. More information about this series at http://www.springer.com/series/15427

R. Maheswar  •  G. R. Kanagachidambaresan R. Jayaparvathy  •  Sabu M. Thampi Editors

Body Area Network Challenges and Solutions

Editors R. Maheswar Department of ECE Sri Krishna College of Technology Coimbatore, TN, India

G. R. Kanagachidambaresan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai, TN, India

R. Jayaparvathy Department of ECE SSN College of Engineering Chennai, TN, India

Sabu M. Thampi Indian Institute of Information Technology and Management - Kerala (IIITM-K) Trivandrum, KL, India

ISSN 2522-8595     ISSN 2522-8609 (electronic) EAI/Springer Innovations in Communication and Computing ISBN 978-3-030-00864-2    ISBN 978-3-030-00865-9 (eBook) https://doi.org/10.1007/978-3-030-00865-9 Library of Congress Control Number: 2018961704 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To our mentors, friends, and collaborators whose support made this project possible.

Preface

Wireless monitoring has become an essential function in current medical practice. The main motivation of a wireless body sensor network (WBSN) is to make it easier to care for people. Developing countries face huge medical facility shortages and can only make up for it through remote patient monitoring, and this could entail utilizing existing medical resources like doctors and health care workers. The changes associated with aging populations in developing countries will require a structure to face challenges like elderly monitoring and providing in-person care. In the modern technology, individualized health care remains an emerging challenge and mainly to fulfil the emergency requirements for elderly people. Current technology in wireless monitoring of patients faces challenges like energy reliability, trustworthiness, and security. In some case, health care and remote monitoring are conducted through wearable and implanted nodes. Implanted nodes must be recharged frequently and energy-efficiency issues, and reducing the frequency of recharging is the main motto. Technological developments are happening so rapidly that nanorobots are involved in monitoring physiological signals deep inside the body. The hacking of medical data can lead to fatal situations for monitored subjects. Sensor accuracy and reusability are the major challenges in WBSNs. Continuous monitoring of physiological parameters, data accuracy, sensing rate, recovery rate, and data trustworthiness are the main factors in decision-making in body area networks. Decision-making on the basis of values from sensors varies from subject to subject based on region, age, sex, and history; such complications create a formidable challenge and makes WBSNs more dependent. The extreme environment creates insecure and inaccurate conditions for the sensors. This book addresses the solutions to the challenges faced by WBSNs. The solutions to the problems and challenges are also addressed through machine learning algorithms.

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Preface

A nano-body sensor network has the capability to recharge on its own and can work autonomously without interruption. Better machine intelligence would replace human intervention and allow WBSNs to be more autonomous in handling critical situations unseen by human experts. Coimbatore, TN, India Chennai, TN, India  Chennai, TN, India  Trivandrum, KL, India 

R. Maheswar G. R. Kanagachidambaresan R. Jayaparvathy Sabu M. Thampi

Acknowledgements

We are so thankful to all the contributors for their tremendous efforts in producing this book. Their enthusiasm and flexible support allowed the book to see the light of day. I would also like to thank all our reviewers for providing unbiased reviews, leading to quality material within a tight schedule. I would also like to thank the EAI Springer editor for providing this opportunity to participate in its global research platform. The generous support of our institute’s management (SSN College of Engineering, Sri Krishna College of Technology, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, and Indian Institute of Information Technology and Management—Kerala (IIITM-K) allowed us to create vastly improved material in this exciting field of research. I hope that the book will serve as a valuable resource for readers and researchers.

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Contents

 ody Area Network (BAN) for Healthcare by Wireless B Mesh Network (WMN)��������������������������������������������������������������������������������������   1 Raluca Maria Aileni, George Suciu, Cristina Mihaela Balaceanu, Cristian Beceanu, Petrache Ana Lavinia, Carmen-Violeta Nadrag, Sever Pasca,Carlos Alberto Valderrama Sakuyama, and Alexandru Vulpe Uncured Disease Rectification Using Net Collaborating Systems ����������������  19 M. Ramalatha, M. Alamelu, and S. Kanagaraj  ecurity and Privacy Issues in Remote Healthcare S Systems Using Wireless Body Area Networks ������������������������������������������������  37 R. Nidhya and S. Karthik  ata Reliability and Quality in Body Area Networks D for Diabetes Monitoring������������������������������������������������������������������������������������  55 Geshwaree Huzooree, Kavi Kumar Khedo, and Noorjehan Joonas  achine Learning-Based Cognitive Support M System for Healthcare���������������������������������������������������������������������������������������  87 M. Ramalatha, S. N. Shivappriya, and K. Malarvizhi  AR Analysis of UWB Antennas for Wireless S Body Area Network Applications �������������������������������������������������������������������� 105 Doondi Kumar Janapala, M. Nesasudha, and T. Mary Neebha  ail Safe Routing Algorithm for Green Wireless F Nano Body Sensor Network (GWNBSN)�������������������������������������������������������� 131 G. R. Kanagachidambaresan, R. Maheswar, R. Jayaparvathy, Sabu M. Thampi, and V. Mahima Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

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Contributors

Raluca Maria Aileni  Faculty of Electronics, Telecommunication and Information Technology, Politehnica University of Bucharest, Bucharest, Romania M.  Alamelu  Department of Information Technology, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India Petrache Ana Lavinia  Beia Consult International, Bucharest, Romania Cristina Mihaela Balaceanu  Beia Consult International, Bucharest, Romania Cristian Beceanu  Beia Consult International, Bucharest, Romania Geshwaree Huzooree  Department of Information Technology, Curtin Mauritius, Moka, Mauritius Doondi  Kumar  Janapala  RF Research Laboratory, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India R.  Jayaparvathy  Department of ECE, SSN College of Engineering, Chennai, Tamil Nadu, India Noorjehan  Joonas  Central Health Laboratory, Victoria Hospital, Ministry of Health & Quality of Life, Candos, Mauritius G.  R.  Kanagachidambaresan  Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India S.  Kanagaraj  Department of Information Technology, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India S. Karthik  SNS College of Technology (Affiliated to Anna University, Chennai), Coimbatore, Tamil Nadu, India Kavi Kumar Khedo  Department of Digital Technologies, University of Mauritius, Reduit, Mauritius xiii

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Contributors

R. Maheswar  Department of ECE, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India V.  Mahima  Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India K.  Malarvizhi  Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India T.  Mary  Neebha  RF Research Laboratory, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India Carmen-Violeta Nadrag  Beia Consult International, Bucharest, Romania M.  Nesasudha  RF Research Laboratory, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India R.  Nidhya  Madanapalle Institute of Technology and Science (Affiliated to Jawaharlal Nehru Technical University, Anantapuram), Angallu, Andhra Pradesh, India Sever  Pasca  Faculty of Electronics, Telecommunication and Information Technology, Politehnica University of Bucharest, Bucharest, Romania M.  Ramalatha  Department of Electronics and Communication, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India Carlos  Alberto  Valderrama  Sakuyama  Electronics and Microelectronics Department, Mons University of Bucharest, Mons, Belgium S. N. Shivappriya  Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India George  Suciu  Faculty of Electronics, Telecommunication and Information Technology, Politehnica University of Bucharest, Bucharest, Romania Beia Consult International, Bucharest, Romania Sabu M. Thampi  Indian Institute of Information Technology and Management Kerala (IIITM-K), Trivandrum, Kerala, India Alexandru  Vulpe  Faculty of Electronics, Telecommunication and Information Technology, Politehnica University of Bucharest, Bucharest, Romania Beia Consult International, Bucharest, Romania

About the Editors

R. Maheswar  Dr. R. Maheswar completed his B.E. in Electronics and Communication Engineering from Madras University in 1999, his M.E. in Applied Electronics from Bharathiyar University in 2002, and his Ph.D. in Wireless Sensor Networks from Anna University in 2012. He has about 16 years of teaching experience at various levels and is presently working as a professor in the Electronics and Communication Engineering Department, Sri Krishna College of Technology, Coimbatore. He has published 40 papers in international journals and in the proceedings of international conferences. His research interests include wireless sensor networks, queueing theory, and performance evaluation. G.  R.  Kanagachidambaresan  Dr. G.  R. Kanaga chidambaresan received his Bachelor’s degree in Electrical and Electronics Engineering in 2010, his Master’s in Pervasive Computing Technologies in 2012, and his Ph.D. in Information and Communication Engineering in 2017. He is currently an Associate Professor in the Department of Computer Science and Engineering at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology. His main research interests include body sensor networks, fault-tolerant wireless networks, and the Internet of Things.

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About the Editors

R. Jayaparvathy  Dr. R. Jayaparvathy is a Professor in the Department of Electronics and Communication Engineering. She has almost 30 years of teaching experience. She graduated with distinction from the Government College of Technology in Coimbatore in 1987. She obtained her post graduate degree in Applied Electronics from Coimbatore Institute of Technology, Coimbatore, and her Ph.D. in Information and Communication Engineering from AU-KBC Research Centre, Anna University, Chennai. Prior to joining SSN she served as a faculty member at the Coimbatore Institute of Technology, Coimbatore, for 24 years. She has been a member of the Academic Council, Research Board, Board of Studies (CIT and PSG College of Technology), and Chief Superintendent of Autonomous Examinations in CIT.  She has served as Auditor for Technical Auditing implemented by Anna University, Coimbatore. She also worked as Member-Research Staff at the AU-KBC Research Centre for 3 years. She  has been the principal investigator of AICTE-­ sponsored projects, and her prototype project for elephant intrusion detection in forest border areas conducted in collaboration with the Forestry Department was widely reported by the media. Her areas of interest include wireless MAC and wireless sensor networks, including body area networks and embedded systems. She has organized national-level technical workshops and national and international conferences. Dr. Jayaparvathy is a senior member of IEEE and a research supervisor at Anna University. Many candidates have completed and some are pursuing their Ph.D. under her supervision in the areas of wireless and embedded systems. She has advised a number of postgraduate and undergraduate projects, with publications leading to best paper awards. She has served as  an expert on AICTE-AQIS proposal evaluations. She is a reviewer for many reputable journals and part  of the Technical Program Committee at many conferences.

About the Editors

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Sabu M. Thampi  Dr. Sabu M. Thampi is an Associate Professor at the Indian Institute of Information Technology and Management-Kerala (IIITM-K), Technopark Campus, Trivandrum, Kerala, India. He completed his Ph.D. in Computer Engineering under the supervision of Dr. K.  Chandrasekaran from the National Institute of Technology Karnataka. Dr. Sabu has several years of teaching and research experience at various institutions in India. His research interests include sensor networks, Internet of Things, authorship analysis, social networks, nature-inspired computing, very large databases, image forensics, video surveillance, and secure localization. He has authored and edited several books published by reputable international publishers and papers in academic journals and international and national proceedings. Dr. Sabu has served as guest editor for special issues in several international journals and as program committee member for many international conferences and workshops. He has cochaired several international workshops and conferences. He has initiated and is also involved in the organization of several annual conferences/symposia: the International Conference on Advances in Computing, Communications and Informatics, International Conference on Computing and Network Communications, Symposium on Intelligent Systems Technologies and Applications, Symposium on Security in Computing and Communications, Symposium on Intelligent Informatics, Symposium on Signal Processing and Intelligent Recognition Systems, and others. Sabu is currently serving as Editor at the Journal of Network and Computer Applications and the Journal of Applied Soft Computing, both published by Elsevier. He is also Associate Editor for IEEE Access and International Journal of Embedded Systems, published by Inderscience (UK) and reviewer for several reputable international journals. Dr. Sabu is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and a member of the IEEE Communications Society, IEEE SMCS, and ACM. He is the founding chair of the professional chapter of ACM Trivandrum. In 2012, Dr. Sabra was honored with the ASDF Award for Best Computer Science Faculty.

Body Area Network (BAN) for Healthcare by Wireless Mesh Network (WMN) Raluca Maria Aileni, George Suciu, Cristina Mihaela Balaceanu, Cristian Beceanu, Petrache Ana Lavinia, Carmen-Violeta Nadrag, Sever Pasca, Carlos Alberto Valderrama Sakuyama, and Alexandru Vulpe

1  Introduction Existing medical resources cannot satisfy the future healthcare demands of different types of patients (older or younger) [1]. The resources are quite limited, and it is impossible for most patients to stay a long time in the hospital because of economic restrictions, work, and other personal reasons, even though their health status must be monitored in real time or frequently. As a result, wireless monitoring medical systems will become part of mobile healthcare centers with real-time monitoring in the future [2]. Wireless body area networks supporting healthcare applications offer different contributions at monitoring, diagnosis, and therapeutic levels (Fig. 1). They cover real-time medical information obtained from different types of sensors with secure data communication and low power consumption. Due to the increasing interest in the applications of this type of networks, several articles dealing with different aspects of such systems have been published recently.

R. M. Aileni (*) · S. Pasca Faculty of Electronics, Telecommunication and Information Technology, Politehnica University of Bucharest, Bucharest, Romania G. Suciu · A. Vulpe Faculty of Electronics, Telecommunication and Information Technology, Politehnica University of Bucharest, Bucharest, Romania Beia Consult International, Bucharest, Romania C. M. Balaceanu · C. Beceanu · P. Ana Lavinia · C.-V. Nadrag Beia Consult International, Bucharest, Romania C. A. V. Sakuyama Electronics and Microelectronics Department, Mons University of Bucharest, Mons, Belgium © Springer Nature Switzerland AG 2019 R. Maheswar et al. (eds.), Body Area Network Challenges and Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-00865-9_1

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Fig. 1  Wireless body area [3]

body temp. sensor glucose sensor

gateway to other services

blood pressure sensor insulin pump sensor / actuator with low-power TRx

E-Health and Telemedicine are two areas that are leveraging current wireless communication technologies to provide emergency medical services, enable outpatient monitoring and treatment, facilitate patient recovery, and directly connect doctors and nursing staff with patients [4]. WBAN healthcare applications can offer valuable contributions to improve patient healthcare, including diagnosis and therapeutic monitoring. This technology, still under development, is generally based on wireless communications technologies. Patients, while performing their activities comfortably at home or outdoors, can be monitored by the medical staff [5, 6]. In this field, data reliability and energy consumption (considering 24/7 monitoring) are fundamental characteristics to consider when choosing appropriate WBAN sensor nodes [7]. These nodes operate in close proximity to the human body collecting data for various medical and non-medical applications. Medical bands used in WBAN provide physiological data from sensor nodes. They are chosen in such a way that it reduces interference and thus increases the coexistence of sensor node devices with other network devices available at medical centers. The collected data is sent to stations using medical gateway wireless boards. There are different types of devices that make up a WBAN architecture: • Sensor nodes—these form the base of any WBAN. There are various sensors to monitor the physiological parameters such as BP, ECG, Pulse Rate, or EEG. These sensor nodes work in close proximity to our body and capture signals that are passed on to another unit for analysis. This type of sensors can be monofunctional or multifunctional. Sensor nodes can be implantable, body surface, and external. • Base Station—local processing system that transmits the information obtained to those interested in a health assessment. The data can be collected locally so that the patient can bring the transmitter home without having to stay in the hospital. • Central Server—a database is maintained and further sent to a specialist for consultancy or proper medical guidance.

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2  Wireless Mesh Network Sensor wireless network has become an important technology, and wireless sensors can be used for patients to permanently monitor their physiological status. In this case, Wireless Mesh Networks (WMN) [8] are used to transmit the necessary information from the wireless body sensor network to the network architecture. WMN is considered as an extension of the LAN, with a much better range, and leading to fewer wires. The WMN consists of two network architectures, ad hoc network, and wireless LAN [9]. Broadband networks are used on a much wider scale in a wireless mesh network configuration. These WMNs are used to expand or improve the Internet connection for mobile phone customers located further away from the wireless network. In WMN networks, nodes are composed of network routers as well as clients. Each node works not only as a host but also as a router, redirecting packets or data to other nodes that are not in the direct wireless area. WMN is self-configuring and self-organizing, and network nodes, thus automatically establishing and maintaining connectivity between them, and leading to lower cost, easy network maintenance, and robustness. Specific sensor network health applications allow for the provision of interfaces for people with disabilities, integrated patient monitoring, diagnosis, administration of drugs in hospitals, telemonitoring of human physiological data, and monitoring hospital patients. More precisely, a WSN can be used to monitor healthcare activities such as: • Telemonitoring of human physiological data: The collection of physiological data by sensor networks that can be stored in a database for a more extended period is subsequently used for the necessary long term medical research. • Follow-up of doctors and patients in a hospital: Each patient has a small sensor node attached to them. Each sensor accomplishes the function for which it has been set/configured. Doctors can monitor through sensors, as well as locate and inform other doctors (Fig. 2). Fig. 2  Wireless mesh sensor network

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• Hospital medication: If sensor nodes can be attached to drugs, then, to better control the prescription medicines, patients can have sensory nodes identifying allergies and medications. This computerized system has shown that it can help minimize side effects of drugs [10].

3  Electromagnetic Radiation and Radio Waves Radio waves are most used for the wireless transmission of information as well as for public and private communications. Classical transmission modes are frequency modulation (FM), amplitude modulation (AM) and pulse modulation. Transmission of data involves a frequency band with width proportional to the data density. For instance, the data bandwidth for voice is around 10,000  Hz and for high fidelity 20,000 Hz [11]. Electromagnetic radiation brings us heat and light, like the sun’s energy that all plants need for photosynthesis and growth. James Clerk Maxwell discovered the existence of electromagnetic waves in 1873. Electromagnetic radiation has the following properties: • • • • • • • •

It can be manufactured or found in nature. It does not require a particular medium for propagation. Travels with the speed of light. It carries energy as it propagates. The higher the frequency, the higher the energy associated with the wave. Its transferred energy may be sufficient to ionize the matter on which it impinges. It can be utilized to transmit information. It can be reflected or refracted. It can be split to form diffraction patterns, travels in straight lines, and passes through walls. Electromagnetic radiation emanates from [12]:

• • • • • • • • •

Electrical appliances. Electronic equipment. Computers and related equipment. Cell phone masts. Microwave ovens. House-wiring. Cellular (mobile) phones. Information networks. Different voltage level power lines.

The impact of electromagnetic radiation on human health is called electromagnetic hypersensitivity (EHS). EHS represents a physiological process associated with the disease. In addition, researchers proved that it is related to the significant metallic element [13]. The solid metallic element attached to proteins within tissues and organs is considered low hazard. However, researchers have observed that

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magnetic field generated by mobile phones and other wireless devices can cause the release of mercury vapor from dental amalgam. Mercury gradient diluted in saliva can increase in amalgam carriers [13]. Cell phones are also used in close proximity to brain tissue. Therefore, brain tissue can be influenced by electromagnetic waves. Numerous studies have shown that human sensory system and its behavior are affected closely by the radiofrequency electromagnetic waves coming from the base stations (BTS) [14]. The increase in wireless devices and using of GSM (Global System for Mobile communications) technology is affecting human health and body function, and electromagnetic field components have the potential to distort brain formations (such as meningioma) [15].

4  Low-Power Computing Design for Wearable Devices Low-power design is crucial for new technologies like internet of things, and artificial intelligence (AI), which mainly uses wireless sensor networks. Initiatives such as a fully programmable architecture support a software-defined radio—a high-end signal processing application. This software-defined radio signal processing called SODA handles different implementations for low-power design [16]. SODA uses DSP processors with intrinsic operations, clustered Register files with reduced number of ports, and a smaller Instruction Fetch Logic. All these optimizations, allowing for a lower energy consumption, illustrate what can be done in terms of low-power design. One of the aspects to consider regarding wireless devices and power consumption is the payload size of the wireless technology. A wireless device takes about 60% of its energy when preparing a connected device to begin its normal operation. Regarding low power, Fig. 3 shows that the 6LoPAN (IPv6 over Low-Power WPAN) header consumes 2.8% energy for maximum 10-bit payloads. It has less than 2% for frame lengths. The IPv6 128-bit lengths become very small due to compression in case of local transmission [17]. Payload, data size, and sampling frequency are equally related to the accuracy and type of the measured magnitude. Applications involved in the active monitoring include sending data such as heartbeats, breathing, or posture. The application domain (medicine, physics, etc.) will dictate the type, size, and quality of the sensor used. For instance, health measurement can use magnetic fields, ultrasound, laser diodes, motion sensors, and ECG (electrocardiograph) electrodes that measures the electrical activity of the heart. A body sensor network can favor GSR (Galvanic Skin Response) or magnetometers [18]. Low power means also low-voltage, low-frequency devices and the applications of techniques such as voltage/frequency scaling [19]. Studies about low power in multimedia devices follow consequences of increasing voltage and errors that come with it. To overcome the errors, we suggest logic complexity reduction by obtaining, for example, approximated Full Adder Cells (FA) cells. Therefore, we have shorter critical paths, enabling voltage scaling [19].

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Fig. 3  Energy overhead in preparation of a wireless connection

This approximation experiment was evaluated. Post-layout simulations show power savings of up to 60% and area savings of up to 37% with an insignificant loss of precision on the displayed result.

5  Radiofrequency Interference In recent years, there has been an increase in medical device issues such as cardiac pacemakers and wheelchairs that have been electrically driven due to interference from other devices; this phenomenon is called radiofrequency interference. The main inconveniences with this are the following: • Increasing the number of electronically controlled medical devices that do not have electronic security. • Increasing the number of sources in the environment. Some of the most important radiofrequency sources are mobile phones, transmitters mounted on vehicles or portable.

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Some medical equipment has a high sensitivity to digital modulation that certain wireless systems use. The international standard on the radiofrequency protection of medical equipment is represented by IEC 60601-1-2 of the International Electrotechnical Commission. This standard establishes a minimum immunity level of 3 V/m in the frequency range 26–1000 MHz. Existing technology aims to protect most medical equipment from radio fields that are 3 V/m more powerful than standard. The main procedures such as screening, grounding, and filtering are not very expensive if they are introduced in the initial design of the electronic system [20]. Healthcare applications have the shorter bandwidth for the newly allocated frequencies. The current bandwidth for 2.4 and 5 GHz 802.11a/b/g is 383 MHz (not including 255  MHz introduced in the 5  GHz band). This compares to WMTS (Wireless Medical Telemetry Service), which has a bandwidth of 13 MHz. This was quite enough for several wireless connected monitors back in 1999, when they were the only wireless medical device, but not in today’s hospitals. The 802.11 bandwidth is 30–400 times higher than WTMS. Another dedicated band is the MICS, Medical Implant Communications Service, with a bandwidth of 3 MHz. The bandwidth is influenced by the capacity; the higher the bandwidth, the higher the number of users that can be supported. To be effective and secure, they need a larger bandwidth. Due to the known wireless network problems, the alternatives found for wireless applications were by replacing short-range cables within the company’s network with cables up to 30 m. The connectivity options for the company’s network were rather limited and included WMTS and ISM (Industrial Scientific Medical). The short-range cable replacement applies to wireless sensors in BAN (Body Area Network) networks that are limited to ISM [21]. With the increased growth of electromagnetic radiation equipment, especially wireless communications such as mobile phones and Wi-Fi transmitters, over 50 years, it has become clear that radiofrequency interference can affect human health. Studies are carried out on the impact of radiofrequency interference at the specific rate of absorption (SAR), increase of cell temperature, blood glucose levels, and change of the RNA/DNA structure, to highlight the electromagnetic effects on human health and to achieve the necessary reduction systems of radio frequency interference. The brainwaves are detected by recording the electrical signal of the brain by noninvasive method such as electroencephalography (EEG) [22] or by using invasive microelectrodes for deep signal recording in case of epilepsy.

6  WBAN Systems for Healthcare As WBAN systems are expected to become more widespread in medical applications, several studies present new architectures and improvements. Research in telemonitoring describes a sensor network system for detecting and sending signals from the patients [23]. The author also presents a series of improve-

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ments that need to be made for such a system, in order for it to become more practical and to be successfully applied in the medical field. A comparison between the current systems and a future concept for a medical monitoring platform is also presented, in order to highlight the changes needed. The study takes into consideration sensor optimization and energy saving techniques in order to implement a large-­ scale patient monitoring system. The software component gathers and stores data, which are later displayed on a graphical user interface (GUI) and interpreted by specialists. In addition, the approach taken by the author envisions the extension of the application, as there are mentioned different wireless standards to be used. In their article [24], Jeevan et al. mention three different possibilities of dealing with the sensor nodes design when developing a WBAN system for medical healthcare: creating a sensor node from the scratch, using predefined components for creating it or adopting an existing node. The first option is described as being the most expensive but also the most efficient. However, several other parameters need to be taken into consideration when designing a new sensor node, such as: –– –– –– ––

To be wearable, this depends on sensors’ size, weight. Security. How reliable the communication is, based on communication requirements. Interoperability.

The paper also highlights the importance of monitoring multiple patients at a time while using a system as simple as possible in order not to require a high computational time. This also makes the system suitable to be used for monitoring people working in harsh environments, and have a high efficiency. This paper [25] presents a full architecture of a WBAN system, and its practical implementation. The system contains two nodes, which collect and send continuously data such as HR (heart rate), body temperature and patient’s location. At the same time, the base station requests data and coordinates the entire system. It is equipped with an ARDUINO board, which uses both WiFi and ZigBee for accessing data. The software component is an intuitive GUI for the medical personnel and displays data in a user-friendly manner. The system has also been tested under ­multiple conditions, comparing data gathered both when the patients were relaxed and after doing physical activities. Figure  4 shows the network architecture as described by the authors:

Fig. 4  The network architecture

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7  Data Management: Edge Computing vs. Cloud Computing The edge system is taken into account by energy management, AI, Cloud, Fog researchers. They want to place the actions that require cognitive tasks at the edge of the network, which means certain edge equipment, will handle those tasks. At the edge layer, there are various objects connected wirelessly with the network, exchanging information (e.g., smartphones, cars, smart city equipment) [26]. Edge computing provides energy efficiency replacing data centers and eliminating the overhead resulted from big data. They form small cells giving the possibility of saving energy when sending information, better timing, enough bandwidth to execute the cognitive tasks and other new AI tasks. One example is the voice recognition system. Cloud does not have enough computation ability to handle the new cognitive tasks, and it cannot face the traffic volume resulted. However, Cloud handles certain tasks and the new architectures proposed include both Cloud and Edge (Fig. 5). The Mobile crowd sensing service is a good example to show how Cloud and Edge work together. MCS (Mobile Crowdsensing) relates to human tasks using wireless objects enables collaboration between individuals. MCS it has applications in healthcare, transport, and social care. MCS require sensing devices and analyzing

Fig. 5  Cloud is not replaced by Edge, some information travels from Edge to Cloud. Certain tasks are taken by Cloud [26]

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Fig. 6  MCS deployments. (a) Cloud-based crowdsource architecture. (b) MCS architecture powered by edge infrastructure [27]

functions. MCS implies load on network, traffic on cloud servers, delays, not enough security. Mobile Edge computing can solve these issues, by managing the applications near the mobile devices and Edge servers. The following image presents the MCS running on a cloud architecture compared to running on Edge architecture [27, 28]. Figures 5 and 6 present mobile crowd sensing deployments in cloud architecture and the edge.

8  W  ireless Body Area Network: Future Perspective for Self-­Monitoring Systems Wireless body area networks support some interesting applications. These applications include several areas of research such as smart healthcare, assisted elderly living, emergency response, and interactive gaming [29]. The rising healthcare costs and the aging of the world population contribute to the advancements in telemedicine network for the delivery of several healthcare services [30]. Telemedicine is considering health information systems and telecommunication technologies that can allow scientists to serve more patients. Do to the signals that body sensors provide, gathered information could be processed efficiently to obtain accurate physiological estimations and to let a distant doctor have real-time opinions on medical diagnosis and prescription [31]. Such smart health system can provide applications for a diagnostic procedure, maintenance of a chronic condition, and supervised recovery from a surgical procedure.

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Patient monitoring applications control vital signals and provide real-time feedback and information helping the recovery of the patient. In such situations, we can keep the patient under doctor monitoring under natural physiological states without constraining their normal activities and without injuring his high cost. Daily-life activity monitoring monitors the actions during the daily life of patients with some specific diseases, while in-hospital monitoring focuses on cases in which patients must stay in a hospital for intensive care and observations, sometimes for a prolonged period. Wireless body area network can provide continuous measurements of the physiological parameters and allow for better diagnosis of organ failures and faster detection of emergencies. Such remote monitoring system will be safer, more convenient, and cheaper. In this field, many works have been proposed in the literature. Some of them tried to design a generic framework able to support most of cases, while others tried to study specific diseases. Cardiovascular diseases, diabetes, cancer detection, Parkinson, asthma, Alzheimer’s, and artificial retina are some examples of specific remote patient monitoring applications [32]. Wireless medical applications show great promise in improving the lives of people and satisfying many requirements of old people by enabling them to live safely, securely, healthily, and independently. Since wireless medium provides a very convenient way for information transmission, wireless technologies involved in sensor communication as well as the communication between the base station and sensors. The specific requirements of WBAN are as follows: • Reliability: Data sent by WBAN sensors concern health information for which high reliability is required. • Latency: Some medical applications handling emergency data cannot tolerate long response time. Thus, real-time transmission with performance guarantee is required. • Security: such systems handle personal and critical data; the security and privacy of such data are becoming important issues. • Power consumption: Battery replacement in WBAN is easy, so there is less focus on power consumption, for some scenarios. These requirements may differ while considering the different operational environments and characteristics of each wireless body area network application. In fact, applications for rehabilitation aim to capture movements and postures of patients for monitoring his motor activities during rehabilitation therapy. Possible clinical applications include cognitive rehabilitation such as cognitive impairment or brain injury treatments, as well as motor rehabilitation such as post-stroke rehabilitation, post-surgery rehabilitation, post-accident rehabilitation, or post-disease rehabilitation. As many sensors are used, taking into account the proximity of the nodes on the body and interferences should be considered at network layers to provide reliable communication. Besides, to correctly get the phenomenon being monitored, sensors should be sampled at high frequencies. The system must show high accuracy in data collection and data processing to extract correct medical information.

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It should also support real-time communications with guaranteed delays to deliver real-time feedback to the patient during rehabilitation sessions to allow the patient to adjust his movements immediately. As the health of patients is involved, the system must guarantee the delivery of alerts, such as falls of elderly during exercising, within strict delay constraints. Energy concerns can be considered for elderly or impaired patients to avoid burdensome battery charging. Biofeedback also offers to users the ability to continuously monitor body parameters such as temperature, heartbeat rate, and arterial blood pressure in an efficient way [33]. Even when the application feeds this biological information back to the user, the wireless technology adopted must consider the intrinsic characteristics of the medium, such as interferences. Such applications should address sensor node energy constraints. In fact, sensor size constraints limit battery capacity, and if a sensor stops working, a health parameter is lost. Besides, these physiological applications require designing solutions to address new challenges in efficiency, cost, and user interface. For such applications, it is imperative to transform raw sensor data into meaningful data for both patients and medical staff. Many provided solutions rely on Bluetooth-enabled mobile devices, such as a Smartphone. Telemedicine field also handles sensitive and important data, since it is related to human life. Detection of medical emergencies on the basis of monitoring of patients in real time, must be correlated with transmission parameters and latency over a wireless network. For example, Wi-Fi cannot provide timing guarantees on packet delivery, while beacon-Enabled ZigBee can provide real-time communication by supporting GTS.

9  Conclusions The important thing is to integrate Bluetooth on the smart sensor platform in order to send data independently to the gateway. A challenge is to reduce electromagnetic radiation by choosing a low radiant wireless device and send to the gateway only critical events (biomedical parameters values that are not repetitive in discrete time). The main trend in small electronics is in designing the wearable technologies for healthcare, sport, emergency services (fire fighters), or space suits for harsh environments. The main objective is to obtain wearable technology with new characteristics such as light, ultrathin, low power computing, and energy autonomy using photovoltaic cells or piezoelectric devices. An efficient wearable monitoring system should be based on a WBAN topology composed by a main board and several sensor nodes, each having the role to capture, A/D-convert the signal, sample the signal, and send wirelessly the vital signals for respiration rate, heart rate, blood pressure, ECG, oxygen saturation, and glucose concentration.

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Acknowledgments  This has been supported in part by UEFISCDI Romania and MCI through projects ESTABLISH, [email protected], EmoSpaces and TelMonAer, and funded in part by European Union’s Horizon 2020 research and innovation program under grant agreement No. 777996 (SealedGRID project) and No. 787002 (SAFECARE project).

References 1. D.  Rathee, R.  Savita, S.K.  Chakarvarti, V.R.  Singh, Recent trends in Wireless Body Area Network (WBAN) research and cognition based adaptive WBAN architecture for healthcare. J. Health Technol. 4(3), 239–244 (2014) 2. R.K. Kachroo, R. Bajaj, A novel technique for optimized routing in wireless body area network using genetic algorithm. J. Telecommun. Electron. Comput. Eng. 10(2), (2015) 3. S. Sindhu, S. Vashisth, S.K. Chakarvarti, A review on Wireless Body Area Network (WBAN) for health monitoring system: implementation protocols. Commun. Appl. Electron. 4(7), 16–20 (2016) 4. F. Hafez, F. Hesham, Design and implementation of wireless sensors network and cloud based telemedicine system for rural clinics and health centers. Int. J. Sci. Eng. Res. 6(2), 478 (2015) 5. J.Y. Khan, M.R. Yuce, Wireless Body Area Network (WBAN) for Medical Applications, New Developments in Biomedical Engineering (InTech, Rijeka, 2010) 6. A. Milenkovic, C. Otto, E. Jovanov, Wireless sensor networks for personal health monitoring: issues and an implementation. Comput Commun 29, 2521–2533 (2006) 7. C. Chen, A. Knol, H.E. Wichman, A. Horsch, A review of three-layer wireless body sensor network systems in healthcare for continuous monitoring. J. Modern Internet Things (MIOT) 2(3), 24–34 (2013) 8. H.A. Mogaibel, M. Othman, S. Subramaniam, N.A.W.A. Hamid, High throughput path establishment for common traffic in wireless mesh networks, ed. By A. Krendzel. Wireless Mesh Networks - Efficient Link Scheduling, Channel Assignment and Network Planning Strategies, (InTech, Rijeka, 2012) 9. N.A. Benjamin, S. Sankaranarayana, Performance of wireless body sensor based mesh network for health application. Int. J. Comput. Informat. Syst. Industr. Manag. Appl. 2, 020–028 (2010) 10. A. Darwish, A.E. Hassanien, Wearable and implantable wireless sensor network solutions for healthcare monitoring. Sensors 11(6), 5561–5595 (2011) 11. A. Zamanian, C. Hardiman, Electromagnetic radiation and human health: a review of sources and effects, summit technical media. High Freq. Electron. 2005, 16–26 (2005) 12. A.  Mahajan, M.  Singh, Human health and electromagnetic radiations. Int. J.  Eng. Innov. Technol. 1(6), 95–97 (2012) 13. B. Hocking, I.R. Gordon, H.L. Grain, G.E. Hatfield, Cancer incidence and mortality and proximity to TV towers. Med. J. Aust. 165(11), 601–605 (1996) 14. V.G. Khurana, C. Teo, M. Kundi, L. Hardell, M. Carlberg, Sep cell phones and brain tumors. Surg. Neurol. 72(3), 205–214 (2009) 15. F. Ozdemir, A. Kargi, Electromagnetic waves and human health, in Electromagnetic Waves, ed. by Z. Vitaliy (Ed), (InTech, Rijeka, 2011) 16. Y. Lin, H. Lee, M. Who, Y. Harel, S. Mahlke, T. Mudge, C. Chakrabarti, K. Flautner, SODA: a low-power architecture for software radio, in 33rd International Symposium on Computer Architecture (ISCA’06) (2006), pp. 89–101 17. G. Mulligan, The 6LoWPAN architecture, in Proceedings of the 4th workshop on Embedded networked sensors (EmNets ’07) (ACM, 2007), pp. 78–82 18. I. Awolusia, E. Marks, M. Hallowell, Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Autom. Constr. 85, 96–106 (2018)

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19. V. Gupta, D. Mohapatra, S.P. Park, A. Raghunathan, K. Roy, IMPACT: IMPrecise adders for low-power approximate computing, in IEEE/ACM International Symposium on Low Power Electronics and Design (2011), pp. 409–414 20. ANSES, Radiofrequency interference with medical devices. A technical information statement. IEEE Eng. Med. Biol. Mag. 17(3), 111–114 (1998) 21. T. Gee, Can-we-fix-wireless-in-health-care, in Medical Connectivity (2009), https://medicalconnectivity.com/2009/03/24/can-we-fix-wireless-in-health-care 22. Y.Q. He, S.W. Leung, Y.L. Diao, W.N. Sun, Y.M. Siu, P. Sinha, K.H. Chan, Impacts of radio frequency interference on human brain waves and neuro-psychological changes, in International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa (2015), pp. 257–261 23. M.R. Yuce, Implementation of wireless body area networks for healthcare systems. Sensors. Actuators A Phys. 162(1), 116–129 (2010) 24. K. Jeevan, T.R. Haftu, Y.S. Soo, Fog computing-based smart health monitoring system deploying LoRa wireless communication. IETE Tech. Rev., 1–14 (2018) 25. A.V. Mbakop, A. Lambebo, L. Jayatilleke, S. Haghani, Implementation of a wireless body area network for healthcare monitoring, in Conference Proceedings (2013) 26. Y. Zhu, G.Y. Wei, Cloud no longer a silver bullet, edge to the rescue. arXiv:1802.05943 2018, arXiv:1802.05943v1 (2018) 27. M. Marjanovic, A. Antonic, I.P. Zarko, Edge computing architecture for mobile crowdsensing. IEEE Access. 6, 10662–10674 (2018) 28. X. Chen, L. Jiao, W. Li, X. Fu, Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Networking 24(5), 2795–2808 (2016) 29. S. Ullah, H. Higgins, B. Braem, B. Latre, C. Blondia, I. Moerman, S. Saleem, Z. Rahman, K.S.  Kwak, A comprehensive survey of wireless body area networks: on PHY, MAC, and Network layers solutions. J. Med. Syst. 36(3), 1065–1094 (2012) 30. D.P. Tobon, T.H. Falk, M. Maier, Context awareness in WBANs: a survey on medical and non-­ medical applications. Wireless Commun. IEEE 20(4), 30–37 (2013) 31. A.  Boulemtafes, N.  Badache, Design of wearable health monitoring systems: an overview of techniques and technologies, in mHealth Ecosystems and Social Networks in Healthcare. Annals of Information Systems, ed. by A.  Lazakidou, S.  Zimeras, D.  Iliopoulou, D.  D. Koutsouris (Eds), vol. 20, (Springer, New York, NY, 2016) 32. P. Khan, M.A. Hussain, K.S. Kwak, Medical applications of wireless body area networks. Int. J. Digital Content Technol. Appl. 3(3), 185–193 (2009) 33. G.  Acampora, D.J.  Cook, P.  Rashidi, A.V.  Vasilakos, A survey on ambient intelligence in healthcare. Proc. IEEE 101(12), 2470–2494 (2013)

Raluca Maria Aileni  is scientific researcher third degree in Computer Science and has obtained in 2012 the PhD degree in Industrial Engineering at Technical University “Gheorghe Asachi” of Iasi. She is a PhD student at Faculty of Electronics, Telecommunication and Information Technology, Politehnica University of Bucharest. She graduated from the Faculty of Textile Leather and Industrial Engineering Management and the Faculty of Computer Science. In 2010 during her PhD, she obtained a research fellowship for doctoral studies at ENSAIT—Lille University of Science and Technology, France, where she specialized in 3D modeling and simulation for textiles, using the Kawabata system, 2D-3D Design Concept for the design and simulation of technical textile articles. In 2015, she obtained the Excellence Fellowship Grant for doctoral studies in Belgium, Mons University.

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George Suciu  is a senior researcher third degree, with more than 15 years of experience in R&D projects. He graduated from the Faculty of Electronics, Telecommunications and Information Technology at the University POLITEHNICA of Bucharest, where he received also his M.Sc. He holds a PhD in cloud communications from the same university. Also, he holds an MBA in Informatics Project Management and IPR from the Faculty of Cybernetics, Statistics and Economic Informatics of the Academy of Economic Studies Bucharest, and currently, his post-doc research work is focused on the field of cloud communications, blockchain, big data, and IoT/M2M. George has experience as coordinator and WP leader for over 30 R&D projects (FP7, H2020, Eureka / Eurostars, etc.) and is involved currently in over 10 international and 5 national projects. He is the author or coauthor of over 150 journal articles and scientific papers presented at various international conferences, holding over 5 patents. He is R&D and Innovation Manager at BEIA Consult International since 2008, having previously worked as ICT Solutions Manager since 1998. Cristina Mihaela Balaceanu  is a researcher with over 10 years’ experience in the fields of climate change, environmental impact assessment, data analysis, and air quality modeling. She is an engineering graduate, and she graduated from the Faculty of Physics, University of Bucharest, specializing in Physics and Environmental Protection. She is an engineer with a Master of Science degree in Meteorology and Earth Science from the University of Bucharest, Faculty of Physics, Romania and earned a Ph.D. in air quality modeling and telemetry from the Polytechnics University of Bucharest, Romania. She is the author of 1 book, has published over 14 articles in ISI and BDI journals, and 20 proceedings and conference volumes. Also, she was the project manager and team member of 4 national grants and 1 international project within the last 5 years. Besides the research activities, she won the BENA fellowship in 2006 and 2007 and the prize of Romanian Physics Society “Ion Agarbiceanu.” Cristian Beceanu  is a junior researcher in the field of energy and electronics on the national and European projects, who graduated from the Faculty of Power Engineering, specializing in the Engineering of Power Systems in the license and master’s degree studies. He has contributed to two European projects, 3dSafeguard and Aladin, published many articles on various technical topics that have been accepted at different conferences, and has contributed sections of a chapter to be published in a book.

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R. M. Aileni et al. Petrache Ana Lavinia  is a licensed engineer, with 4 years of experience in networking and one year experience in cryptography. She graduated from the college of Automatics, Electronics and Engineering at the Hyperion University in 2011 in Applied Electronics specialization. Her License project is Generator of pseudo-aleatory signal using HDB-3 code. Other projects include FSN in which more binary numbers are formalized (or simplified) and then introduced in a circuit with logic gates and the Fire Detector project that has a fire sensor and a Schmidt trigger. Her professional experience includes mobile communications and network design. Her research activity includes security in the IoT environment and attack graphs. She wrote an article for Agir and she participated in one for Atom-N magazine. Also, she is interested in digital electronics. Carmen-Violeta Nadrag  is a junior researcher at Beia Consult International. During the last year she worked on projects related to Smart Health—ESTABLISH, [email protected] and platform development—SoMeDi, TORCH. She has worked in acquisition and processing of data from environmental sensors—TelMONAER, CarbaDetect. She is the coauthor of 2 papers in proceedings of international scientific conferences (one published and one accepted).

Sever Pasca   is Director of Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunication and Information Technology, Politehnica University of Bucharest. He is Doctor of Engineering in Electronic and Telecommunications, Medical Informatics. Prof. Dr. Eng. Sever Pasca designed and built 56 systems, programs, devices, and appliances for various contracts, within the framework of research projects in collaboration or for self-endowment. He is the main designer of the only Chemiluminometer made in the Eastern Bloc (The Eastern Bloc was the former communist state of Central and Eastern Europe). He has an important contribution in designing, building, and homologation of the prototype and of the fabrication of the complex Stimulator for anesthesia through electro-acupuncture, a device with two brevets.

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Carlos Alberto Valderrama Sakuyama  obtained a PhD degree in Microelectronics at the INPG/TIMA lab in Grenoble, France, in 1998 as member of the Brazilian government R&D program. In 1989, he graduated as an electric-electronics engineer from the UNC, in Cordoba, Argentina. Since September 2004, he is leading the Electronics and Microelectronics Department of the Polytechnic Faculty of Mons FPMs, Mons, Belgium. Between 1999 and 2004, he was leading the CoWare NV. Hardware Flow team located in Belgium. He was also an invited professor in two Brazilian universities; in 2004 at the Federal University of Pernambuco UFPE and in 1998 at the Federal University of Rio Grande do Norte UFRN. Alexandru Vulpe  (M’12) received a PhD degree in Electronics, Telecommunications and Information Technology from the University POLITEHNICA of Bucharest, Romania in 2014, and is currently a lecturer with the same institution. His research interests include, among others, Wireless Sensor Networks, eHealth, Mobile Communications, Security, Qualityof-Service, Radio Resource Management, and Mobile Applications. His publications include more than 50 papers published in journals or presented at international conferences. He participated as a manager or researcher in a number of national or international projects in the area of eHealth, Security and Internet of Things such as “Platform for Studying Security in Internet-of-Things (PaSS-IoT)”, UPB Excellence Grants project (2016–2017), “eWALL—eWall for Active Long Living” (FP7 project, 2013–2016), “Optimization and Rational Use of Wireless Communication Bands (ORCA)”, NATO Science for Peace project (2013–2015), “Terms of Reference— Integrated Software Platform for Mobile Malware Analysis (ToR-SIM)”, UEFISCDI Solutions project (2017–2020).

Uncured Disease Rectification Using Net Collaborating Systems M. Ramalatha, M. Alamelu, and S. Kanagaraj

1  Introduction Today, healthcare is one of the most important processes that define the economic and social growth of a country. All developed and developing countries are striving to increase the usage of technology in bringing excellent healthcare to their population. While changes in healthcare processes can vouch for longevity and reduced mortality rate in urban areas, it is also true that an equivalent healthcare in terms of quantity and quality is not available to rural populations. According to the surveys conducted by various NGOs and WHO, there has been a steep rise in health spending by all countries. But this has not resulted in equal contribution to both rural and urban areas simply due to the lack of healthcare providers in rural areas. It is impossible for healthcare providers to travel to rural areas often to deliver necessary aid, which brings us to the important question: “Were all people created equal?” One solution to bringing healthcare providers and rural patients together is to utilize the growth of technology. If untrained healthcare workers in rural areas could get trained or get consultation with experts available in urban hospitals, it would lead to better utilization of the available resources. In addition, if governments can be informed about an imminent outbreak of disease, proactive avoidance or treatment would become possible, thus reducing the spreading of the disease. M. Ramalatha (*) Department of Electronics and Communication, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India e-mail: [email protected] M. Alamelu · S. Kanagaraj Department of Information Technology, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2019 R. Maheswar et al. (eds.), Body Area Network Challenges and Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-00865-9_2

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Though usage of information communication technologies (ICT) has become common in treatment of diseases, it is still in early stages in the domain of monitoring and prediction. Cloud computing and mobile application are two technologies that can shift the existent paradigms related to healthcare access. From the patient’s side, though the increase in the number of healthcare providers and infrastructure should have had an effect in reducing the difficulties faced by patients in meeting doctors physically, it is often evident that this is not the case from long queues of people waiting their turn. One reason is lack of human resources and another more important reason is that as hospitals become larger, lack of proper organization of everyday routines lead to failures in facing emergency situations. With increased pressure in everyday life, lack of healthy food, lifestyle choices, and stress, it is inevitable that a good percentage of a population develops chronic ailments like osteoporosis, heart failure, stroke, asthma, and cancer. At the same time, owing to pollution, accidents, and lack of awareness, a good percentage of a population is affected by acute diseases or conditions like viral infections and broken bones. Considering the lack of human resources and increase in the requirement for healthcare providers, it is possible for the ICT solutions to create an interim solution. In this context, a Net collaborating system is proposed to establish a query-based disease rectification solution system for needy patients. With this system, patients can place their queries about any healthcare operation like fixing up an appointment with a doctor, checking the availability of experts, or even asking for common medications. The queries are collected and classified and executed to provide appropriate query results to patients or doctors, or both. The execution is based on the criticality and the urgency level of the query and based on this, the requirement for expert opinion is decided.

2  Literature Review Use of telecommunication technologies in healthcare has been prevalent from nineteenth century, and in 1910, the idea of tele stethoscope was conceived, according to Bashshur et al. [1]. It is also a known fact that astronauts’ health has been monitored by base stations from mid-twentieth century. Healthcare industry in the past few decades has gone into a paradigm shift by introducing automated systems, wearable sensors, and long period monitoring systems, leading to greater efficiency and improved deliverability and thus giving birth to telemedicine and telehealth concepts. Telemedicine is a distant healthcare delivery concept which has gained very high popularity in rural healthcare of the world. Telehealth is another concept which is defined as the use of technology to deliver remote clinical care. Healthcare has become the most important industry which affects the economic growth of any country in the recent decades, be it a developed country or a developing country. A report to the Congress in 2017 highlighted the current efforts, needs, challenges, and outcomes in administering telehealth and telemedicine services in

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the USA. This report states that more than 61% of the hospitals across the country are known for current usage of telehealthbringing in a growth of 60% in revenue from 2012. The major reason for this is that telehealth is particularly applicable for chronic disease management [2]. The scenario is not much different in developing countries like India where the rural population is more than 65% of the total population, leading to a severe shortage of healthcare infrastructure and human resources available nearby. Pankaj Mathur et  al. talk about how telemedicine has evolved from the starting of this century with the support of the government using various initiatives involving ICT and health experts. North Eastern Space Applications Centre (NESAC) was of Indian government started commissioning 72 telemedicine regional nodal centers in all districts of North East, successfully connecting district level hospitals to other specialty tertiary care hospitals and setting up village resource centers, thus bringing healthcare and technology to the rural masses. Recognizing the importance of telemedicine, the Indian government has set up a national rural telemedicine network connecting peripheral healthcare centers in rural areas with district tertiary care centers and academic teaching hospitals [3]. Another report to Congress by US Department of Health and Human services lists the basic modalities or methods of telehealth as follows: (a) Remote patient monitoring using sensor-based networks which collects data on vital statistics from patients elsewhere and transmits to healthcare specialists. This also serves as a major source for medical data collection. (b) Mobile health which is a app-based healthcare method providing a variety of services ranging from text messages to encourage healthy behaviors to alerts about disease outbreaks. These can also be used as sensors to capture data as mentioned in the previous paragraph. (c) Synchronous audio–video communication between the specialist and the patient is the simplest concept used effectively with personal networks. Today’s professional network providers are also providing reliable platforms for daily-­ based one-to-one connections between the patient and the specialist. (d) Store and Forward is an asynchronous transmission of captured images of patient data which is often shared to specialists for a second opinion. The report talks about some of the challenges faced by telehealth programs like high-speed Internet for rural areas, insurance payment coverage, and licensing boundaries of medical experts[4]. K.  Ganapathy foretold that the development of e-health programs in India in 1999 is a necessity based on the statistics that in India 80% of medical specialists are catering to only 20% of the people living in urban areas, leaving out the vast 80% of the people living in rural areas. But with the penetration of mobile phones in every home, be it rural or urban, it is possible for India to emerge as an example of a country providing quality anytime, anywhere access to medical care. He explains about the evolution from simple video conferencing to virtual visits to the ICU to mHealth [5].

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According to Bernard K. Lamsu et al., in countries like Africa, where the rural population is dependent on traditional medicines and primary healthcare providers are knowledgeable about the traditional medicines, telemedicine can enhance the healthcare experience by providing modern medicine or traditional medicine according to the patient’s belief and preference. They believe mobile health apps can enhance this type integrative medicine in any developing country [6]. Africa seems to have received the most attention from researchers in implementing telemedicine and telehealth initiatives. Maurice Marshas argued that Africa needs telemedicine and telecardiology due to the extreme shortage of health professionals and the greater burden of the disease. He suggests the usage of mobile phones to overcome the legal and ethical issues clouding the telemedicine implementation programs [7]. Cloud computing and mobile applications are the two technologies preferred for telehealth and telemedicine owing to the pervasive nature of mobile phones and growing cloud popularity even in remote places. There are three aspects to be considered when analyzing the efficacy of cloud computing-based healthcare. One is the diagnosis which involves transmitting the data from remote and either physical or virtual instruments to measure the health parameters of the patient to the physician through Internet and getting the expert opinion on the diagnosis of the condition or the disease. The second one is the expertise linking where remote experts can interact with local healthcare providers to discuss the condition or the treatment of the patient which will require transmitting of data through charts, tables, and the like. The third one is training people and healthcare providers on steps to be taken to prevent or eradicate the outbreak of new diseases which might be seasonal or a result of a major disaster. While the first one is patient centric and a continuous process, the other two are need based. Earlier, expert system-based diagnostics have been developed continually for diagnosing diseases from 1970 onwards. De Dombal et al. proposed a computer-­ based expert system for diagnosing abdominal pain [8], while Seto et al. proposed a mobile-based expert system for detecting heart failures [9]. Slowly these expert systems were combined with sensors and automation systems, while the software control of the system transformed the rule-based diagnosis into machine learning techniques-based one, making the system an intelligent one. Kevin C. Tseng et al. report the use of machine learning techniques to improve the quality of service of the cloud and expert system-based diagnosis systems, by introducing an elastic algorithm based on Poisson distribution to allocate computation resources dynamically to ensure the quality of service [10]. Ryhan Ehad puts forth excellent infrastructure of communication networks, power, and security, which are considered as challenges for developing countries, as the requirements of a telemedicine network for maintaining reliability, authenticity, and security. The opportunities offered are better and more accurate healthcare for the rural population, and less necessity for the travel of patients and experts, leading to better management of available resources and cost-effective healthcare. The improved communication network facilities can open up wide the inter-country

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collaborative research and networking, thus providing valuable data for the researchers [11]. With cloud solutions, sharing and managing a large amount of data has become possible, thus making it possible for intelligent systems to evolve. This has created research opportunities not only in the biomedical stream but also in the computational stream. Meeting the challenge of creating an intelligent system head on, many researchers are working on improving the machine learning techniques to assist healthcare providers in sifting through the voluminous data and making decisions. Data mining techniques to sort the data, optimization techniques to improve the efficiency of the system, and machine learning techniques to make data-based predictions are some of the areas. William Morrow emphasizes on the importance of the use of cloud computing for making informed decisions using the voluminous data hospitals generate every day [12]. Milan Vukicevic et  al. have identified opportunities for improving healthcare processes by training a cloud-based network to select and rank the best predictive algorithms for a given data at any point of time. The performance of any learning system is limited by the volume of data and the availability of large number of algorithms, making it difficult to select the best algorithm. They have addressed this issue by designing an extended meta-learning system as a data and model driven knowledge service using component-based data mining algorithms. The system divides algorithms with similar structure into subproblems, creating reusable components which can then be used to combine with other algorithms and thus creating a hybrid algorithm[13]. Most of the challenges foreseen in data management is data security and security of the users. While the challenges for middleware of user end systems like sensors and connected modules are power saving and size, on the software side it is the compatibility of standards used for storage, management, and communication of information. Zhanlin Ji et al. have designed a ubiquitous mobile healthcare system and define the ISO/IEEE 11073 personal health data (PHD) standards (X73) along with a distributed “big data” processing subsystem for a cloud-based environment. The objectives of the system is to provide high-level performance in data storage, data cleaning, and data management mechanisms so as to increase the efficiency in disease prevention, and monitoring of patients, thus leading to timely remedy [14]. Yulianti et  al. [15] propose an optimization-based document summarization method to extract passage level answers for non-factoid queries. Three optimization-­ based methods called query biased, Community Question Answering biased and expanded query biased are used for retrieval of query from a collected customer database and match features. The results show that using CQA as the first level and continuing to extract features using answer biased summary optimization provides a very good result. The quality of a result depends on the quality of the CQA content, and the optimization techniques provide a comparable accuracy to other techniques available. The system was extensively tested on various data sets with various qualities of CQA[15].

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Qin et al. [16] analyze the Inequality Query (IQ) in tuple independent probabilistic databases. Using the categorization of IQ path, IQ tree, and IQ graph, two issues of IQ queries are identified and discussed. They propose a dynamic programming algorithm called “Dec” to perform sensitivity analysis of IQ queries. The algorithm is used to analyze sensible data with Inequality Query condition with a decomposition and compilation tree. The dynamic programming algorithm uses a two-pass process to compute the probabilities and influences for IQ queries with Shannon expansions. The time complexity reduction is an important goal of this algorithm[16]. Wu et al. [17] proposed new secured data structures MIR (Merkle IR) Tree and MIR* Tree to serve a spatial keyword query with a continuously moving query location. The spatial web objects like restaurants, tourist attractions, and hotels are used to detect the query location and match it to nearby web objects. Instead of delegating this work to a service provider it is proposed to authenticate results from the client side itself using algorithms, thus reducing client server communication cost. With the proposed data structures, the Moving top k spatial key word (MkSk) query can easily search a specific location for spatial data collection. MkSk query can be quickly viewed and extracted from the proposed MIR and MIR* Trees. MIR tree structure was developed with IR tree; it defines the spatial distance and spatial digest values to be stored in non-leaf nodes. Safe Zone construction and authentication is done by constructing a VO (Verification Object) with client mobile users. With the two types of data structure techniques client users can inhibit a spatial data query from any sort of location [17]. Dakka et al. [18] proposes a framework to identify important time intervals which may be of relevance to a query and rank responses on the basis of time sensitiveness in addition to the existing methods. For example, if the user wants to retrieve any sort of information pertaining to a news item, the time-sensitive queries can track the data with respect to the present and history of the item. The paper describes the relevance of time and the method of estimation of time as well as matching of documents. The time-sensitive query analysis is done with the estimation of word tracking, language models, BM25 method, and pseudo relevance feedback. The word tracking uses perday searching technique, in which the collected words are stored in the dictionary and viewed in the form of an index. The index will display how many times a word appears per day. This is integrated with temporal relevance search to answer the “regency” queries by ranking the documents according to dates and more recent documents are ranked higher. Next the Language model is used for searching where the topic is matched with the predicted model. The paper also describes about integrating the temporal relevance with probabilistic relevance model and pseudo relevance feedback techniques and proves that the integration of time relevance improves query search in terms of quality of retrieval [18]. The above works describe various methods of query search and retrieval of data with improvements on accuracy, relevance, and quality. Based on this it is clear that many factors like accuracy of result, sensitivity, and context have to be considered while dealing with query search and query servicing.

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3  Query Classification for Net Collaborating Systems Some chronic ailments like asthma, diabetes, AIDS, and cancer require long-term monitoring and treatment, and they can affect any age group. It is found that asthma affects even children and since it is a hereditary affliction, it may require long-term treatment. Similarly, diabetes has different types and stages and thus the urgency and the level of treatment and expertise required will also vary. If it is gestational diabetes, the treatment will be specialized and temporary since the diabetes may disappear after delivery. For diseases like AIDS which is caused by human immunodeficiency virus, continuous monitoring is required. Though a recent cure has been found, not many people are aware of it. The huge number of diseases affecting people and the diversity in terms of causes, symptoms, area of affliction, and age group of the patient make a query diversified and the analysis of the query also difficult. Since there is a wide data range as regards healthcare, the total query-based analysis is designed at two levels: (a) Broad categorization of the query into emergency and non-emergency based on the type of ailment. (b) Decisive categorization as emergency and non-emergency based on the age of the person. This can be used to make the system learn about the various types of enquiries such as seasonal enquiries and regular enquiries along with more detailed ones for specialist appointments. The proposed system is designed to decide the course of action required for the customer based on these queries and intimate the customer about it. The proposed NCS is designed for three types of actions. • Data collection from registered patients and implanted registered devices and actions based on them from time to time. • Extracting data from queries received through social networking and assimilating similar types of diseases and cures and storing them after comparison for the action given below. • Collecting data of expert healthcare providers through registration and mapping new patients for appointments. The inputs from the devices may be routine data collection for monitoring or any emergency data that requires immediate action. The urgency can be understood by categorization and specific requirements of caretakers. A questionnaire will be standardized to collect personal data of new patients registering into the system along with the inputs about the disease so that the database can be updated for the second action. Emergency cases may be due to accidents, a pregnant woman with labor pain, or due to unforeseen conditions realized in already monitoring patients. In Fig. 1 the third category is shown as others. These are to be taken care of immediately, and these are classified in category C1. Routine checkups, prescheduled visits, and other

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Fig. 1  Broad categorization of queries

visits due to common and seasonal diseases like cold constitute the non-emergency category C2. This is shown in Fig. 1. The following section shows the query tracking mechanism for emergency and non-emergency queries.

4  Query Analysis Based on Broad Categorization This is the tracking system for two categories, emergency and non-emergency. They are indicated by class 1 and class 2 tracking systems. Based on the tracking of queries as per emergency and non-emergency categorization, the data and the query will be put forward to expert doctors or administration for further action.

4.1  Class 1 Tracker Class 1 tracker queries define sudden emergency condition queries like accidents or labor pain or emergency for a monitored patient. The collected queries will be put under the immediate action required category and forwarded to relevant medical experts. Examples of query tracking are given below: In these cases, the patient requests the expert doctor’s appointment and presence immediately. The query can be framed as Accident + Time duration + Complaint Baby birth + Time duration + Complaint

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The third category for Class 1 is the “emergency for patients under treatment” and the query for the same can be framed as Emergency for registered patient + Time duration + Complaint

4.2  Class 2 Tracker Tracker 2 identifies non-emergency cases, such as: 1. Long-term monitoring. 2. Seasonal diseases. 3. Chronic ailments. Long-term monitoring of patients will require for routine checkups and patients who are suffering from chronic ailments will be required to have prescheduled visits with time gaps. For example, a cancer patient undergoing chemotherapy will have prescheduled visits to the therapy unit and may be called to the expert for routine checkups. Patients with pacemakers may need continuous monitoring with or without the presence of a caretaker. The patients may be admitted in the hospital and some may be prefer home treatment. All these different categories are classified as “Non-Emergency,” and each will have to be dealt with based on the requirement. In some cases, if non-emergency diseases develop an emergency condition, it may need to be moved to the class 1 tracker analysis. The format of class 2 tracker analysis is: Disease + Duration of treatment + Complaint + Scheduled/Non-scheduled Another categorization done under both emergency and non-emergency is categorization based on age. Based on the age of the patient, usually the urgency of a situation, treatments, and the medication to be administered vary. Sometimes, because of the age, a non-emergency situation may turn into emergency too. In addition, when small children are afflicted with diseases or need emergency care, the query needs fast resolving. Thus, the proposed system will categorize the input data into two types with the threshold age of 45. The major classification categorizes data into above and below 45, and under 45 data again is subject to a second level classification for ages below and above 3. This general categorization will lead to quick decision-making for identifying incurable disease identification queries with respect to age. In Fig. 1 this is clearly represented in a Net collaborating system (NCS) system. The classification is explained in more detail in Sect. 8.

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5  Working of a Net Collaborating System The NCS in Fig. 2 shows the internal working components with the number of sub-­ modules. The working scenario is explained with a layer-by-layer process. In the proposed system the initial query is collected from patients. As patients are affected with different diseases, the system will collect queries and retrieve plenty of data. The process for the NCS is as follows: Queries and data are received from various sources. Data are usually collected from monitoring devices and queries are generated by patients, caretakers, and other experts. The main function of the NCS is to provide answers to the queries or distribute the requests to various experts. The query received is checked from the QDB (Query Distributed Database) database and the NCS will analyze the query based on C1 and C2 categorization. The second step is to retrieve the details of the experts corresponding to the complaint and the details of the patients. The functionality of this tracking will lead to identify and map the experts and then the hospitals can proceed on to the next step of fulfilling the requests, like fixing an appointment, distributing the reports, connecting the experts to the patients.

Fig. 2  Net collaborating system

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6  Response of the Query Tracker For the emergency track, the response of the query tracker will be just to connect to the emergency numbers and convey the messages to both the patients and the experts. But for the non-emergency track, the system will start collecting the data of the patients and the experts who attended to them and the reports will be used for future. The functionality of the retrieval query is based on the collection of questions. It will collect all types of questions and categorize them based on severity. The types of processing level for the queries are categorized as follows: (a) Normal observation (b) Medium observation (c) High observation For these three categories, the processing steps of the tracker are given below.

6.1  Normal Observation In this observation the queries are for mild or minimum severity category. Thus, these do not require an emergency measure and hence the tracker and the system will treat the queries as non-urgent and low priority (e.g., if a patient is affected with viral fever and would like to consult with a general physician or equivalent). No special treatment may be required and the duration of the affliction is also low. Seasonal ailments, small accidents, and routine checkups will fall into this category. Examples: 1. “I am affected with cold and viral fever and need to see a doctor. Processing time: one or two days” 2. “Requesting the expert doctors in the field of cardiology, gynecology for general checkup for my health” Such types of queries are collected and put into the package of normal observation and the system will respond according to the priority level.

6.2  Medium Observation In medium observation the queries are classified in medium severity category. Thus, these do not require an emergency measure, but the problem has been a long-term one and hence requires a remedy as fast as possible. The tracker and the system will treat these queries as non-emergency but of medium priority (e.g., if a patient is complaining of intermittent fever for a long period, say for a fortnight, even after treatment, and requires a second opinion). Here the level of urgency may not be

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known till a checkup is done and since already a considerable time has passed, this should be given higher priority than the normal observation query. Viral fevers which may turn fatal, irregularity in the sensor data for a patient admitted in the hospital, and accident patients who are under observation will fall into this category. Even patients who are taking regular medicines can raise this type of query, if they are not able to find them in pharmacies and want to know some equivalent. Examples: 1. “I have been having intermittent fever in the evenings over the past 10 days. I have seen a doctor and taken the prescribed medicines regularly but still the fever persists. I would like to get a second opinion”. 2. “I am having gastric trouble induced stomach pain for a month and have tried to treat the same with home remedies. Now the pain has increased and I would like to have a test for ulcer. Need to have an expert contact.” Such types of queries are collected and put into the package of medium observation and the system will respond according to the priority level.

6.3  Higher Observation A higher observation query is critical and of highest priority that requires immediate attention and solution. These are not emergency situations but may turn into one, if not treated immediately. This type of query will also require an expert opinion and any intervention immediately. The query will be immediately classified as a higher observation one and the duty expert will be connected to the user immediately. This process is quickly approved and immediately replied by expert doctors. For example, 1. “My child has a light fever and has a history of fits. We are new to the area and are traveling. We would like an expert to look at the child immediately since the fever is rising. We are in this latitude and longitude. Kindly suggest a nearby expert or a hospital” 2. “My wife is in a family way; her delivery due date has been given as next month. And our family is planning to have the delivery at our native place Coimbatore. In this case, please refer the expert Gynecology doctors in the city of Coimbatore” For this case, the query is categorized as high observation and put forward for an immediate response from experts.

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7  Query Analysis Based on Decisive Categorization The next phase of analysis is based on age since the level and severity of affliction, symptoms as well as the method of treatment will vary based on the age of the patient. This categorization will improve the system to execute queries in a quick way. The purpose of creating this group is to reduce the overload/deadlock of queries to the (Net Collaborating) NC query tracker. The age group criterion is divided into two categories. (a) Less than 45 years (b) Above 45 years

7.1  Less Than 45 Years Some common diseases in younger people are the following: 1. 2. 3. 4.

Asthma Diabetes Diphtheria Measles

The identified queries are tracked and put forward for query categorization under this and the expert list already available for these diseases are mapped with the requirement.

7.2  Above 45 Years This category will identify the frequently occurring problem for patients above 45 years. Common problems in this category of people are the following: (a) Osteoporosis (b) Heart failure and heart attacks (c) Nervous system problems (d) Cancer Based on the type and duration of the problem priority is fixed as high priority and low priority.

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High Priority High priority will be given for 60–80 age groups. People in this age group will require immediate or permanent solutions for a given query. And due to advanced age, response to medication and treatment is very low, thus complicating the problem. Hence experienced experts will be required while treating ailments of these patients. For example, the first heart attack for an aged person may prove fatal and hence immediate first aid and then continuous treatment may be required. Low Priority When comparatively younger persons in the age group of 45–60 years require an expert, their problems may well be in the early stage and the priority is fixed as low. For example, a 45-year-old person is afflicted with the onset of osteoporosis. The condition should be checked and proper medication must be given. But more than that, a change in lifestyle and food habits may prove useful. This kind of query can be assigned low priority level and accordingly responded by the system.

8  Overall Query Analysis and Response 8.1  Lower Priority This section explains how queries based on age group and priority level can decide on the level of urgency to classify the query as emergency. For example: A 25-year-old woman would like to know the expert medical consultation in gynecology for pregnancy problems, in a specified location. The user enters the query as “I am Helen, 25 years old, I have problems in getting pregnant due to ovary cysts. I have undergone many treatments, but my problem has not been solved. Could you please suggest the expert doctor from this location?” Helen registered at 3.45 pm, Date 12.8.2017

The priority-based tracker will accept the query with the conditions of (Age category + Time of registration with date + Requested location) The tracker will divide the query as per the format: (I am Helen, 25 years old, I have problems in getting pregnant due to ovary cysts. I have undergone many treatments, but my problem has not been solved. Could you please suggest the expert doctor from this location) + (3.45pm, Date 12.8.2017) + (Mumbai) The requested data is accessed from the Query Distributed Database (QDB) and details of expert doctors are retrieved with respect to the location. Based on the age, the priority is fixed as low.

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The time of registration is taken as a criterion to fix the priority when multiple requests come from various locations at the same time. The priority-based tracker will make the queue formation and match the data with the database and forward the same to the expert doctors.

8.2  Higher Priority For higher priority queries the analysis and the response of the query tracker will be different since the major functionality of this tracker is to check the availability of doctors and get appointments in person or plan video calls with patients. The tracker will generate the query information as (Age category  +  Time registration  +  location  +  Doctors appointment/ Interaction + Emergency case) For example: Hi, I am David. Today afternoon, my father (68 years old) had severe back pain. Shall I get an appointment tomorrow (6/12/2017) from Dr. Aravind, Cardiovascular, AR Hospital Gandhipuram, Coimbatore? Registered at 4.30 pm on 5/12/2017

The query for high priority tracking will be as follows: (Hi, I am David. Today afternoon, my father (68 years old) had severe back pain. Shall I get an appointment tomorrow (6/12/2017) from Dr. Aravind, Cardiovascular,AR Hospital Gandhipuram, Coimbatore?) + (4.30PM,5.12.2017) +(Coimbatore)+ (Direct appointment) + (Emergency case) The details of expert doctors will be collected from the QDB to make the admin fix an appointment and a quick reply will be given to patients. In some cases, if doctor details are not available in the area specified, then alternate expert details will be found out and results will be provided to patients. The specialist information from multiple hospitals is collected and stored in the database. The system initially creates awareness to doctors about the purpose of developing the system and trains them to use it. A complete memorandum of understanding may be required from experts and hospitals for the system to be installed and used.

9  Social Networking Tracking Packages One of the important sources for accessing the NCS is Social Networking Tracking Package (SNTP). This aspect of the proposed system connects and provides a platform for expert doctors from different hospitals in different countries to share their expertise for a common cause. A person who is far away from his/her nation also can get consultation with the help of this system.

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The patients can access and get help from the system through the same query tracking system. But here while a physical expert may not be available for them in that country, it is possible for the patients to get help from an expert in another country. But the important condition here is confirmation of the identity of the person querying. For this, they need to get a security approval from the system. Similarly, this helps experts in developing countries to get opinions for their patients for some unresolved problems from those in developed countries. Connecting with hospitals and experts from these countries will help in bringing medication which might be unavailable in their countries and will be helpful in getting advanced medical facilities for patients who can afford it. This will ensure that patients get the right kind of treatment always.

10  Result and Discussion Telehealth connecting systems have been implemented till now for medicine administration and monitoring of patients. There have been some predictions about rural telehealth whereby general help and assistance can be provided through registration on websites or mobile apps. But not all ailments require the attention of doctors or medications. Sometimes it is enough to have a consultation or home remedies which are inexpensive and do not require a physician. In addition, there are some requirements which are time sensitive like an accident or labor which require immediate expert opinion and attention. The query-based system which we describe can differentiate between these and ascertain the need for detailed medical attention or if just expert opinion is enough. It is a holistic system wherein, queries if any, from patients or caretakers are routed through a secure and automatic system and support is provided from the database of experts already available in the system. This decreases the dependency of patients on experts and increases the quality time of experts to be spent on needy patients. The query-based system explained here uses simple algorithms for categorization stage and the complexity can be increased in the subsequent stages wherein patients have to be given explanations. The novelty of the system lies in the categorization of queries with time sensitivity as a factor along with age. Another characteristic is that the system does not require registration to get support. Any new non-registered patients also are catered for.

11  Conclusion The chapter describes an all-purpose healthcare system which can network between various entities and use human and other resources available for the benefit of people. Distance has always been an important reason for many fatalities, and this system can reduce the fatality by making expert help available to needy patients on

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time. The system takes different aspects like age, type of disease, and severity of issue into account and strives to provide solutions to all people.

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M. Ramalatha et al. M. Ramalatha  has been involved in teaching for 30 years and currently working as Professor in ECE in Kumaraguru College of Technology. She has published six technical books and has presented over 30 research papers at international conferences and in journals in areas of Vedic mathematics, embedded system design, data sciences, and VLSI. Her interest in building assistive systems for healthcare and special needs people has culminated in developing unique solutions for dyslexia, speech impaired, drop foot, and autism. This earned her invitations from Google and various universities all over the world to deliver special lectures. She has won many international awards from organizations like IEEE, USA, Lions Club, and Anita Borg Institute for Women and Technology, California for her work towards the community. M. Alamelu  is working as Assistant Professor (Senior Grade) in the department of Information Technology, Kumaraguru College of Technology, Coimbatore. She has 13 years of experience in teaching and research. Her research areas include service oriented architecture, Web services, artificial intelligence, and robotics. She has published several articles in the national/international journals and has presented several papers at various conferences. She was the co-PI of the project “Smart Agriculture for sustainable food production” sponsored by IEEE foundation. She has received VIWA foundation International award “Outstanding Women award in Engineering—Information Technology” under the category of Engineering and Dr. A.P.J. Abdul Kalam award for “Teaching Excellence.”

S.  Kanagaraj  has a B.E. in Computer Science from PGP College of Engineering and Technology, Namakkal and an M.E. in Computer Science from Kumaraguru College of Technology. He worked for an IT and ITES organization, Chennai as System Administrator for a period of 3 years. He is passionate to educate and motivate the student community in the field of networking and IoT. He has authored and coauthored five technical papers in various international journals. He also holds the responsibility of IEEE student branch counsellor at Kumaraguru College of Technology and motivating students to take part in various IEEE activities.

Security and Privacy Issues in Remote Healthcare Systems Using Wireless Body Area Networks R. Nidhya and S. Karthik

1  Introduction The wireless body area network (WBAN) is a subset of WBSN that consists of a set of sensors placed on or implanted on a person with a base station. The huge availability of pervasive smart wearable medical devices such as smart medical sensors and the usage of medical management software brought the new paradigm of healthcare data collection to the forefront. During medical process heterogeneous data are continuously sensed from human body using sensor devices. The data sensed by sensor devices are confidential and also sensitive in nature. These data should be accessed by only authorized users such as doctors or nurses for treatment decision-making. To protect individual privacy, already there has been a set of regulations and standards proposed. The first one is HIPAA of 1996 (Health Insurance Portability and Accountability Act) which provides data privacy for personal healthcare data, followed by the European Information Protection Directive 95/46/EC, the Sarbanes-­ Oxley Act, the Gramm–Leach–Bliley Act (GLBA), and the EU’s Safe Harbour Law [1]. These are some of laws which emphasize strict security measures over the sharing and exchanging health data. If providers fail to meet the security measures, then severe penalties are imposed on them. The electronic healthcare systems (EHCRs) rules have been categorized into different systems. One among them is a security critical system [2]. These systems are differentiated with other systems based on their various important aspects. The major R. Nidhya (*) Madanapalle Institute of Technology and Science (Affiliated to Jawaharlal Nehru Technical University, Anantapuram), Angallu, Andhra Pradesh, India S. Karthik SNS College of Technology (Affiliated to Anna University, Chennai), Coimbatore, Tamil Nadu, India © Springer Nature Switzerland AG 2019 R. Maheswar et al. (eds.), Body Area Network Challenges and Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-00865-9_3

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aspect is balancing between confidentiality and availability. Patients’ data should be always available to be shared with the healthcare professionals for providing healthcare services. To provide security, a portion of the data can be considered confidential and must not be accessible. This way, a balance between the goals might be achieved. It provides excellent care for patients. EHCRs are basically patient-centric and real-time systems. These systems provide patient health data to authorized healthcare professionals in digital format. In fact, EHCR is built based on the three tier architecture with certain standards used to collect data from patients. In this architecture, first tier includes a set of intelligent tine-powered sensors with some controller device to gather the important signs from a human body. It can be a heterogeneous network. The second tier includes a device or WIFI or Zigbee or other technology to transmit a data. Third tier includes a remote healthcare server. In EHCRs, a health data owner will be patient or doctors or pharmacist, servers can be a local or cloud servers are used to hold, process, and examine the sensed health data [3, 4]. The connecting bridge between medical professionals and patients to support the transmitting and sharing of data is called Network [4]. Figure 1 illustrates the architecture of an EHCR system. Even though numerous benefits are provided by remote healthcare systems, owing to their portability and design they are susceptible to a broad range of security threats [5]. These threats emerge at each level of the system and it is classified into three levels. They are Data collection level [5–10], Transmission level [11–14], and Storage level [15–19]. Adding on to the abovementioned issues, patients have many security concerns while using healthcare system applications. So it is mandatory to provide a confidential environment to the users so that they will be able to protect the privacy of their data [11]. In this chapter, we present a detailed survey conducted by us to examine the security and privacy threats of healthcare systems. Yu et al [20] explain about the security model that captures the security fundamental for data interoperability of EHCR along with providing capability of access control [20].

2  Security Attacks in Wireless Healthcare System Healthcare systems are subject to vulnerable attacks by different users for profit. This affects the performance efficiency of healthcare systems [4, 21, 22]. Specifically, sensor data, hospital server networks, or the personal health data of individual patients can be hacked by intruders [19, 23].

2.1  Attacks at Data Collection Level At data collection level the attacks cause several threats to collected information such as modifying information, dropping the important information, or resending data messages.

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Fig. 1  Architecture of EHCR System

Jamming attack. The frequency of a body area network is intercepted by the attacker’s radio signal. Within the range of the attacker’s signals, this attack separates and stops a sensor node. As long as the jamming signal continues the affected node will send or receive any message with other sensor nodes under the control of the intruder [6, 8]. Data collision attack. When two or more nodes try to transmit data simultaneously data collision attack may happen. It also refers to jamming attacks when an opponent tries to generate extra collision by sending repeated messages on the channel [7, 8]. When collision occurs the frame header will be changed. At the receiving

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end the error checking detecting method finds an error and rejects received data. Modification in the data frame header is hazardous to data availability in the BAN [6]. Data flooding attack. The intruder continuously sends plenty of requests to the target node for connection until power resource utilization of the node reach a maximum limit which finally results in causing a flooding attack [9]. Desynchronization attack. The intruder intercepts the information between sensor nodes and duplicates it a number of times using a forged sequence number to multiple receivers of an active connection. This would end in an infinite cycle in a WBAN which will make the sensor nodes to transmit the information again and thus waste their energy [7, 9]. Spoofing attack. The intruders target the routing message to perform several disturbances such as spoofing, altering, or replaying which would confuse the network by creating routing loops [10]. Selective forwarding attack. In this method the intruder can attack the malicious node in a network which will forward only the selected messages in the data flow path and drop the others. If the malicious node is located at the base station then the damage caused by the intruder becomes serious [13]. Sybil attacks. The intruder’s malicious node denotes more than one identity to the same network [7]. It is called Sybil attack. It affects the geographic routing protocols. When the location information is needed to share between two different nodes, their neighbor nodes should route the packets which are geographically addressed efficiently [7, 13]. The detection of a Sybil attacker is not an easy one because of their unpredictable paths and high mobility [4, 21].

2.2  Attacks at Transmission Level At transmission level there are many threats to data transmission due to various attacks. Some of the threats include modifying information, sending extra signals to block the base station, spying, disturbing communication, and producing networking traffic. Eavesdropping of patient’s health data. The monitoring system transmits the patient’s health data record from BANs to the healthcare providers. Intruders can easily develop a system through wireless technology which can spy on the patient’s medical data. So whenever the developer builds a system they must apply some validating authority, which would safeguard the patient’s data from eavesdroppers and guards the privacy of the same [9, 11]. Man in the middle attacks. In this method the intruder can communicate between two specific users like the original users. They can do all actions like reading, modifying, and inserting of data between the users, and communication is entirely controlled by the intruder [6, 12]. Data tampering attack. In this attack, an attacker can replace the encrypted data by authorized nodes in the network [7, 13].

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Scrambling attacks. It is also a type of jamming attack. During transmission of control or management information for short intervals of time the radiofrequency will affect the normal operation of the network. It disturbs the communication from patient’s smartphone to prevent the data forwarding which would cause availability issue in the network [6]. Signaling attacks. Initially before the patient’s device begins the data transmission, some initial signaling operation which includes key management, authentication, connection establishment, and registration has to take place. The intruder may begin signal attacking on the base station. By adding extra signal to base station, the intruder would block the base station. This action provides a tremendous amount of load to base station which will result in a DoS attack. At the end the patient’s device cannot forward patient health data due to base station unavailability [6]. Unfairness in allocation. By disturbing the MAC (Medium Access Control) priority schemes the attackers reduce the network performance [13, 14]. Message modification attack. In this attack, the intruder can extract patients’ medical information by capturing wireless channels and the information can be interfered later, which would result in dissemination of faulty information to the involved users (doctor, nurse, family), thus misleading the system [9]. Hello flood attack. In this attack, the intruder has to convince all the nodes to select him as routing node for patients’ data. To this end, the intruder forwards a high powered hello message in the transmission network [7, 13]. This attack will compromise the network. Data interception attack. When patient health information is transferred between two health computers which is connected through LAN an intruder may capture the data. It is called data interception attack [6]. Wormhole attack. In this attack, patient data packet copy of one location will be replayed at another location without modification in data. It is a silent and severe type of attack. This attack’s motivation is damage the topology of a network. It can be achieved by creating a tunnel between the two intruders which can be used for transmitting data between them [5, 13].

2.3  Attacks at Storage Level At storage level attacks lead to several issues such as changing the configuration of system, monitoring servers, or altering patients’ medical information. Patient’s data inference. Intruders attempt to merge the authorized information with other relevant information to repossess patients’ data such as health information, diseases, and treatments provided to them [9, 11, 17]. To prevent this kind of attack, before publishing/posting the data patients’ information should be unsigned to protect the identity of data [3]. Patient medical data unauthorized access. Intruders try to access patients’ data without a valid authentication. It results in damaging patient data [18]. So it is

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necessary to guard patient privacy from unauthorized users to avoid stabbing and mistreating of data [11, 16]. Malware attack. In this type of attack a software program is designed to achieve harmful motives [19]. This software makes hospital servers unavailable for further process. By altering system configurations, system malfunctioning and communication interruption would occur [6, 12]. Social engineering attacks. In this type of attack, an authorized user can also disclose confidential information of patients to third parties for fraudulent purposes. A third party attacker is also possible in this kind of attack. The attack compromises the confidentiality of patients and gain access to the system to retrieve their data [6, 12].

3  Security Models for Wireless Healthcare Systems To enhance the quality of healthcare system, patient’s data should be shared across different users, which may result in privacy issues. So it is mandatory to protect e-Health systems through suitable security models to provide authorized access controls [24–27]. A traditional solution is encryption. It is used to provide a simple access control, but it is not suitable for complex EHCR systems because it requires various accesses. Two big reasons for keeping the e-Health data as a secured one are the computational overhead which occurs because of using encryption techniques and the personal medical information sensitivity [28]. In this section, a set of security models with their corresponding levels are described in detail.

3.1  Data Collection Level Security Models Morchon O. G. and Wehrle K. [29] presented a modular access control system for healthcare applications. The system broadened the traditional RBAC model for two major issues: The first one is for allocating and distributing access control policies to the sensor nodes. The second one is to store the current medical information based on access control decisions which depends upon the patient’s medical situation. The modular system makes the configuration of the system more efficient and shortens the policies to position secure medical sensor networks. Still when an emergency situation rises, the medical staff can overrule the restrictions to access sensitive data which remain restricted under normal situations. The major disadvantage of this system is, when critical situations occur, the system cannot detect unauthorized access. Amini S. et  al. [30] inspected a certain group of security protocols such as TinySec, MiniSec along with different algorithms (Skipjack, AES, and RC4) to design a lightweight security model. Finally, the authors merged different kinds of attacks (data loss, spoofing of sensors, and eavesdropping and replay) and suitable

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algorithms for tackling them. They found that the most efficient cipher algorithms are RC4 and Skipjack which will fulfill the confidentiality of RAM, ROM, and clock cycles per byte (CPB). However, the authors did not consider other security threats. Maw H. A. et al. [31] presented an Adaptive Access Control model that provides perfect access control for medical data in BANs and WSNs. The model provides privilege overriding when unexpected events occur so that the users can avoid any denial of access. Once the users initialize their session using location, time, and action of user in a behavior trust model, human effort is not necessary to clear authorizations and policies. The major disadvantage of this model is that the systems do not have any mechanism to detect or prevent unauthorized users’ data access when a critical situation occurs. Linciya [32] and Rasheed [33] explained a three-tier security architecture based on pairwise key pre-distribution schema. The architecture included two key sets: the first one for the mobile sink which is used to access the network, and the second one for pairwise key establishment which is used between the sensors. It is used to enhance the elasticity of the network and reduce the damages caused by replication attacks. The system has enhanced the authentication between the sensor and access node. Still intruders can guess the pre-key distribution based on the key value. Ramli S. N. et al. [34] proposed a biometric-based security architecture for data authentication in WBAN. Signals can be used as key to guarantee that the patients’ data will not be accessed by others because the patient has used his/her own specific biometrics as a key value, which results in computational complexity reduction and increases the efficiency using cryptographic key distribution. The major disadvantage is that sensors themselves will carry out the authentication process. Mu K. and Li L. [35] explained a group-based key distribution scheme for WSNs. This scheme consists of three phases, namely initialization phase, share-key discover phase, and path-key establishment phase. In this scheme all sensor nodes are provided with security levels (high to low), wherein a low level secured node cannot access the data collected for a higher security level sensor. This analysis provides a better flexibility against capture attack. Iehab et al. [36] consolidated the physiological qualities (PVs)-based key administration strategy and pre-stacking strategies by utilizing electrocardiography (EKG/ ECG) estimations of PVs and pre-stacking-based plans to fortify security. The connected procedure upgrades security and additionally decreases storage and power utilization. An approach that uses physiological signs (electrocardiogram (ECG)) to address security issues in WBAN is presented by Mohammed et  al. [37]. This approach deals with the generation and sharing of symmetric cryptographic keys to essential sensors in a WBAN (utilizing ECG flag), thus ensuring protection. A trust key management plot for wireless body area network is displayed. The proposed methods endeavor to tackle the issue of security and protection in WBANs. It additionally means to safely and consistently producing and circulating the session keys between the sensor nodes and the base station to secure end-to-end transmission.

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3.2  Data Transmission Level Security Models Boonyarattaphan et al. [28] proposed a secure structure for authentication and data transmission. Authentication is provided using two different encryption techniques for implementing two mechanisms: Security for Data and Channel. The data security is offered by constructing a SOAP layer above HTTP. The channel security is offered by using an SSL on the HTTP layer. Based on the roles allocated to the stakeholders and data sensitivity, different types of authentication and encryption settings can be chosen by the corresponding layers. It can handle only Web-based e-Health services. Kahani et al. [38] proposed a novel scheme which provided secure authentication and scalable data access control. Here the authors developed a new protocol known as zero-knowledge protocol to confirm and preserve the secrecy of the user’s identity. This scheme combines a system public key and a secret session key produced by Derive Unique Key Per-Transaction (DUKPT) scheme to produce the key which is used to establish secure communication between different interacting entities. Guan Z. et al. [39] proposed the cloud-integrated body sensor networks for data security and privacy. They proposed an efficient encryption scheme named Mask-­ Certificate Attribute-Based Encryption (MC-ABE) by combining seven different encryption algorithms. In this scheme, the patient encrypts the outsourcing data to hide the raw data before storing it in the cloud server. Additionally, unique authentication certificate is provided to each individual user to achieve more efficient access. However, it is difficult to achieve perfect access control because of using encryption technique. Simplicio M. A. et al. [40] proposed Secure Health lightweight security structure based on very lightweight mechanisms. It can provide security to the data exchanged with the server without any extra security layer. For both stored and transmitted data, Secure Health provides security services. Dave et al. [41] first portrayed a general healthcare platform and pinpointed the security difficulties and necessities. Further, they proposed and broke down the CICADA-S protocol, a safe cross-layer protocol for WBANs. It is an augmentation of CICADA, which is a cross-layer protocol that manages both routing of the data and medium access in WBANs. The CICADA-S protocol is the initially coordinated arrangement that adapts to dangers that might happen in this mobile medical monitoring scenario. It is demonstrated that the mix of key administration and secure, privacy-saving communication strategies inside the CICADA-S protocol has a low impact on power consumption and throughput. Iyengar et al. [42] exhibited a novel lightweight protocol for information integrity in wireless sensor systems. The protocol depends on a leapfrog technique in which each bunch head confirms if its past node has safeguarded the respectability of the bundle utilizing the secret key it offers with two hop-up tree nodes. The key focal points of the protocol include the following: the protocol is straightforward, it does not need very many header bits, as low as three bits, and in this manner it

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brings about irrelevant transmission capacity overhead. The protocol postures low computational overhead, and it needs to process only a hash when contrasted with numerous mind-boggling operations required by any cryptographic usage for checking the genuineness of the data. Syed Muhammad et  al. [43] highlighted the contrasts between the WSN and WBAN and proposed a productive key administration plot, which makes use of biometrics, particularly intended for WBAN domain. It builds up the key administration protocols for non-specific utilizations of WSN which are excessively perplexing for WBAN situation and cannot misuse the application attributes of WBAN. From this point forward, it presents BARI+, which is a key administration plot planned particularly for WBAN applications. Additionally, it provides examination of the proposed plan and its correlation with different plans. Aside from assault counteractive action, it is additionally essential to concentrate on assault discovery with a specific end goal to give an entire security arrangement. Shahnaz et  al. [7] first highlighted the real security necessities and Denial of Service (DoS) assaults in WBAN at Physical, Medium Access Control (MAC), Network, and Transport layers. They clarified the IEEE 802.15.4 security structure and distinguished the security vulnerabilities and significant assaults with regards to WBAN.  Distinctive sorts of assaults on the Contention Free Period (CFP) and Contention Access Period (CAP) parts of the super frame are examined and analyzed. It is monitored that a smart intruder can effectively corrupt an expanding number of GTS slots in the CFP period and can significantly influence the Quality of Service (QoS) in WBAN. Ragav et al. [44] presented a security suite for WBANs which included IAMKeys, a free and versatile key administration plot for enhancing the security of WBANs, and KEMESIS, a key management conspire for inter-sensor communication security. The originality of these plans lies in the utilization of an arbitrarily produced key for encoding every information outline which is created autonomously at both the sender’s and the recipient’s ends dispensing with the requirement for any key exchange. Stev et al. [45] presented an outline of WBAN foundation work with Medical Component Design Laboratory at Kansas State University (KSU) and at the University of Alabama in Huntsville (UAH). KSU endeavors to incorporate the advancement of wearable well-being status observing frameworks that use ISO/ IEEE 11073, Health Level 7, Bluetooth, and OpenEMed. WBAN endeavors at UAH to incorporate the improvement of wearable action and well-being screens that combine ZigBee-consistent remote sensor stages with equipment level encryption and the TinyOS advancement condition. Syed et al. [46] made utilization of the key refreshment program, which delineates the turn of every node for key refreshment. Periodically new key refreshment plan is issued by the personal server (PS). Every node revives the key in the space allocated to it. This plan utilizes three types of keys to deal with a WBAN: regulatory key, communication key, and essential key. The authors exhibit BARI, which is a key management conspire outlined particularly for WBAN applications. While

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developing the application attributes of WBAN, BARI gives needed level of security in WBAN which other schemes cannot provide. Mriimioy et al. [47] considered packet scheduling schemes for real-time transmission in WBAN with appropriate security and protection. Real-time and nonreal-time traffic are ordered to limit the holding up time of the eHealth application’s data traffic. A productive secure information transmission conspire in WBAN is proposed with information integrity. The scheme is client driven and the protected key is shared among all sensors in a WBAN to limit any extra memory and preparing power necessities. Safe communication between medical sensors and PDA, and guaranteeing QoS for the real-time traffic have been explored. The proposed secure communication plan can limit the key storage space and require less computation. Patient security is guaranteed by utilizing pseudo personality. A need-based traffic scheduling plan for real-time application in WBAN is proposed and analyzed.

3.3  Data Storage and Access Level Security Models Sun et al. [24] explained the notion of purpose to develop a complete usage access control model. Notation purpose was used to specify the privacy policies to the patient data. The proposed system consists of eight major components. They are authorizations, object attributes, rights, subjects, subject attributes, objects, obligations, and conditions. Of these eight components authorizations, obligations, and conditions are components of usage control. They determine whether the user is allowed to access an object or not. Baruna M. et al. [1] proposed a novel architecture for security scheme depending on different privacy levels. The centralized infrastructures will take care of the access control policies. The attribute-based encryption (ABE) is used by the system. The data will be forwarded to the cloud storage for anywhere, anytime access of e-Health records. Because of using all data in a centralized server there is a chance of occurrence of the bottleneck problem. Guo L. et al. [48] proposed the new distributed nature of e-Health system for solving the aforementioned problem. In their system, authentication process is done by the two end users (patients and physicians) instead of doing it by centralized infrastructures. In particular, individual users have rights to authenticate each other without revealing their identities. Gajanayake R. et al. [49] proposed a novel architecture for satisfying e-Health’s requirements known as privacy oriented access control model. The architecture was designed by merging three different access control models, namely DAC, MAC, and RBAC into a single module which provides setting access privilege rights to patients and healthcare professionals. Barua M. et  al. [50] explained a schema for secure patient-centric personal health information. It is used to provide access control to patient health information based on Proxy Re-encryption Protocol. The system has five major phases. They are patient’s data transmission to the healthcare service provider, access policy

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declaration, storing patient’s data at cloud, validating requester of data-access, and auditing the encrypted data at cloud storage. Kumar M. R. et al. [51] explained a new patient-centric structure based on the ABE encryption technique. In this scheme the users were divided into two main domains. They are public and personal domains to manage the complexity of the key management. In the public domain, to enhance the security, users can use multi-­ authority ABE (MA-ABE). In the personal domain, Self attributes are used by an owner to access/encrypt the data. The disadvantage of this model is that incorporating ABE into a large-scale system provides key management issues. Zhu H. et al. [52] proposed a secure and efficient personal scheme. It is based on the ABE and RSA-based proxy encryption. To ensure patient data validity, the proxy encryption technology is used. The write authorization keys are given to medical professional and the read authorization keys are given to patients, so that the data access authorization is not completely controlled by the patient. As a result, computational complexity is much reduced. Sunagar V. and Biradar C. [53] introduced a secured structure depending on advanced encryption standard (AES) algorithm. Based on the security policy, it encrypts all patients’ data. AES is used to maintain the patient’s data securely in cloud storage. This structure consists of three modules: PHR Owner/patient module, Data confidentiality module, and Cloud Server module. Liu W. et  al. [54] proposed a general structure based on hierarchical identity-­ based encryption (HIBE) schema and the role-based access control (RBAC). Before storing the data in cloud storage HIBE is used to encrypt the data of patients. The RBAC is used to assist forwarding the privileges of individual users. According to HIPAA regulations if a scheme does not have any proper authorization, then it suffers from well-known encryption drawbacks (Katz et al. [55]). Jingwei [56] discussed the security issues of WBANs and proposed achievable cross-breed security systems to meet the security prerequisites of WBANs with strict asset compels. They first presented the current advances of WBANs and broke down the principal security dangers to it, which made it simple for WBANs to experience the ill effects of assaulting compared to the alternate systems without asset compels and the security prerequisite of WBANs. At this point, they talk about the accessible cryptographic algorithms and propose a hybrid security structure for it. The proposed security component gives a primitive component to create proficient and secure WBAN frameworks.

4  Privacy Requirements in Healthcare Systems To provide appropriate level of security and privacy in ECHR system the general security and privacy requirements should be satisfied. More than twenty security requirements are defined by the authors that are listed on various surveys [3, 15–18, 23, 57–61]. The most important requirements are listed below.

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4.1  Access Control Access control is the procedure of preventive and controlling the authorized users’ access to resources [3, 57]. It utilizes three different security and privacy requirements. They are identification, authentication, and authorization. Identification is used to identify the user. Basically it is not a genuine security issue in itself. So it is used to examine the user with its authentication characteristic [58– 60]. Authentication is used to guarantee that the requested data access is valid and authentic [3], and it is also used to accomplish identity claim process before data accessing [57]. It also assures that person at the other side of the communication is an authorized party [58]. In the end, the authorization procedure decides based upon the security policy, so some of the data can be limited to an external requester. It is compulsory to provide a proper access control method which will ensure the privacy of the patient. And it also necessary to provide a perfect balance between availability and confidentiality [15, 59] based on the security goals.

4.2  Availability Availability guarantees that resources will be available anytime, anywhere in the healthcare system [61], and it also assures accessibility and usability of resources of the system based upon the demand of the authorized users [15, 18, 57]. Guaranteeing availability also includes avoiding service disturbances due to hardware failures, power failures, and system advancement [16, 58].

4.3  Dependability Dependability is the property of the system which can easily retrieve data at any time. Sometimes if there are any threats caused because of network failure node, data can be retrieved easily by using this property [18, 23]. Because of using dynamic network in medical cases, it may not be possible to retrieve the accurate medical data which could threaten the patient’s life. The necessary requisite for dependability is fault tolerance.

4.4  Flexibility Flexibility is an ability to provide permission to an unauthorized person who is not in the permissible list to access data to safeguard the patient’s life at times of emergency. In emergencies, prevention of the access rules would become a threat to the patient’s life [18].

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5  Wireless Healthcare Systems’ Reliability Ren et al. [62] proposed and executed a reliable and efficient backup scheme to connect monitoring frameworks. It is mostly utilizing a wireless sensor network (WSN) to accumulate the related environmental parameters and to transmit the numerical information to the gateway through multiple hop relay. After this it additionally stores information in the back-end database for the expert checking staff to analyze and study. In addition, the proposed backup scheme could likewise improve the burden to include or evacuate sensor nodes in a current wired extension monitoring system. The aim is to primarily apply the technology of WSN to extend observation. Jinsuk et al. [63] proposed multi-home mobile terminals-based video multicast protocol as a substitute stream control transmission protocol (SCTP) for moderately reliable multicast communications. In application layer, SCTP is enabled to work with peer-to-peer overlay video multicast facility. For a mobile terminal with multi-­ homed model, the essential way switching strategy is applied when a handover is in process which may lead to error burst. The key issue considered in this protocol is the packet loss prediction and retransmission of the lost packets when a mobile terminal finishes its essential way switching procedure. This property manages the transmissions’ delay sense. Additionally, it decreases the message overhead altogether and gives a versatile communication system to multicast applications. Xiaohui et  al. [64] proposed dispersed Prediction based Secure and Reliable directing system (PSR) for developing wireless body area networks (WBANs). It can be incorporated into a particular routing protocol to avert data injection attack and enhance the quality of reliability during information communication. Julio et  al. [65] investigated the execution of two prominent routing models, Energy-Aware Routing and directed diffusion and proposed the new routing algorithm SIR, which has the originality of being founded on the prologue of neural systems in each sensor node. SIR is QoS-based routing algorithm that depends on artificial intelligence. This routing protocol can be utilized over standard protocols of a wireless sensor network (e.g., Bluetooth and IEEE 802.15.4). Xiuming et al. [66] proposed a protocol for WBAN known as MBStar which is a real-time, reliable, secure, and high-frequency protocol. MBStar uses channel blacklist and channel hopping to minimize noise interference. It additionally supports recognized transmission and retransmission to give reliability to the link. MBStar utilizes both public/private key methods for provisioning devices before joining and utilize AES (Advanced Encryption Standard) for encrypting patient data after joining.

6  Conclusion Patient health information should be maintained very secure in medical servers so that medical professionals can able to provide a proper treatment to the patient. Mistreatment of medical data would constitute a threat to the patient’s life. To confirm security in EHCR storage process and access control management, in this

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chapter, we have discussed so many security issues in healthcare system with its proposed solutions to avoid such attacks. Particularly, threats are grouped into various types based on its nature in the healthcare system. The attacks in healthcare system are classified into three levels. They are data collection level, transmission level, and storage level. The attacks at these levels may lead to various issues like altering data, dropping some information, disturbing communication or sending some additional information to block server response, and increasing networking traffic. In addition to this, various models for handling privacy issues and reliability issues of medical data are discussed in this chapter.

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R.  Nidhya  is presently working as Assistant Professor in the Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, affiliated to Jawaharlal Nehru Technical University, Anantapuram, India. She received her M. Tech. and Ph.D. degrees from Anna University, Chennai. Her research interests include wireless body area network, network security, and data mining. She has published 12 papers in refereed international journals and 13 papers at various conferences. She is an active member of ISTE, IAENG, SAISE, and ISRD.

S. Karthik  is presently Professor and Dean of the Department of Computer Science & Engineering, SNS College of Technology, affiliated to Anna University—Chennai, Tamil Nadu, India. He received his M.E. and Ph.D. degrees from Anna University, Chennai. His research interests include network security, bigdata, cloud computing, Web services, and wireless systems. In particular, he is currently working in a research group developing new Internet security architectures and active defense systems against DDoS attacks. Dr. S. Karthik has published more than 96 papers in refereed international journals and has presented 125 papers at various conferences and has served as Technical Chair and Tutorial Presenter for various international conferences. He is an active member of IEEE, ISTE, IAENG, IACSIT, and Indian Computer Society.

Data Reliability and Quality in Body Area Networks for Diabetes Monitoring Geshwaree Huzooree, Kavi Kumar Khedo, and Noorjehan Joonas

1  Introduction Diabetes Mellitus (DM) is among one of the leading worldwide epidemic metabolic disorder that imposes inadmissibly significant human, social, and economic costs. Despite all the recent advancements in drug therapies, technologies, healthcare education, and preventive intervention strategies, the prevalence of diabetes is still on the rise and its associated health complications have become even more prominent and life-threatening [1]. Globally, 425 million people have been affected by diabetes and around 693 million people are expected to be affected by 2045 if the current trends continue. Moreover, USD 727 billion is being spent yearly by diabetic people in their healthcare treatment and approximately four million people have lost their lives due to diabetes in 2017. The total healthcare expenditure has increased from USD 232 billion in 2007 to USD 727 billion in 2017 and the economic burden is expected to increase to USD 776 billion by 2045 [2]. The high prevalence of diabetes is due to unmanaged and uncontrolled diabetes levels among patients. Despite no cure for diabetes has been found so far, the symptoms can be alleviated, and complications can be reduced to a great extent. In addition, treatment efficiency can be improved significantly through continuous G. Huzooree (*) Department of Information Technology, Curtin Mauritius, Moka, Mauritius e-mail: [email protected] K. K. Khedo Department of Digital Technologies, University of Mauritius, Reduit, Mauritius e-mail: [email protected] N. Joonas Central Health Laboratory, Victoria Hospital, Ministry of Health & Quality of Life, Candos, Mauritius e-mail: [email protected] © Springer Nature Switzerland AG 2019 R. Maheswar et al. (eds.), Body Area Network Challenges and Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-00865-9_4

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glucose monitoring coupled with proper medication, dietary habits, and physical exercise. Ongoing monitoring of blood glucose (BG) level has become imperative in the management and treatment of diabetes. Patients can perform self-monitoring of blood glucose (SMBG) to control their insulin levels, to improve adherence, and to keep BG levels within a normal range. Thus, the mortality and associated healthcare complications of diabetes will be better controlled. Very often, failure to manage diabetes results into lower quality of life, increased economic burden, and social problems [3, 4]. Consequently, societal costs related to hospitals, readmission rates and hospital visits with cases of hypoglycemia/hyperglycemia can be reduced with continuous monitoring. Therefore, there is an increasingly high need for cost-­ effective healthcare services that can be provided to everyone, everywhere, and anytime ubiquitously to support and monitor patients to avoid expensive hospital-­ based care [5]. The evolution of Pervasive Healthcare Systems (PHSs) is a promising potential for unobtrusive remote health monitoring anywhere and anytime. It empowers the patients with the ability to detect their symptoms at an earlier stage and to easily share their medical information with healthcare professionals for further real-time analysis, diagnosis and timely intervention without spatial–temporal restrictions. In case of emergencies, the concerned parties can be alerted through message or email notifications. Consequently, the wide usage and adoption of PHSs can lead to better self-disease management, proactive monitoring of conditions and significant reduction in healthcare economic burden and hospital visits. PHSs include WBAN which is an emerging technology involving the use of wireless communication whereby low-power, intelligent, small-sized, lightweight, and invasive or non-invasive sensors function in the vicinity of the human body to detect the patient’s vital signs. WBAN is gaining major interest with the widespread plethora of available technologies supporting medical and healthcare applications [6, 7]. An increase in prevalence of diabetes is leading to a worldwide paradigm shift from doctor-centric to patient-centric whereby WBANs have a fundamental role in addressing the multifarious challenges in healthcare [8]. The recent advancements and evolution in WBAN have demonstrated the huge potential to improve the quality of life of diabetic patients [9]. Nevertheless, these WBANs require high level of data reliability and quality to be effective and widely adopted. Alarming reasons such as high societal and economic burden are triggering various research works in the field of healthcare especially in continuous monitoring of diabetic patients using WBAN. Furthermore, the benefits of the WBAN are beyond argument as they have the capability to improve the health conditions and lifetime expectation of diabetic patients. This chapter presents an overview of the current BAN for monitoring patients by focusing on the traditional systems and continuous glucose monitoring systems (CGMs). The challenges of CGMs are further described and some leading and latest CGMs on the market are presented. It also discusses recent advances in sensor technologies and some emerging devices for non-invasive glucose monitoring via other human fluids than blood. This chapter also outlines the need for data quality

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and reliability in BAN for diabetes monitoring. The different metrics to measure the dimensions of Quality of Information (QoI) are presented and the research directions in BAN with regard to data quality and reliability are discussed at the sensor level, network level and human-centric level.

2  Body Area Networks for Diabetes Monitoring 2.1  Traditional Systems The gold standard way for diabetic patients to perform SMBG is through a blood glucose meter, an uncomfortable and slow process. A small blood sample (

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