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Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (25 August 2019) | Viewed by 47897

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Special Issue Editors

Dr. Ali Hassan Sodhro

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Guest Editor

1. Decision Information and Production System Laboratory, Institute University Technology, University Lumiere Lyon2, 69500 Lyon, France
2. Electrical Engineering Department, Sukkur IBA University, Airport Road-65200, Sukkur, Sindh, Pakistan
Interests: sustainable healthcare; energy-aware communication; smart IoT; body sensor neworks; fog computing for healthcare
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Dr. Qammer Abbasi

E-Mail Website
Guest Editor

James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Interests: nano communication; biomedical applications of millimeter and terahertz communication; wearable and flexible sensors; compact antenna design; RF design and radio propagation; antenna interaction with human body; implants; body centric wireless communication issues; wireless body sensor networks; non-invasive health care solutions; physical layer security for wearable/implant communication and multiple-input–multiple-output systems
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Dr. Akram Alomainy

E-Mail Website
Guest Editor

School of Electronic Engineering and Computer Science, Faculty of Science and Engineering, Queen Mary University of London, Mile End Road, London E1 4NS, UK
Interests: basics of antennae and electromagnetism, from megastructures and metasurfaces to novel applications in telerobotics, cognitive radio, wearable electronics, nanoscale networks, healthcare, and bioengineering
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Prof. Dr. Muhammad Ali Imran

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James Watt School of Engineering, University of Glasgow, Glasgow, UK
Interests: 5G and Beyond networks
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Dr. Sandeep Pirbhulal

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Guest Editor

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
Interests: biometric security; internet of medical things; wearable sensing technologies; privacy perserving; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is playing the remarkable role in healthcare field by entirely changing the landscape of the whole world. Miniaturized devices are the building blocks and paradigm shift to reshape the healthcare, thus security and sustainability are the pivotal parameters to build the smart IoT enabled medical healthcare. Besides, IoT system is the magic to establish the strong ties between physical and real-worlds by bringing together all the heterogeneous technologies at the single common platform in a prominent, cost-effective, secure, flexible and dynamic manner. Besides, emerging and widely spreading role of IoT on one hand made the lives of everyone comfortable and convenient while on the other hand posed the various challenges i.e., less sustainability, less security and high energy drain, etc which threaten the smart IoT based health related applications.

Henceforth, secure, sustainable and smart IoT platform in the medical healthcare is the dire need and considered ad revolutionary paradigm. In the mean-time healthcare engineering in association with technological development for vitalizing the IoT in healthcare. This also promotes the importance of healthcare among individuals and integrated societies by improving their lives in a secure and sustainable way. This amazing and interesting emerging practice builds voluminous data centers and clouds while collecting information from sensor-based devices. Thus, it is very vital to manage these huge databases in a smart, secure and sustainable way for providing the economical care with longer life to anyone at any time.

Novel research contribution in this special issue will emphasis the remarkable role of smart, sustainable and secure techniques for IoT enabled healthcare domains for facilitating common citizen in various ways. Some of these are presented below but not limited in scope.

Pervasive medical care
Machine Learning based non-invasive, secure and sustainable healthcare solutions
Smart IoT for ubiquitous healthcare,
Secure and sustainable mobile healthcare Technologies
Bio-Nano communication for secure healthcare
RF Antenna Design for Body-centric smart and sustainable networks
State-of-the art Frameworks and Architectures for smart, secure and sustainable IoT
Smart, Secure and Sustainable digital healthcare.
Wearable and Secure IoT devices for Smart medical home monitoring
Applications of smart IoT based secure and sustainable medical world
Machine Learning and Deep Learning Algorithms for Clinical Applications
QoS/QoE management and optimization solutions in Internet of Medical Things
Wearable Devices for Clinical and Health Informatics Applications
Telemedicine/Telecare/Telesurgery based frameworks, and solutions for medical media monitoring.
Biometric Security for Healthcare
Smart Decentralized Framework for Health Monitoring
Smart and Energy-aware wearable Healthcare system
Fog computing/edge computing for IoT based Healthcare Applications

Dr. Ali Hassan Sodhro
Dr. Qammer H. Abbasi
Dr. Akram Alomainy
Prof. Muhammad Ali Imran
Dr. Sandeep Pirbhulal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

medical data security
sustainable network
smart healthcare
wearable/ambient technologies
internet of things
decentralized IoT
ubiquitous medical care

Published Papers (8 papers)

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Research

14 pages, 2608 KiB

Open AccessArticle

TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks

by Mohsin Javed, Ghufran Ahmed, Danish Mahmood, Mohsin Raza, Kamran Ali and Masood Ur-Rehman

Sensors 2019, 19(15), 3275; https://doi.org/10.3390/s19153275 - 25 Jul 2019

Cited by 26 | Viewed by 3940

Abstract

Wireless Body Area Networks (WBANs) are in the spotlight of researchers and engineering industries due to many applications. Remote health monitoring for general as well as military purposes where tiny sensors are attached or implanted inside the skin of the body to sense the required attribute is particularly prominent. To seamlessly accomplish this procedure, there are various challenges, out of which temperature control to reduce thermal effects and optimum power consumption to reduce energy wastage are placed at the highest priority. Regular and consistent operation of a sensor node for a long-time result in a rising of the temperature of respective tissues, where it is attached or implanted. This temperature rise has harmful effects on human tissues, which may lead to the tissue damage. In this paper, a Temperate Aware and Energy Optimized (TAEO) routing protocol is proposed that not only deals with the thermal aspects and hot spot problem, but also extends the stability and lifetime of a network. Analytical simulations are conducted, and the results depict better performance in terms of the network lifetime, throughput, energy preservation, and temperature control with respect to state of the art WBAN protocols. Full article

(This article belongs to the Special Issue Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications)

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18 pages, 4964 KiB

Open AccessArticle

An Intelligent Fire Warning Application Using IoT and an Adaptive Neuro-Fuzzy Inference System

by Barera Sarwar, Imran Sarwar Bajwa, Noreen Jamil, Shabana Ramzan and Nadeem Sarwar

Sensors 2019, 19(14), 3150; https://doi.org/10.3390/s19143150 - 17 Jul 2019

Cited by 60 | Viewed by 8993

Abstract

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the raw data in a linguistic variable which is trained in ANFIS to get the probability of fire occurrence. The proposed idea also generates alerts with a message sent directly to the user’s smartphone. Our system uses small size, cost-effective sensors and ensures that this solution is reproducible. MATLAB-based simulation is used for the experiments and the results show a satisfactory output. Full article

(This article belongs to the Special Issue Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications)

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17 pages, 1822 KiB

Open AccessArticle

An Internet of Things Based Bed-Egress Alerting Paradigm Using Wearable Sensors in Elderly Care Environment

by Muhammad Awais, Mohsin Raza, Kamran Ali, Zulfiqar Ali, Muhammad Irfan, Omer Chughtai, Imran Khan, Sunghwan Kim and Masood Ur Rehman

Sensors 2019, 19(11), 2498; https://doi.org/10.3390/s19112498 - 31 May 2019

Cited by 36 | Viewed by 5814

Abstract

The lack of healthcare staff and increasing proportions of elderly population is alarming. The traditional means to look after elderly has resulted in 255,000 reported falls (only within UK). This not only resulted in extensive aftercare needs and surgeries (summing up to £4.4 billion) but also in added suffering and increased mortality. In such circumstances, the technology can greatly assist by offering automated solutions for the problem at hand. The proposed work offers an Internet of things (IoT) based patient bed-exit monitoring system in clinical settings, capable of generating a timely response to alert the healthcare workers and elderly by analyzing the wireless data streams, acquired through wearable sensors. This work analyzes two different datasets obtained from divergent families of sensing technologies, i.e., smartphone-based accelerometer and radio frequency identification (RFID) based accelerometer. The findings of the proposed system show good efficacy in monitoring the bed-exit and discriminate other ambulating activities. Furthermore, the proposed work manages to keep the average end-to-end system delay (i.e., communications of sensed data to Data Sink (DS)/Control Center (CC) + machine-based feature extraction and class identification + feedback communications to a relevant healthcare worker/elderly) below

\frac{1}{10} th

of a second. Full article

(This article belongs to the Special Issue Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications)

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23 pages, 1722 KiB

Open AccessArticle

Comparison and Efficacy of Synergistic Intelligent Tutoring Systems with Human Physiological Response

by Fehaid Alqahtani and Naeem Ramzan

Sensors 2019, 19(3), 460; https://doi.org/10.3390/s19030460 - 23 Jan 2019

Cited by 13 | Viewed by 4945

Abstract

The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer-based learning and tutoring. Intelligent Tutoring Systems (ITS) is a fast-paced growing field which deals with the design and implementation of customized computer-based instruction and feedback methods without human intervention. This paper introduces the key concepts and motivations behind the use of physiological signals. It presents a detailed discussion and experimental comparison of ITS. The synergism of ITS and physiological signals in automated tutoring systems adapted to the learner’s emotions and mental states are presented and compared. The insights are developed, and details are presented. The accuracy and classification methods of existing systems are highlighted as key areas of improvement. High-precision measurement systems and neural networks for machine-learning classification are deemed prospective directions for future improvements to existing systems. Full article

(This article belongs to the Special Issue Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications)

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21 pages, 3273 KiB

Open AccessArticle

A Smart Recommender Based on Hybrid Learning Methods for Personal Well-Being Services

by Rayan M. Nouh, Hyun-Ho Lee, Won-Jin Lee and Jae-Dong Lee

Sensors 2019, 19(2), 431; https://doi.org/10.3390/s19020431 - 21 Jan 2019

Cited by 35 | Viewed by 8450

Abstract

The main focus of the paper is to propose a smart recommender system based on the methods of hybrid learning for personal well-being services, called a smart recommender system of hybrid learning (SRHL). The essential health factor is considered to be personal lifestyle, with the help of a critical examination of various disciplines. Integrating the recommender system effectively contributes to the prevention of disease, and it also leads to a reduction in treatment cost, which contributes to an improvement in the quality of life. At the same time, there exist various challenges within the recommender system, mainly cold start and scalability. To effectively address the inefficiencies, we propose combined hybrid methods in regard to machine learning. The primary aim of this learning method is to integrate the most effective and efficient learning algorithms to examine how combined hybrid filtering can help to improve the cold star problem efficiently in the provision of personalized well-being in regard to health food service. These methods include: (1) switching among content-based and collaborative filtering; (2) identifying the user context with the integration of dynamic filtering; and (3) learning the profiles with the help of processing and screening of reflecting feedback loops. The experimental results were evaluated by using three absolute error measures, providing comparable results with other studies relative to machine learning domains. The effects of using the hybrid learning method are gathered with the help of the experimental results. Our experiments also show that the hybrid method improves accuracy by 14.61% of the average error predicted in the recommender systems in comparison to the collaborative methods, which mainly focus on the computation of similar entities. Full article

(This article belongs to the Special Issue Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications)

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14 pages, 3640 KiB

Open AccessArticle

Posture-Specific Breathing Detection

by Hualin Guan, Xiaodong Yang, Wanrong Sun, Aifeng Ren, Dou Fan, Nan Zhao, Lei Guan, Daniyal Haider and Qammer H. Abbasi

Sensors 2018, 18(12), 4443; https://doi.org/10.3390/s18124443 - 15 Dec 2018

Cited by 6 | Viewed by 3673

Abstract

Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity. Full article

(This article belongs to the Special Issue Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications)

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17 pages, 3077 KiB

Open AccessArticle

A Parallel Architecture for the Partitioning around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare

by Hassan Mushtaq, Sajid Gul Khawaja, Muhammad Usman Akram, Amanullah Yasin, Muhammad Muzammal, Shehzad Khalid and Shoab Ahmad Khan

Sensors 2018, 18(12), 4129; https://doi.org/10.3390/s18124129 - 25 Nov 2018

Cited by 8 | Viewed by 4213

Abstract

Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time. This architecture can easily be implemented either on a multi-core processor system to deal with big data or on a reconfigurable hardware platform, such as FPGA and MPSoCs, which makes it suitable for real-time clustering applications. Our proposed model partitions data equally among multiple processing cores. Each core executes the same sequence of tasks simultaneously on its respective data subset and shares intermediate results with other cores to produce results. Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel. It is also observed that the speedup graph of our proposed model becomes more linear with the increase in number of data points and as the clusters become more uniform. The results also demonstrate that the proposed architecture produces the same results as the actual PAM algorithm, but with reduced computational complexity. Full article

(This article belongs to the Special Issue Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications)

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17 pages, 3146 KiB

Open AccessArticle

Efficient AoA-Based Wireless Indoor Localization for Hospital Outpatients Using Mobile Devices

by Yanbin Hou, Xiaodong Yang and Qammer H. Abbasi

Sensors 2018, 18(11), 3698; https://doi.org/10.3390/s18113698 - 30 Oct 2018

Cited by 53 | Viewed by 5943

Abstract

The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding. Full article

(This article belongs to the Special Issue Smart, Secure and Sustainable (3S) Technologies for IoT Based Healthcare Applications)

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