Special Issue "Internet of Things (IoT)-Based Wireless Health: Enabling Technologies and Applications"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 October 2020).

Special Issue Editors

Dr. S.M. Riazul Islam
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Interests: Wireless Communications; Internet of Things; Applied Artificial Intelligence
Special Issues and Collections in MDPI journals
Prof. Dr. Jaime Lloret
E-Mail Website
Guest Editor
Department of Communications, Polytechnic University of Valencia, Valencia, Spain
Interests: network protocols; network algorithms; wireless sensor networks; ad hoc networks; multimedia streaming
Special Issues and Collections in MDPI journals
Prof. Dr. Yousaf Bin Zikria
E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Interests: IoT; 5G; wireless networks; cognitive radio networks; information and network security
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless health is transforming health care by integrating wireless technologies into conventional medicine, including the diagnosis, monitoring, and treatment of illness. The list of tools for wirelessly monitoring and diagnosing disease is expanding. The ability to remotely manage drugs and health devices is increasing. With the aid of smart and intelligent systems, the knowledge of how genetics affects susceptibilities to disease is growing. These trends suggest that the society is approaching towards a revolution in health care. The role that wireless health plays is being further enhanced by the Internet of things (IoT). The IoT revolution is reshaping modern healthcare with promising technological, economic, and social prospects. IoT-based healthcare services are expected to further reduce costs, increase the quality of life, and enrich the user’s experience. Despite the enormous potentials and a decent amount of existing research, IoT-based healthcare comes with several difficulties in its path, including regulatory hurdles, privacy, and interoperability standards. The IoT still remains in its infancy in the healthcare field, and researchers across the world are therefore working hard to address the potential of the IoT in the healthcare field, with consideration of the various practical challenges. The purpose of this Special Issue is to present recent novel advances in enabling technologies for IoT-based wireless health.

Both theoretical and practical papers are solicited on the following related aspects: algorithms, system design, performance analysis, and experimental studies. Potential topics include, but are not limited to, the keywords listed below.

  • Network architectures and platforms for IoT-based wireless health
  • Applications and industrial trends in IoT-based wireless health
  • Security and privacy features for IoT-based wireless health
  • Policies and regulations for IoT-based wireless health
  • Integration of big data and ambient intelligence with IoT-based wireless health
  • Sensors and wearables for IoT-based wireless health
  • Ontology and semantic knowledge for IoT-based wireless health
  • Precision medicine and wireless health
  • Machines learning and bioinformatics in wireless health
  • Uses of smartphone in health care
  • Technology convergence and standardization issues for IoT-based wireless health
  • Energy-efficient protocols for IoT-based wireless health
  • QoS Issues for IoT-based wireless health
  • Cost Analysis for IoT-based wireless health
  • Business model for IoT-based wireless health

The technical program committee members are as follows:

  1. Prof. Dr. Tao Han

School of Electronic Information and Communications, Huazhong University of Science and Technology, Hubei 430074, China

  1. Prof. Dr. M. Abdullah-Al-Wadud

Department of Software Engineering, King Saud University, Riyadh 11451, Saudi Arabia

Prof. Dr. S.M. Riazul Islam
Prof. Dr. Jaime Lloret Mauri
Prof. Dr. Yousaf Bin Zikria
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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

  • Network architectures and platforms for IoT-based wireless health
  • Applications and industrial trends in IoT-based wireless health
  • Security and privacy features for IoT-based wireless health
  • Policies and regulations for IoT-based wireless health
  • Integration of big data and ambient intelligence with IoT-based wireless health
  • Sensors and wearables for IoT-based wireless health
  • Ontology and semantic knowledge for IoT-based wireless health
  • Precision medicine and wireless health
  • Machines learning and bioinformatics in wireless health
  • Uses of smartphones in health care
  • Technology convergence and standardization issues for IoT-based wireless health
  • Energy-efficient protocols for IoT-based wireless health
  • QoS Issues for IoT-based wireless health
  • Cost Analysis for IoT-based wireless health
  • Business model for IoT-based wireless health

Published Papers (15 papers)

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Editorial

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Open AccessEditorial
Internet of Things (IoT)-Based Wireless Health: Enabling Technologies and Applications
Electronics 2021, 10(2), 148; https://doi.org/10.3390/electronics10020148 - 12 Jan 2021
Cited by 1 | Viewed by 459
Abstract
Wireless health is transforming health care by integrating wireless technologies into conventional medicine, including the diagnosis, monitoring, and treatment of illness [...] Full article

Research

Jump to: Editorial, Review

Open AccessArticle
A Public Platform for Virtual IoT-Based Monitoring and Tracking of COVID-19
Electronics 2021, 10(1), 12; https://doi.org/10.3390/electronics10010012 - 23 Dec 2020
Cited by 2 | Viewed by 940
Abstract
The world is developing an app that alerts my smartphone when a COVID-19 (COrona VIrus Disease 19) confirmed case comes near me. However, regardless of what will be put to practical use first, the COVID-19 tracking system should satisfy the issues of legalization [...] Read more.
The world is developing an app that alerts my smartphone when a COVID-19 (COrona VIrus Disease 19) confirmed case comes near me. However, regardless of what will be put to practical use first, the COVID-19 tracking system should satisfy the issues of legalization of location tracking and scalability as a public platform used by the world. Additional problems need solutions related to real-time authentication for information gathering, blind naming and privacy of tracked persons, and quality of service on the Query/Reply procedure. This paper proposes the Software-Defined Networking Controller-centric global public platform to monitor and track information for the COVID-19 relevant people and provide real-time information disclosure services to world-wide Centers for Disease Control and Prevention (CDCs) and regular users. The CDC manages a list of people who needs to be monitored related to the COVID-19 and forcibly installs COVID-19 virtual Internet of Things (vIoT) nodes in the form of applications on their smartphones. In addition to these nodes, the vIoT support nodes also engage as information providers to improve the quality of information services. The design of our platform aims to ensure confidentiality and authentication services giving individually different secret keys. In addition, our platform meets system scalability and reduces Query/Reply latency, where the platform accommodates a large number of world-wide CDCs and persons in control per CDC. Full article
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Open AccessArticle
An IoT Smart Environment in Support of Disease Diagnosis Decentralization
Electronics 2020, 9(12), 2108; https://doi.org/10.3390/electronics9122108 - 10 Dec 2020
Cited by 1 | Viewed by 457
Abstract
The percentage of seniors in the global population is constantly growing and solutions in the field of fall detection and early detection of neuro-degenerative pathologies have a crucial role in order to increase life expectancy and quality of life. This study aims to [...] Read more.
The percentage of seniors in the global population is constantly growing and solutions in the field of fall detection and early detection of neuro-degenerative pathologies have a crucial role in order to increase life expectancy and quality of life. This study aims to extend fall detection and effective recognition of early signs of diseases to new smart environments, conceiving the decentralization of diagnostic monitoring in everyday life activities in a more pervasive paradigm. Inspiring to research outcomes, in this work an architecture is designed to detect falls in crowded indoor environments during events/exhibitions, for favoring a timely and effective intervention. It also foresees a continue monitoring of the gait for seniors during the visit, thus extracting key features which are stored on a dedicated database. The proposed solution allows third party researchers to perform analysis on the obtained gait datasets, through the adoption of advanced data-mining techniques for the detection of early signs of neuro-degenerative diseases and other pathologies. The architecture designed here aims to provide a step forward concerning the extension of smart monitoring environments for the detection of falls and early signs of pathologies in everyday life, in a more pervasive and decentralized paradigm. Full article
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Open AccessArticle
Secure Exchange of Medical Data Using a Novel Real-Time Biometric-Based Protection and Recognition Method
Electronics 2020, 9(12), 2013; https://doi.org/10.3390/electronics9122013 - 28 Nov 2020
Cited by 2 | Viewed by 501
Abstract
Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy [...] Read more.
Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy when shared with an external organization in the current arena of the internet of medical things (IoMT). Various watermarking techniques were introduced in the medical field to secure patients’ data. Most of the existing techniques focus on an image or document’s imperceptibility without considering the watermark(logo). In this research, a novel technique of watermarking is introduced, which supersedes the shortcomings of existing approaches. It guarantees the imperceptibility of the image/document and takes care of watermark(biometric), which is further passed through a process of recognition for claiming ownership. It extracts suitable frequencies from the transform domain using specialized filters to increase the robustness level. The extracted frequencies are modified by adding the biomedical information while considering the strength factor according to the human visual system. The watermarked frequencies are further decomposed through a singular value decomposition technique to increase payload capacity up to (256 × 256). Experimental results over a variety of medical and official images demonstrate the average peak signal-to-noise ratio (PSNR 54.43), and the normal correlation (N.C.) value is 1. PSNR and N.C. of the watermark were calculated after attacks. The proposed technique is working in real-time for embedding, extraction, and recognition of biometrics over the internet, and its uses can be realized in various platforms of IoMT technologies. Full article
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Open AccessArticle
IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique
Electronics 2020, 9(9), 1469; https://doi.org/10.3390/electronics9091469 - 08 Sep 2020
Cited by 8 | Viewed by 1021
Abstract
The unprompted patient’s and inimitable physician’s experience shared on online health communities (OHCs) contain a wealth of unexploited knowledge. Med Help and eHealth are some of the online health communities offering new insights and solutions to all health issues. Diabetes mellitus (DM), thyroid [...] Read more.
The unprompted patient’s and inimitable physician’s experience shared on online health communities (OHCs) contain a wealth of unexploited knowledge. Med Help and eHealth are some of the online health communities offering new insights and solutions to all health issues. Diabetes mellitus (DM), thyroid disorders and tuberculosis (TB) are chronic diseases increasing rapidly every year. As part of the project described in this article comments related to the diseases from Med Help were collected. The comments contain the patient and doctor discussions in an unstructured format. The sematic vision of the internet of things (IoT) plays a vital role in organizing the collected data. We pre-processed the data using standard natural language processing techniques and extracted the essential features of the words using the chi-squared test. After preprocessing the documents, we clustered them using the K-means++ algorithm, which is a popular centroid-based unsupervised iterative machine learning algorithm. A generative probabilistic model (LDA) was used to identify the essential topic in each cluster. This type of framework will empower the patients and doctors to identify the similarity and dissimilarity about the various diseases and important keywords among the diseases in the form of symptoms, medical tests and habits. Full article
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Open AccessArticle
End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring
Electronics 2020, 9(9), 1439; https://doi.org/10.3390/electronics9091439 - 03 Sep 2020
Cited by 6 | Viewed by 1607
Abstract
Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the [...] Read more.
Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses. Full article
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Open AccessArticle
Automatic Identification of High Impact Relevant Articles to Support Clinical Decision Making Using Attention-Based Deep Learning
Electronics 2020, 9(9), 1364; https://doi.org/10.3390/electronics9091364 - 22 Aug 2020
Cited by 1 | Viewed by 747
Abstract
To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning [...] Read more.
To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning for finding and ranking relevant studies against a topic of interest. For learning the proposed model, we collect data consisting of 240,324 clinical articles from the 2018 Precision Medicine track in Text REtrieval Conference (TREC) to identify and rank relevant documents matched with the user query. We built a BERT (Bidirectional Encoder Representations from Transformers) based classification model to classify high and low impact articles. We contextualized word embedding to create vectors of the documents, and user queries combined with genetic information to find contextual similarity for determining the relevancy score to rank the articles. We compare our proposed model results with existing approaches and obtain a higher accuracy of 95.44% as compared to 94.57% (the next best performer) and get a higher precision by about 14% at [email protected] (precision at 5) and about 12% at [email protected] (precision at 10). The contextually viable and competitive outcomes of the proposed model confirm the suitability of our proposed model for use in domains like evidence-based precision medicine. Full article
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Open AccessArticle
Deep Learning Based Biomedical Literature Classification Using Criteria of Scientific Rigor
Electronics 2020, 9(8), 1253; https://doi.org/10.3390/electronics9081253 - 05 Aug 2020
Cited by 1 | Viewed by 789
Abstract
A major blockade to support the evidence-based clinical decision-making is accurately and efficiently recognizing appropriate and scientifically rigorous studies in the biomedical literature. We trained a multi-layer perceptron (MLP) model on a dataset with two textual features, title and abstract. The dataset consisting [...] Read more.
A major blockade to support the evidence-based clinical decision-making is accurately and efficiently recognizing appropriate and scientifically rigorous studies in the biomedical literature. We trained a multi-layer perceptron (MLP) model on a dataset with two textual features, title and abstract. The dataset consisting of 7958 PubMed citations classified in two classes: scientific rigor and non-rigor, is used to train the proposed model. We compare our model with other promising machine learning models such as Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosted Tree (GBT) approaches. Based on the higher cumulative score, deep learning was chosen and was tested on test datasets obtained by running a set of domain-specific queries. On the training dataset, the proposed deep learning model obtained significantly higher accuracy and AUC of 97.3% and 0.993, respectively, than the competitors, but was slightly lower in the recall of 95.1% as compared to GBT. The trained model sustained the performance of testing datasets. Unlike previous approaches, the proposed model does not require a human expert to create fresh annotated data; instead, we used studies cited in Cochrane reviews as a surrogate for quality studies in a clinical topic. We learn that deep learning methods are beneficial to use for biomedical literature classification. Not only do such methods minimize the workload in feature engineering, but they also show better performance on large and noisy data. Full article
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Open AccessFeature PaperArticle
A Smart Glucose Monitoring System for Diabetic Patient
Electronics 2020, 9(4), 678; https://doi.org/10.3390/electronics9040678 - 22 Apr 2020
Cited by 6 | Viewed by 1363
Abstract
Diabetic patients need ongoing surveillance, but this involves high costs for the government and family. The combined use of information and communication technologies (ICTs), artificial intelligence and smart devices can reduce these costs, helping the diabetic patient. This paper presents an intelligent architecture [...] Read more.
Diabetic patients need ongoing surveillance, but this involves high costs for the government and family. The combined use of information and communication technologies (ICTs), artificial intelligence and smart devices can reduce these costs, helping the diabetic patient. This paper presents an intelligent architecture for the surveillance of diabetic disease that will allow physicians to remotely monitor the health of their patients through sensors integrated into smartphones and smart portable devices. The proposed architecture includes an intelligent algorithm developed to intelligently detect whether a parameter has exceeded a threshold, which may or may not involve urgency. To verify the proper functioning of this system, we developed a small portable device capable of measuring the level of glucose in the blood for diabetics and body temperature. We designed a secure mechanism to establish a wireless connection with the smartphone. Full article
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Open AccessArticle
Open IoT Architecture for Continuous Patient Monitoring in Emergency Wards
Electronics 2019, 8(10), 1074; https://doi.org/10.3390/electronics8101074 - 23 Sep 2019
Cited by 8 | Viewed by 1130
Abstract
Due to multiple reasons, emergency wards can become overloaded with patients, some of which can be in critical health conditions. To improve the emergency service and avoid deaths and serious adverse events that could be potentially prevented, it is mandatory to do a [...] Read more.
Due to multiple reasons, emergency wards can become overloaded with patients, some of which can be in critical health conditions. To improve the emergency service and avoid deaths and serious adverse events that could be potentially prevented, it is mandatory to do a continuous monitoring of patients physiological parameters. This is a good fit for Internet of Things (IoT) technology, but the scenario imposes hard constraints on autonomy, connectivity, interoperability, and delay. In this paper, we propose a full Internet-based architecture using open protocols from the wearable sensors up to the monitoring system. Particularly, we use low-cost and low-power WiFi-enabled wearable physiological sensors that connect directly to the Internet infrastructure and run open communication protocols, namely, oneM2M. At the upper end, our architecture relies on openEHR for data semantics, storage, and monitoring. Overall, we show the feasibility of our open IoT architecture exhibiting 20–50 ms end-to-end latency and 30–50 h sensor autonomy at a fraction of the cost of current non-interoperable vertical solutions. Full article
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Open AccessArticle
An Intelligent Air Quality Sensing System for Open-Skin Wound Monitoring
Electronics 2019, 8(7), 801; https://doi.org/10.3390/electronics8070801 - 17 Jul 2019
Cited by 3 | Viewed by 1454
Abstract
There are many factors that may have a significant effect on the skin wound healing process. The environment is one of them. Although different previous research woks have highlighted the role of environmental elements such as humidity, temperature, dust, etc., in the process [...] Read more.
There are many factors that may have a significant effect on the skin wound healing process. The environment is one of them. Although different previous research woks have highlighted the role of environmental elements such as humidity, temperature, dust, etc., in the process of skin wound healing, there is no predefined method available to identify the favourable or adverse environment conditions that seriously affect (positively or negatively) the skin wound healing process. In the current research work, an IoT-based approach is used to design an AQSS (Air Quality Sensing System) using sensors for the acquisition of real-time environment data, and the SVM (Support Vector Machine) classifier is applied to classify environments into one of the two categories, i.e., “favourable”, and “unfavourable”. The proposed system is also supported with an Android application to provide an easy-to-use interface. The proposed system provides an easy and simple means for patients to evaluate the environmental parameters and monitor their effects in the process of open skin wound healing. Full article
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Open AccessArticle
A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction
Electronics 2019, 8(6), 635; https://doi.org/10.3390/electronics8060635 - 05 Jun 2019
Cited by 6 | Viewed by 1468
Abstract
Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to [...] Read more.
Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice. Full article
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Open AccessArticle
HealthyBroker: A Trustworthy Blockchain-Based Multi-Cloud Broker for Patient-Centered eHealth Services
Electronics 2019, 8(6), 602; https://doi.org/10.3390/electronics8060602 - 29 May 2019
Cited by 10 | Viewed by 1681
Abstract
Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and [...] Read more.
Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker can provide care teams with a trustworthy, transparent ecosystem that can facilitate information sharing and well-informed decisions for patient-centric care. Full article
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Review

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Open AccessReview
Role of IoT Technology in Agriculture: A Systematic Literature Review
Electronics 2020, 9(2), 319; https://doi.org/10.3390/electronics9020319 - 12 Feb 2020
Cited by 26 | Viewed by 3647
Abstract
The growing demand for food in terms of quality and quantity has increased the need for industrialization and intensification in the agriculture field. Internet of Things (IoT) is a highly promising technology that is offering many innovative solutions to modernize the agriculture sector. [...] Read more.
The growing demand for food in terms of quality and quantity has increased the need for industrialization and intensification in the agriculture field. Internet of Things (IoT) is a highly promising technology that is offering many innovative solutions to modernize the agriculture sector. Research institutions and scientific groups are continuously working to deliver solutions and products using IoT to address different domains of agriculture. This paper presents a systematic literature review (SLR) by conducting a survey of IoT technologies and their current utilization in different application domains of the agriculture sector. The underlying SLR has been compiled by reviewing research articles published in well-reputed venues between 2006 and 2019. A total of 67 papers were carefully selected through a systematic process and classified accordingly. The primary objective of this systematic study is the collection of all relevant research on IoT agricultural applications, sensors/devices, communication protocols, and network types. Furthermore, it also discusses the main issues and challenges that are being investigated in the field of agriculture. Moreover, an IoT agriculture framework has been presented that contextualizes the representation of a wide range of current solutions in the field of agriculture. Similarly, country policies for IoT-based agriculture have also been presented. Lastly, open issues and challenges have been presented to provide the researchers promising future directions in the domain of IoT agriculture. Full article
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Open AccessFeature PaperReview
Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review
Electronics 2019, 8(10), 1081; https://doi.org/10.3390/electronics8101081 - 24 Sep 2019
Cited by 39 | Viewed by 2251
Abstract
Internet of Things (IoT) is an evolution of the Internet and has been gaining increased attention from researchers in both academic and industrial environments. Successive technological enhancements make the development of intelligent systems with a high capacity for communication and data collection possible, [...] Read more.
Internet of Things (IoT) is an evolution of the Internet and has been gaining increased attention from researchers in both academic and industrial environments. Successive technological enhancements make the development of intelligent systems with a high capacity for communication and data collection possible, providing several opportunities for numerous IoT applications, particularly healthcare systems. Despite all the advantages, there are still several open issues that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information security, and privacy. IoT provides important characteristics to healthcare systems, such as availability, mobility, and scalability, that offer an architectural basis for numerous high technological healthcare applications, such as real-time patient monitoring, environmental and indoor quality monitoring, and ubiquitous and pervasive information access that benefits health professionals and patients. The constant scientific innovations make it possible to develop IoT devices through countless services for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced living environments (ELEs). This paper reviews the current state of the art on IoT architectures for ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities, open-source platforms, and operating systems. Furthermore, this document synthesizes the existing body of knowledge and identifies common threads and gaps that open up new significant and challenging future research directions. Full article
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