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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 14735

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Special Issue Editor

Prof. Dr. Cristiano André da Costa

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Guest Editor

Software Innovation Laboratory – SOFTWARELAB, Universidade do Vale do Rio dos Sinos – UNISINOS, Sao Leopoldo 93022-750, Brazil
Interests: Internet of Things; distributed applications; wearable computing; health informatics

Special Issue Information

Dear Colleagues,

The Internet of Health Things (IoHT) is the application of Internet of Things (IoT) concepts to the area of healthcare. The IoHT consists of interconnected objects with the capacity to exchange and process data, focusing on improving patient health. Among the areas involved in the IoHT, we highlight research related to the acquisition, storage, processing, and presentation of collected health data. Another important topic is the infrastructure for managing all IoHT processes. Acquisition realizes smart health objects, including the development of devices, including wearables, sensors, and actuators, generally gathering data related to vital signs or other physiological patient conditions. Storage deals with the representation of the information generated or derived from the collected data and includes aspects such as interoperability, i.e., the use of semantic strategies, and standard protocols, such as HL7 FHIR or OpenEHR. Processing regards the analysis of IoHT-collected data, transforming them into information and knowledge. This processing phase includes advanced data fusion, predictive analysis, and machine, federated, and swarm learning approaches. Presentation refers to how results appear to the stakeholders and how they interact with the system, including the generation of notifications, alerts, suggested actions, and user experience. In terms of infrastructure, combinations of cloud, fog, and edge computing have been proposed for used in the IoHT area. More recently, blockchain has also been employed for applications in the IoHT.

This Special Issue addresses all research that applies the Internet of Things to the healthcare area.

Prof. Dr. Cristiano André da Costa
Guest Editor

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Keywords

Internet of Health Things
Internet of Medical Things
wearables
healthcare sensors and actuators
healthcare interoperability
healthcare protocols
healthcare-applied machine and deep learning
federated and swarm learning
edge computing
fog and cloud computing
blockchain

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Published Papers (5 papers)

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Research

29 pages, 5389 KiB

Open AccessArticle

PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber–Physical Systems

by Venkata K. V. V. Bathalapalli, Saraju P. Mohanty, Elias Kougianos, Vasanth Iyer and Bibhudutta Rout

Sensors 2024, 24(3), 938; https://doi.org/10.3390/s24030938 - 31 Jan 2024

Cited by 2 | Viewed by 1605

Abstract

This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber–physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient’s Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle’s Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient’s gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient’s BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time. Full article

(This article belongs to the Special Issue Internet of Health Things)

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24 pages, 6577 KiB

Open AccessArticle

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

by Ziyad Almudayni, Ben Soh and Alice Li

Sensors 2023, 23(16), 7286; https://doi.org/10.3390/s23167286 - 20 Aug 2023

Cited by 1 | Viewed by 1274

Abstract

In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future. Full article

(This article belongs to the Special Issue Internet of Health Things)

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23 pages, 3320 KiB

Open AccessArticle

Resource Allocation and Data Offloading Strategy for Edge-Computing-Assisted Intelligent Telemedicine System

by Yan Li, Yubo Wang, Shiyong Chen, Xinyu Huang and Tiancong Huang

Sensors 2023, 23(10), 4943; https://doi.org/10.3390/s23104943 - 21 May 2023

Cited by 1 | Viewed by 1766

Abstract

Intelligent telemedicine technology has been widely applied due to the quick development of the Internet of Things (IoT). The edge-computing scheme can be regarded as a feasible solution to reduce energy consumption and enhance the computing capabilities for the Wireless Body Area Network (WBAN). For an edge-computing-assisted intelligent telemedicine system, a two-layer network architecture composed of WBAN and Edge-Computing Network (ECN) was considered in this paper. Moreover, the age of information (AoI) was adopted to describe the time cost for the TDMA transmission mechanism in WBAN. According to the theoretical analysis, the strategy for resource allocation and data offloading in edge-computing-assisted intelligent telemedicine systems can be expressed as a system utility function optimizing problem. To maximize the system utility, an incentive mechanism based on contract theory (CT) was considered to motivate edge servers (ESs) to participate in system cooperation. To minimize the system cost, a cooperative game was developed to address the slot allocation in WBAN, while a bilateral matching game was utilized to optimize the data offloading problem in ECN. Simulation results have verified the effectiveness of the strategy proposed in terms of the system utility. Full article

(This article belongs to the Special Issue Internet of Health Things)

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19 pages, 4620 KiB

Open AccessArticle

An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks

by Zeeshan Ali, Sheneela Naz, Hira Zaffar, Jaeun Choi and Yongsung Kim

Sensors 2023, 23(7), 3548; https://doi.org/10.3390/s23073548 - 28 Mar 2023

Cited by 13 | Viewed by 3047

Abstract

Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is very high, which leads to less accuracy of various traditional, parametric, and deep learning-based methods. Hence, as a solution to the challenge of accurate segmentation, we propose an advanced generative deep learning model called the Conditional Generative Adversarial Network (cGAN) for lesion segmentation. In the suggested technique, the generation of segmented images is conditional on dermoscopic images of skin lesions to generate accurate segmentation. We assessed the proposed model using three distinct datasets including DermQuest, DermIS, and ISCI2016, and attained optimal segmentation results of 99%, 97%, and 95% performance accuracy, respectively. Full article

(This article belongs to the Special Issue Internet of Health Things)

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23 pages, 1325 KiB

Open AccessArticle

A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach

by Fausto Neri da Silva Vanin, Lucas Micol Policarpo, Rodrigo da Rosa Righi, Sandra Marlene Heck, Valter Ferreira da Silva, José Goldim and Cristiano André da Costa

Sensors 2023, 23(1), 14; https://doi.org/10.3390/s23010014 - 20 Dec 2022

Cited by 17 | Viewed by 6181

Abstract

Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms. Full article

(This article belongs to the Special Issue Internet of Health Things)

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