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Trust in the Internet of Things

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

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 4495

Special Issue Editor


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Guest Editor
Institut de Recherche en Informatique de Toulouse (IRIT), Université Paul Sabatier, 31062 Toulouse, France
Interests: trust management; identity and access management; privacy protection

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) underpins most industries and businesses and has now entered people’s homes. It allows the automation of tasks and monitoring of systems and people, and feeds the data needed by artificial intelligence applications. IoT is the cornerstone of Industry 4.0, Agriculture 4.0, eHealth, connected vehicles and smart cities, to cite a few examples.

However, this widespread adoption of IoT is accompanied by many questions about the security, ethics, and privacy of such IoT systems. Since they significantly impact our daily lives, it is of paramount importance to increase their trustworthiness. 

This Special Issue is an ideal platform to report the more recent research advances in trust in support of IoT systems. We invite contributions with original research or review articles, which share new mechanisms, protocols, and architectures, present theoretical proposals, describe practical deployments, or identify new research opportunities.

Articles may include, but are not limited to, the following topics related to trustworthy IoT systems:

  • Security and IoT;
  • Ethics and IoT;
  • Privacy and IoT;
  • Ownership and control of IoT systems, including of generated data;
  • Cybersecurity attacks of IoT;
  • Risks and threats originated by IoT systems;
  • Secure conception and deployment of IoT systems;
  • Secure computing and IoT;
  • Reliability of IoT systems, including of generated data;
  • Blockchains and IoT;
  • Regulations and compliance (e.g., the EU’s GDPR, NIS2, and Cybersecurity Resilience Act, or the USA’s IoT Cybersecurity Improvement Act).

Dr. Romain Laborde
Guest Editor

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Published Papers (1 paper)

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25 pages, 3947 KiB  
Article
Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain
by Ibrahim Aqeel, Ibrahim Mohsen Khormi, Surbhi Bhatia Khan, Mohammed Shuaib, Ahlam Almusharraf, Shadab Alam and Nora A. Alkhaldi
Sensors 2023, 23(11), 5349; https://doi.org/10.3390/s23115349 - 5 Jun 2023
Cited by 15 | Viewed by 4086
Abstract
The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited [...] Read more.
The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions. Full article
(This article belongs to the Special Issue Trust in the Internet of Things)
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