Special Issue "New Trends on Internet-of-Things (IoT)"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Smart Cities and Urban Management".

Deadline for manuscript submissions: 22 February 2022.

Special Issue Editors

Dr. Jaume Segura-Garcia
E-Mail Website
Guest Editor
Department of Computer Science, ETSE, Universitat de València, 46100 Burjassot, Valencia, Spain
Interests: IoT; WSN; Smart Cities; signal processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Santiago Felici-Castell
E-Mail Website
Guest Editor
Department of Computer Science, ETSE, Universitat de València, 46100 Burjassot, Valencia, Spain
Interests: WSN; IoT; routing protocols; networks; SDN; slicing; orchestration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a growing field of research and development. It is connected to other concepts like artificial intelligence and Smart Cities and is also applied to various sectors such as industry (IIoT) and health (eHealth), all of which contribute to the development of a smart society. Social IoT is a new concept derived from this integration, and it is gaining ground due to its potential benefits of IoT when applied to the social networks, such as simple navigability of a dynamic network composed of many objects, robustness in the management of object trustworthiness when providing information and services, and efficiency in the dynamic discovery of services and information. Another new concept is “prosumer communities”, which refers to applications in the energy sector that could enable new links between devices and between clusters of devices, thereby allowing synergetic relationships between producers and consumers. All of these concepts related to IoT describe a new panorama in the evolution of future generation networks and open new windows for the interrelationships between technology and society. This Special Issue will collect original research papers and novel insights into new trends in IoT systems relating to energy harvesting, management, artificial intellgence, and social interrelations.

Dr. Jaume Segura-Garcia
Dr. Santiago Felici-Castell
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 papers will be 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.

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. Energies 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 2200 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

  • IoT process management (scheduling, synchronization, threading, programming, etc.)
  • Energy-efficient IoT protocols (management of sleep modes, hibernate modes, etc.)
  • Energy harvesting for IoT devices
  • IoT Energy Applications (e.g., Smart metering, Smart vehicles, etc.)
  • AI–IoT
  • IIoT
  • SIoT and/or SIoE
  • Sustainable IoT systems

Published Papers (3 papers)

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Research

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Article
Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing
Energies 2021, 14(19), 6414; https://doi.org/10.3390/en14196414 - 07 Oct 2021
Cited by 1 | Viewed by 422
Abstract
Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze [...] Read more.
Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze the data for infectious diseases, i.e., body fever, flu, COVID-19, shortness of breath, etc. with the least operation cost. However, the most important research challenges in such applications are storing the medical data on a secured cloud and make the disease diagnosis system more energy efficient. Additionally, the rapid explosion of IoMT technology has involved many cyber-criminals and continuous attempts to compromise medical devices with information loss and generating bogus certificates. Thus, the increase in modern technologies for healthcare applications based on IoMT, securing health data, and offering trusted communication against intruders is gaining much research attention. Therefore, this study aims to propose an energy-efficient IoT e-health model using artificial intelligence with homomorphic secret sharing, which aims to increase the maintainability of disease diagnosis systems and support trustworthy communication with the integration of the medical cloud. The proposed model is analyzed and proved its significance against relevant systems. Full article
(This article belongs to the Special Issue New Trends on Internet-of-Things (IoT))
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Article
FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks
Energies 2021, 14(13), 3935; https://doi.org/10.3390/en14133935 - 02 Jul 2021
Cited by 1 | Viewed by 790
Abstract
Technological advancements have led to increased confidence in the design of large-scale wireless networks that comprise small energy constraint devices. Despite the boost in technological advancements, energy dissipation and fault tolerance are amongst the key deciding factors while designing and deploying wireless sensor [...] Read more.
Technological advancements have led to increased confidence in the design of large-scale wireless networks that comprise small energy constraint devices. Despite the boost in technological advancements, energy dissipation and fault tolerance are amongst the key deciding factors while designing and deploying wireless sensor networks. This paper proposes a Fault-tolerant Energy-efficient Hierarchical Clustering Algorithm (FEHCA) for wireless sensor networks (WSNs), which demonstrates energy-efficient clustering and fault-tolerant operation of cluster heads (CHs). It treats CHs as no special node but equally prone to faults as normal sensing nodes of the cluster. The proposed scheme addresses some of the limitations of prominent hierarchical clustering algorithms, such as the randomized election of the cluster heads after each round, which results in significant energy dissipation; non-consideration of the residual energy of the sensing nodes while selecting cluster heads, etc. It utilizes the capability of vector quantization to partition the deployed sensors into an optimal number of clusters and ensures that almost the entire area to be monitored is alive for most of the network’s lifetime. This supports better decision-making compared to decisions made on the basis of limited area sensing data after a few rounds of communication. The scheme is implemented for both friendly as well as hostile deployments. The simulation results are encouraging and validate the proposed algorithm. Full article
(This article belongs to the Special Issue New Trends on Internet-of-Things (IoT))
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Review

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Review
Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review
Energies 2021, 14(19), 6384; https://doi.org/10.3390/en14196384 - 06 Oct 2021
Viewed by 414
Abstract
The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, [...] Read more.
The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT. Full article
(This article belongs to the Special Issue New Trends on Internet-of-Things (IoT))
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