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Special Issue Information

Dear Colleagues,

We are pleased to announce that the section Internet of Things is now compiling a collection of papers submitted by Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions as well as recommendations from EBMs.

We expect original papers and review articles that show state-of-the-art, theoretical, and applicative advances, new experimental discoveries, and novel technological improvements regarding the Internet of Things. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collated into a printed-edition book after the deadline and will be well promoted.

We would also like to take this opportunity to call on more excellent scholars to join the section Internet of Things so that we can work together to further develop this exciting field of research.

Prof. Dr. Klaus Moessner
Guest Editor

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.

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. Sensors 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 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

internet of multimedia things
industrial Internet of Things (IIoT)
underwater IoT communication and networks
machine-type communications
low-power and energy harvesting technologies
real-time systems for the IoT
service middleware and device management for the IoT
privacy, security, and trust in IoT systems
cyber–physical system (CPS) platforms
edge/fog/cloud computing in the IoT
data management and mining platforms for the IoT
IoT architectures and standards
future internet design for the IoT
IoT pilots and testbeds
5G and beyond 5G architectures and protocols for the IoT
AI/ML and distributed intelligence for the IoT
IoT applications and uses (smart factory, smart city, smart health, smart transportation, and smart agriculture)

Related Special Issue

Published Papers (1 paper)

Research

21 pages, 2726 KiB

Open AccessArticle

LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems

by Alexios Papaioannou, Asimina Dimara, Charalampos S. Kouzinopoulos, Stelios Krinidis, Christos-Nikolaos Anagnostopoulos, Dimosthenis Ioannidis and Dimitrios Tzovaras

Sensors 2024, 24(7), 2125; https://doi.org/10.3390/s24072125 - 26 Mar 2024

Viewed by 394

Abstract

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

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