Computational Intelligence in Wireless Sensor Networks and IoT

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 4240

Special Issue Editors


E-Mail Website
Guest Editor
TelSiP Research Laboratory, Department of Electrical and Electronic Engineering, Faculty of Engineering, University of West Attica, 12244 Athens, Greece
Interests: computational intelligence; optimization; nonlinear system modeling and control with emphasis on model predictive control methods, and applications for autonomous vehicles; energy systems; process engineering; materials science; geoscience and environmental science

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
Interests: algorithm design; approximation algorithms; algorithmic mechanism design; game theory, optimization algorithms for wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, thanks to the advances achieved in micro-electro-mechanical system (MEMS) technology, it is possible to mass manufacture low-cost devices that, despite their small size, have great sensing, processing, and communication capabilities. Through the networked collaboration of such devices, data concerning ambient conditions in any field of interest can be monitored and managed.  

Taking advantage of this technology, both wireless sensor networks (WSNs) and the Internet of Things (IoT) have an endless range of applications. However, WSNs and the IoT face various problems that cannot be surmounted using typical mathematical approaches. Computational intelligence (CI) can provide very effective solutions to problems of this kind, thanks to its methodologies, which are inspired by mechanisms found in nature.

This Special Issue aims to present articles related to the state of the art, standards, experimentations, implementations, applications, new research proposals, and industrial case studies of CI in WSNs and the IoT. Submissions must be original and neither published in nor under consideration for any other conference or journal.

Prof. Dr. Dionisis Kandris
Prof. Dr. Alex Alexandridis
Dr. Eleftherios Anastasiadis
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 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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • machine learning algorithms in WSNs and IoT
  • neural algorithms in WSNs and IoT
  • algorithms for big data analytics in WSNs and IoT
  • deep learning algorithms for IoT in WSNs and IoT
  • algorithms for self-learning systems in WSNs and IoT
  • algorithm-supervised and -unsupervised learning methods in WSNs and IoT
  • evolutionary algorithms in WSNs and IoT
  • meta-heuristic algorithms in WSNs and IoT
  • algorithm swarm intelligence in WSNs and IoT
  • algorithm hybrid intelligent systems in WSNs and IoT
  • fuzzy logic algorithms in WSNs and IoT
  • neuro-fuzzy algorithms in WSNs and IoT
  • probabilistic reasoning algorithms in WSNs and IoT

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

21 pages, 3167 KiB  
Article
Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
by Reiner Jedermann
Algorithms 2023, 16(4), 203; https://doi.org/10.3390/a16040203 - 11 Apr 2023
Cited by 1 | Viewed by 1652
Abstract
Wireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite impulse response [...] Read more.
Wireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite impulse response (FIR) filters, entail a group delay of 10 s of samples, which is not acceptable regarding the typical interval of wireless sensors of seconds or minutes. The purpose of this paper is to verify the feasibility of single-delay resampling, i.e., the algorithm resamples the data without waiting for future samples. A new method to parametrize Kriging interpolation is presented and compared with two variants of Lagrange interpolation in detailed simulations for the resulting prediction error. Kriging provided the most accurate resampling in the group-delay scenario. The single-delay scenario required almost double the OSR to achieve the same signal-to-noise ratio (SNR). An OSR between 1.8 and 3.1 was necessary for single-delay resampling, depending on the required SNR and signal distortions in terms of jitter, missing samples, and noise. Kriging was the least noise-sensitive method. Especially for signals with missing samples, Kriging provided the best accuracy. The simulations showed that single-delay resampling is feasible, but at the expense of higher OSR and limited SNR. Full article
(This article belongs to the Special Issue Computational Intelligence in Wireless Sensor Networks and IoT)
Show Figures

Figure 1

Review

Jump to: Research

32 pages, 2679 KiB  
Review
An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
by Vasiliki Demertzi, Stavros Demertzis and Konstantinos Demertzis
Algorithms 2023, 16(8), 378; https://doi.org/10.3390/a16080378 - 6 Aug 2023
Cited by 7 | Viewed by 1844
Abstract
The rapid advancements in technology have given rise to groundbreaking solutions and practical applications in the field of the Industrial Internet of Things (IIoT). These advancements have had a profound impact on the structures of numerous industrial organizations. The IIoT, a seamless integration [...] Read more.
The rapid advancements in technology have given rise to groundbreaking solutions and practical applications in the field of the Industrial Internet of Things (IIoT). These advancements have had a profound impact on the structures of numerous industrial organizations. The IIoT, a seamless integration of the physical and digital realms with minimal human intervention, has ushered in radical changes in the economy and modern business practices. At the heart of the IIoT lies its ability to gather and analyze vast volumes of data, which is then harnessed by artificial intelligence systems to perform intelligent tasks such as optimizing networked units’ performance, identifying and correcting errors, and implementing proactive maintenance measures. However, implementing IIoT systems is fraught with difficulties, notably in terms of security and privacy. IIoT implementations are susceptible to sophisticated security attacks at various levels of networking and communication architecture. The complex and often heterogeneous nature of these systems makes it difficult to ensure availability, confidentiality, and integrity, raising concerns about mistrust in network operations, privacy breaches, and potential loss of critical, personal, and sensitive information of the network's end-users. To address these issues, this study aims to investigate the privacy requirements of an IIoT ecosystem as outlined by industry standards. It provides a comprehensive overview of the IIoT, its advantages, disadvantages, challenges, and the imperative need for industrial privacy. The research methodology encompasses a thorough literature review to gather existing knowledge and insights on the subject. Additionally, it explores how the IIoT is transforming the manufacturing industry and enhancing industrial processes, incorporating case studies and real-world examples to illustrate its practical applications and impact. Also, the research endeavors to offer actionable recommendations on implementing privacy-enhancing measures and establishing a secure IIoT ecosystem. Full article
(This article belongs to the Special Issue Computational Intelligence in Wireless Sensor Networks and IoT)
Show Figures

Figure 1

Back to TopTop