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Electronics
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Topology Control and Optimization for WSN, IoT, and Fog Networks
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Topology Control and Optimization for WSN, IoT, and Fog Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 6111

Special Issue Editors

Dr. Konstantinos Kalovrektis

E-Mail Website
Guest Editor
Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
Interests: physical computing; computational thinking; embedded systems; sensors; digital twin; educational technology; educational robotics; learning machines; remote labs; AR/VR; STE(A)M; IoT; IoE
Special Issues, Collections and Topics in MDPI journals
Dr. Apostolis Xenakis

E-Mail Website
Guest Editor
Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
Interests: wireless sensor networks; networks; wireless communications; cross-layer optimization; quantum communications; security and IoT; Physical Computing; STEM; Robotis
Special Issues, Collections and Topics in MDPI journals
Dr. Peristera Baziana

E-Mail Website
Guest Editor
Department of Computer Science and Telecommunications, University of Thessaly, 35131 Lamia, Greece
Interests: optical communications; optical networks architectures and transmission protocols; analytical modeling and optimization; optical access; fog and cloud networking

Special Issue Information

Dear Colleagues,        

This Special Issue will mainly focus on research works on Topology Control and Optimization issues for WSN, IoT, and Fog networks for several application domains such as smart health, smart grid, precision agriculture, and Industry 4.0. Wireless communication networking is essential for our daily activities, but they have become more complex. This Special Issue will highlight state-of-the-art algorithms, optimization techniques, and architectures for next-generation smart wireless networking devices, embedded devices, and pervasive computing.

The contribution topics of primary interest include, but are not limited to the following:

  • Topology control optimization (2D, 3D)
  • Underwater sensor topology control
  • Energy-efficient communication
  • Energy-efficient routing
  • Wireless sensor network (WSN) technologies and trends
  • IoT architectures and protocols
  • Smart sensors
  • Ad hoc and mesh networks
  • Applications of fog computing and networking
  • Cognitive sensor networks
  • Cognitive fog networks
  • WSN, IoT for Industry 4.0
  • WSN, IoT for smart health
  • WSN, IoT for precision agriculture
  • WSN, IoT for smart grid
  • Monitoring and control in WSN, IoT, and fog networks
  • Edge and fog networking
  • Biomimetic engineering applications

Dr. Apostolos Xenakis
Dr. Konstantinos Kalovrektis
Dr. Peristera Baziana
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. Electronics 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 2400 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

  • WSNs
  • topology control
  • efficient communications
  • fog and edge networking

Published Papers (3 papers)

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Research

19 pages, 1726 KiB  
Article
High Density Sensor Networks Intrusion Detection System for Anomaly Intruders Using the Slime Mould Algorithm
by Mohammed Hasan Alwan, Yousif I. Hammadi, Omar Abdulkareem Mahmood, Ammar Muthanna and Andrey Koucheryavy
Electronics 2022, 11(20), 3332; https://doi.org/10.3390/electronics11203332 - 16 Oct 2022
Cited by 6 | Viewed by 1424
Abstract
The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic routing information communication and an unprotected public media make them easy targets for a wide variety of security [...] Read more.
The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic routing information communication and an unprotected public media make them easy targets for a wide variety of security threats. IDSs are helpful tools that can detect and prevent system vulnerabilities in a network. Unfortunately, there is no possibility to construct advanced protective measures within the basic infrastructure of the WSN. There seem to be a variety of machine learning (ML) approaches that are used to combat the infiltration issues plaguing WSNs. The Slime Mould Algorithm (SMA) is a recently suggested ML approach for optimization problems. Therefore, in this paper, SMA will be integrated into an IDS for WSN for anomaly detection. The SMA’s role is to reduce the number of features in the dataset from 41 to five features. The classification was accomplished by two methods, Support Vector Machine with polynomial core and decision tree. The SMA showed comparable results based on the NSL-KDD dataset, where 99.39%, 0.61%, 99.36%, 99.42%, 99.33%, 0.58%, and 99.34%, corresponding to accuracy, error rate, sensitivity, specificity, precision, false positive rate, and F-measure, respectively, are obtained, which are significantly improved values when compared to other works. Full article
(This article belongs to the Special Issue Topology Control and Optimization for WSN, IoT, and Fog Networks)
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30 pages, 1361 KiB  
Article
A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications
by Ranumayee Sing, Sourav Kumar Bhoi, Niranjan Panigrahi, Kshira Sagar Sahoo, Nz Jhanjhi and Mohammed A. AlZain
Electronics 2022, 11(19), 3207; https://doi.org/10.3390/electronics11193207 - 06 Oct 2022
Cited by 11 | Viewed by 1957
Abstract
Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are [...] Read more.
Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are designed to handle the real-time continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies the task heterogeneity using dynamic fuzzy c-means clustering and buffers into parallel virtual queues according to enhanced least laxity time. The second module is task offloading and optimal resource allocation (TOORA), which decides to offload the task either to cloud or fog and also optimally assigns the resources of fog nodes using the whale optimization algorithm, which provides high throughput. The simulation results of our proposed algorithm, called whale optimized resource allocation (WORA), is compared with results of other models, such as shortest job first (SJF), multi-objective monotone increasing sorting-based (MOMIS) algorithm, and Fuzzy Logic based Real-time Task Scheduling (FLRTS) algorithm. When 100 to 700 tasks are executed in 15 fog nodes, the results show that the WORA algorithm saves 10.3% of the average cost of MOMIS and 21.9% of the average cost of FLRTS. When comparing the energy consumption, WORA consumes 18.5% less than MOMIS and 30.8% less than FLRTS. The WORA also performed 6.4% better than MOMIS and 12.9% better than FLRTS in terms of makespan and 2.6% better than MOMIS and 4.3% better than FLRTS in terms of successful completion of tasks. Full article
(This article belongs to the Special Issue Topology Control and Optimization for WSN, IoT, and Fog Networks)
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23 pages, 3435 KiB  
Article
OGWO-CH: Hybrid Opposition-Based Learning with Gray Wolf Optimization Based Clustering Technique in Wireless Sensor Networks
by Rajakumar Ramalingam, Dinesh Karunanidy, Aravind Balakrishnan, Mamoon Rashid, Ankur Dumka, Ashraf Afifi and Sultan S. Alshamrani
Electronics 2022, 11(16), 2593; https://doi.org/10.3390/electronics11162593 - 18 Aug 2022
Cited by 1 | Viewed by 1244
Abstract
A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs are battery-powered, and the energy drainage is a significant issue. The clustering approach holds an imperative part in boosting the lifespan of WSNs. [...] Read more.
A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs are battery-powered, and the energy drainage is a significant issue. The clustering approach holds an imperative part in boosting the lifespan of WSNs. This approach gathers the sensors into clusters and selects the cluster heads (CHs). CHs accumulate the information from the cluster members and transfer the data to the base station (BS). Yet, the most challenging task is to select the optimal CHs and thereby to enhance the network lifetime. This article introduces an optimal cluster head selection framework using hybrid opposition-based learning with the gray wolf optimization algorithm. The hybrid technique dynamically trades off between the exploitation and exploration search strategies in selecting the best CHs. In addition, the four different metrics such as energy consumption, minimal distance, node centrality and node degree are utilized. This proposed selection mechanism enhances the network efficiency by selecting the optimal CHs. In addition, the proposed algorithm is experimented on MATLAB (2018a) and validated by different performance metrics such as energy, alive nodes, BS position, and packet delivery ratio. The obtained results of the proposed algorithm exhibit better outcome in terms of more alive nodes per round, maximum number of packets delivery to the BS, improved residual energy and enhanced lifetime. At last, the proposed algorithm has achieved a better lifetime of ≈20%, ≈30% and ≈45% compared to grey wolf optimization (GWO), Artificial bee colony (ABC) and Low-energy adaptive clustering hierarchy (LEACH) techniques. Full article
(This article belongs to the Special Issue Topology Control and Optimization for WSN, IoT, and Fog Networks)
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