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Electronics
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Advances in Wireless Sensor Networks
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Advances in Wireless Sensor Networks

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

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 6476

Special Issue Editors

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
Prof. Dr. Ilias K. Savvas

E-Mail Website
Guest Editor
Department of Digital Systems, Faculty of Technology, University of Thessaly, 415000 Larissa, Greece
Interests: quantum computing (QML, QKD, post-quantum cryptography); parallel and distributed systems; computational clouds
Special Issues, Collections and Topics in MDPI journals
Dr. Costas Chaikalis

E-Mail Website
Guest Editor
Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
Interests: mobile communications; forward error correction coding; reconfigurable (software radio) architectures; cross-layer architectures; V2V applications
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitrios Kosmanos

E-Mail Website
Guest Editor
1. Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
2. Department of Electrical & Computer Engineering, University of Thessaly, 38334 Volos, Greece
Interests: security and privacy in wireless communications; vehicular ad-hoc networks (VANETs); estimation techniques for physical layers; error detection and correction techniques in physical layers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main goal of this Special Issue is to offer researchers the opportunity to spread their research works on advances related to the applications and technologies of wireless sensor networks for several application domains such as: precision agriculture, smart grid, smart cities, Industry 4.0, VANETs, 5G and beyond, fog networks, etc. The Special Issue will highlight the state-of-the-art algorithms, cross-layer optimization techniques, architectures and frameworks for next-generation WSNs, networking devices, IoT, fog, and pervasive computing environments.

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

  • Wireless sensor networks and their applications
  • Ad hoc and mesh networks
  • Wireless traffic and routing
  • Cross layer optimization
  • Wireless multimedia communications
  • IoT communication protocols (ZigBee, BLE, NFC, MQTT, XMPP)
  • Network performance, QoS techniques and reliability
  • Energy efficient routing in WNSs, IoT and fog networks
  • Topology control in WSN, IoT and fog networks
  • Cognitive sensors and radio networks
  • VANETs
  • Error correction codes
  • Topology Control (2D and 3D)
  • Underwater WSNs
  • Security issues and algorithms
  • Monitoring and control in wireless networks
  • Resource allocation techniques
  • Measurements and transmission in wireless networks
  • Intelligent vehicular systems
  • Wireless edge computing and communications
  • Channel modelling and propagation
  • Wireless system architectures and protocols
  • Error control, detection and estimation
  • 5G communications
  • Signal processing algorithms and techniques
  • Applications in precision agriculture, smart cities, connected vehicles, drones, etc.

Dr. Apostolos Xenakis
Prof. Dr. Ilias K. Savvas
Dr. Costas Chaikalis
Dr. Kosmanos Dimitrios
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

  • wireless sensor networks
  • efficient communications
  • cross layer optimization
  • topology control and routing
  • signal processing and communications
  • applications

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Published Papers (4 papers)

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Research

19 pages, 7045 KiB  
Article
Application of Gray Wolf Particle Filter Algorithm Based on Golden Section in Wireless Sensor Network Mobile Target Tracking
by Duo Peng, Kun Xie and Mingshuo Liu
Electronics 2024, 13(13), 2440; https://doi.org/10.3390/electronics13132440 - 21 Jun 2024
Cited by 3 | Viewed by 886
Abstract
In order to address the issue of low tracking accuracy caused by particle depletion in the particle filter, a mobile target tracking algorithm tailored for wireless sensor networks (WSNs) is presented. This algorithm, based on the golden-section gray wolf particle filter (PF), represents [...] Read more.
In order to address the issue of low tracking accuracy caused by particle depletion in the particle filter, a mobile target tracking algorithm tailored for wireless sensor networks (WSNs) is presented. This algorithm, based on the golden-section gray wolf particle filter (PF), represents a novel approach to target tracking. The algorithm’s originality lies in its ability to guide the particle swarm toward regions of higher weights, thereby striking a balance between global and local exploration capabilities. This not only alleviates issues related to sample depletion and local extrema but also enhances the diversity of the particle swarm, significantly improving tracking performance. To assess the effectiveness of the proposed algorithm, a series of simulation experiments were conducted, comparing it with the extended Kalman filter (EKF) and the standard PF algorithm. The experiments employed a constant velocity circular motion model (CM) for filtering and tracking. The root mean square error metric demonstrated a significant reduction in error of 57% and 37% in comparison to the extended Kalman filter (EKF) and the particle filter (PF), respectively. This serves to illustrate the superiority of our method in enhancing tracking accuracy. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks)
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27 pages, 3989 KiB  
Article
QWLCPM: A Method for QoS-Aware Forwarding and Caching Using Simple Weighted Linear Combination and Proximity for Named Data Vehicular Sensor Network
by Dependra Dhakal and Kalpana Sharma
Electronics 2024, 13(7), 1183; https://doi.org/10.3390/electronics13071183 - 23 Mar 2024
Cited by 1 | Viewed by 1077
Abstract
The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is [...] Read more.
The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is a mobile ad hoc network that uses vehicles equipped with sensors to collect and disseminate data. QoS is critical in vehicular networks, as the data transmission must be reliable, efficient, and timely to support various applications. This paper proposes a QoS-aware forwarding and caching algorithm for NDVSNs, called QWLCPM (QoS-aware Forwarding and Caching using Weighted Linear Combination and Proximity Method). QWLCPM utilizes a weighted linear combination and proximity method to determine stable nodes and the best next-hop forwarding path based on various metrics, including priority, signal strength, vehicle speed, global positioning system data, and vehicle ID. Additionally, it incorporates a weighted linear combination method for the caching mechanism to store frequently accessed data based on zone ID, stability, and priority. The performance of QWLCPM is evaluated through simulations and compared with other forwarding and caching algorithms. QWLCPM’s efficacy stems from its holistic decision-making process, incorporating spatial and temporal elements for efficient cache management. Zone-based caching, showcased in different scenarios, enhances content delivery by utilizing stable nodes. QWLCPM’s proximity considerations significantly improve cache hits, reduce delay, and optimize hop count, especially in scenarios with sparse traffic. Additionally, its priority-based caching mechanism enhances hit ratios and content diversity, emphasizing QWLCPM’s substantial network-improvement potential in vehicular environments. These findings suggest that QWLCPM has the potential to greatly enhance QoS in NDVSNs and serve as a promising solution for future vehicular sensor networks. Future research could focus on refining the details of its implementation, scalability in larger networks, and conducting real-world trials to validate its performance under dynamic conditions. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks)
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16 pages, 508 KiB  
Article
Scheduling Precedence Constraints among Charging Tasks in Wireless Rechargeable Sensor Networks
by Lanlan Li, Haipeng Dai, Chen Chen, Zilu Ni and Shihao Li
Electronics 2024, 13(2), 346; https://doi.org/10.3390/electronics13020346 - 13 Jan 2024
Viewed by 1484
Abstract
The development of wireless power transfer (WPT) facilitates wireless rechargeable sensor networks (WRSNs) receiving considerable attention in the sensor network research community. Most existing works mainly focus on general charging patterns and metrics while overlooking the precedence constraints among tasks, resulting in charging [...] Read more.
The development of wireless power transfer (WPT) facilitates wireless rechargeable sensor networks (WRSNs) receiving considerable attention in the sensor network research community. Most existing works mainly focus on general charging patterns and metrics while overlooking the precedence constraints among tasks, resulting in charging inefficiency. In this paper, we are the first to advance the issue of scheduling wireless charging tasks with precedence constraints (SCPC), with the optimization objective of minimizing the completion time of all the charging tasks under the precedence constraints while guaranteeing that the energy capacity of the mobile charger (MC) is not exhausted and the deadlines of charging tasks are not exceeded. In order to address this problem, we first propose a priority-based topological sort scheme to derive a unique feasible sequence on a directed acyclic graph (DAG). Then, we combine the proposed priority-based topological sort scheme with the procedure of a genetic algorithm to obtain the final solution through a series of genetic operators. Finally, we conduct extensive simulations to validate our proposed algorithm under the condition of three different network sizes. The results show that our proposed algorithm outperformed the other comparison algorithms by up to 11.59% in terms of completion time. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks)
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25 pages, 5737 KiB  
Article
Seamless Connections: Harnessing Machine Learning for MAC Optimization in Home Area Networks
by Bilal Muhammad Khan and Muhammad Bilal Kadri
Electronics 2023, 12(19), 4082; https://doi.org/10.3390/electronics12194082 - 29 Sep 2023
Cited by 1 | Viewed by 1672
Abstract
The latest technologies and communication protocols are arousing a keen interest in automation, in which the field of home area networks is the most prominent area to work upon toward solving the issues and challenges faced by wireless home area networks regarding adaptability, [...] Read more.
The latest technologies and communication protocols are arousing a keen interest in automation, in which the field of home area networks is the most prominent area to work upon toward solving the issues and challenges faced by wireless home area networks regarding adaptability, reliability, cost, throughput, efficiency, and scalability. However, managing the immense number of communication devices on the premises of a smart home is a challenging task. Moreover, the Internet of Things (IoT) is an emerging global trend with billions of smart devices to be connected in the near future resulting in a huge amount of diversified data. The continuous expansion of the IoT network causes complications and vulnerabilities due to its dynamic nature and heterogeneous traffic. In the applications of IoT, the wireless sensor network (WSN) plays a major role, and to take benefits from WSN, medium access control (MAC) is the primary protocol to optimize, which helps in allocating resources to a huge number of devices in the smart home environment. Furthermore, artificial intelligence is highly demanded to enhance the efficiency of existing systems and IoT applications. Therefore, the purpose of this research paper is to achieve an optimized medium access control protocol through machine learning. The machine learning classifier, e.g., random forest (RF) and linear regression model, is adopted for predicting the features of home area networks. The proposed technique is helpful and could overcome the demerits of existing protocols in relation to scalability, throughput, access delay, and reliability and help in achieving an autonomous home area network (HAN). Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks)
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