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Algorithms
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Energy-Efficient Algorithms for Large-Scale Wireless Sensor Networks
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Energy-Efficient Algorithms for Large-Scale Wireless Sensor Networks

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1293

Special Issue Editors

Prof. Dr. Chang Wu Yu

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Guest Editor
Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu City 300, Taiwan
Interests: theoretical and fundamental problems in wireless sensor networks; algorithms in wireless sensor networks; graph algorithms
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rei Heng Cheng

E-Mail
Guest Editor
School of Information Engineering, Xiamen Ocean Vocational College, Xiamen 361100, China
Interests: wireless rechargeable sensor networks; deep learning application; routing protocol in wireless sensor networks

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) pose numerous practical and theoretical challenges that remain partially unexplored. Traditional techniques often fall short in addressing these issues efficiently and effectively due to several inherent limitations, including constrained energy and computational resources, unpredictable sensor failures, channel impairments, node mobility, untrusted or hostile deployment environments, and susceptibility to external attacks. These vulnerabilities make WSNs significantly more fragile compared to other wireless and wired networks. Designing energy-efficient algorithms and developing robust theoretical frameworks for large-scale WSNs remains a major challenge, particularly when aiming to minimize energy consumption without compromising performance. For example, artificial intelligence (AI) algorithms have emerged as promising solutions for overcoming these difficulties and enhancing the scalability, resilience, and efficiency of WSNs. However, many algorithmic and theoretical issues specific to large-scale WSNs have yet to be fully addressed. Therefore, the main objective of this Special Issue is to foster a deeper understanding of innovative algorithms and theoretical advancements that support the development and optimization of large-scale WSNs.

Prof. Dr. Chang Wu Yu
Prof. Dr. Rei Heng Cheng
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 250 words) can be sent to the Editorial Office for assessment.

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 1800 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
  • wireless charging algorithm design for large-scale wireless sensor networks
  • energy-efficient algorithms and theories for large-scale wireless sensor networks
  • energy-efficient data structures for large-scale wireless sensor networks
  • energy-efficient protocols for large-scale wireless sensor networks
  • mathematical models for energy consumption for large-scale wireless sensor networks
  • energy optimization problems for large-scale wireless sensor networks
  • performance evaluations for large-scale wireless sensor networks
  • wireless charging system designs for large-scale wireless sensor networks
  • simulations for large-scale wireless sensor networks

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Published Papers (1 paper)

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Research

35 pages, 1965 KB  
Article
Efficient Recurrent Multi-Layer Neural Network for Multi-Scale Noise and Activity Drift Mitigation in Wideband Cognitive Radio Networks
by Sunil Jatti and Anshul Tyagi
Algorithms 2026, 19(3), 172; https://doi.org/10.3390/a19030172 - 25 Feb 2026
Abstract
Wideband spectrum sensing in Cognitive Radio Networks (CRNs) is challenging due to sparse primary user (PU) activity and noise clustering, which obscure signals and generate false alarms. Hence, a novel “Graph Discrete Wavelet Bayesian Kernel Boosted Decision Self-Attention Clustering Neural Network (GDWB-KBSC-NN)” is [...] Read more.
Wideband spectrum sensing in Cognitive Radio Networks (CRNs) is challenging due to sparse primary user (PU) activity and noise clustering, which obscure signals and generate false alarms. Hence, a novel “Graph Discrete Wavelet Bayesian Kernel Boosted Decision Self-Attention Clustering Neural Network (GDWB-KBSC-NN)” is proposed. When sparse PU activity is masked by irregular interference bursts, traditional sensing algorithms misclassify weak transmissions as noise, leading to low detection reliability. To resolve this, the first hidden layer employs Discrete Wavelet Sparse Bayesian Kernel Analysis (DW-SBK), integrating Discrete Wavelet Packet Transform (DWPT), Sparse Bayesian Learning (SBL), and Kernel PCA. This restores the true sparse pattern of the spectrum, separates interference from actual PU signals, and enhances detection of weak channels. Additionally, PU signals are fragmented due to cross-scale activity drift, where dynamic bandwidth switching and variable burst durations disrupt temporal continuity. Therefore, the second layer incorporates Gradient Boosted Multi-Head Fuzzy Clustering (GB-MHFC), where Gradient Boosted Decision Trees (GBDT) model nonlinear spectral–temporal patterns, Multi-Head Self-Attention (MHSA) captures long- and short-range temporal dependencies, and Fuzzy C-Means Clustering (FCM) groups feature representations into stable PU activity modes, thereby reducing misclassifications and enhancing robustness under highly dynamic CRN conditions. The proposed method demonstrates superior performance with a maximum detection probability of 0.98, classification accuracy of 98%, lowest sensing error of 5.412%, and the fastest sensing time of 3.65 s. Full article
(This article belongs to the Special Issue Energy-Efficient Algorithms for Large-Scale Wireless Sensor Networks)
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