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Mathematics
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Artificial Intelligence and Optimization Techniques for Wireless Sensor Networks
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Artificial Intelligence and Optimization Techniques for Wireless Sensor Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 404

Special Issue Editors

Dr. Rubén Ferrero-Guillén

E-Mail Website
Guest Editor
Department of Mechanical, Computer and Aerospace Engineering, University of León, 24071 León, Spain
Interests: wireless sensor networks; design optimization; artificial intelligence
Dr. Alberto Martínez-Gutiérrez

E-Mail Website
Guest Editor
Department of Mechanical, Computer and Aerospace Engineering, University of León, 24071 León, Spain
Interests: industrial engineering; simulation; IoT; CPS
Dr. Javier Díez-González

E-Mail Website
Guest Editor
Department of Mechanical, Computer and Aerospace Engineering, University of Leon, 24071 Leon, Spain
Interests: wireless sensor networks; artificial intelligence; evolutionary computation; algorithms; Industry 4.0; manufacturing; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless Sensor Networks (WSNs) are an integral aspect of modern technology, with applications ranging from environmental monitoring to industrial automation. However, the increasing complexity and demands of these networks require their performance, scalability and adaptability to be enhanced via advanced solutions, with artificial intelligence and optimization algorithms emerging as key technologies in this field.

This Special Issue seeks contributions that delve into this topic of research, presenting both theoretical advancements and the practical implementation of advanced mathematical tools for WSNs. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Sensor deployment and coverage optimization
  • Sensor fusion and data aggregation techniques
  • Advanced target tracking and localization algorithms
  • Predictive maintenance and anomaly detection
  • AI-based fault detection and network security
  • Advanced sensor calibration methods
  • Optimization algorithms for network management
  • Energy-efficient routing protocols and resource allocation
  • Methods addressing scalability and performance under constrained environments
  • Edge computing and real-time processing in WSN
  • Resource and information management
  • Performance analysis and simulations of optimized WSNs

This Special Issue provide a platform for researchers and practitioners to address these research issues, fostering the development of impactful, AI-driven solutions in WSNs.

Dr. Rubén Ferrero-Guillén
Dr. Alberto Martínez-Gutiérrez
Dr. Javier Díez-González
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. Mathematics 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

  • wireless sensor networks
  • sensor deployment and coverage
  • target tracking and localization
  • sensor fusion and data aggregation
  • sensor calibration
  • fault detection and predictive maintenance
  • energy efficiency and network management
  • scalability of WSN

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Published Papers (1 paper)

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Research

45 pages, 1534 KB  
Article
Accurate and Scalable DV-Hop-Based WSN Localization with Parameter-Free Fire Hawk Optimizer
by Doğan Yıldız
Mathematics 2025, 13(20), 3246; https://doi.org/10.3390/math13203246 - 10 Oct 2025
Abstract
Wireless Sensor Networks (WSNs) have emerged as a foundational technology for monitoring and data collection in diverse domains such as environmental sensing, smart agriculture, and industrial automation. Precise node localization plays a vital role in WSNs, enabling effective data interpretation, reliable routing, and [...] Read more.
Wireless Sensor Networks (WSNs) have emerged as a foundational technology for monitoring and data collection in diverse domains such as environmental sensing, smart agriculture, and industrial automation. Precise node localization plays a vital role in WSNs, enabling effective data interpretation, reliable routing, and spatial context awareness. The challenge intensifies in range-free settings, where a lack of direct distance data demands efficient indirect estimation methods, particularly in large-scale, energy-constrained deployments. This work proposes a hybrid localization framework that integrates the distance vector-hop (DV-Hop) range-free localization algorithm with the Fire Hawk Optimizer (FHO), a nature-inspired metaheuristic method inspired by the predatory behavior of fire hawks. The proposed FHODV-Hop method enhances location estimation accuracy while maintaining low computational overhead by inserting the FHO into the third stage of the DV-Hop algorithm. Extensive simulations are conducted on multiple topologies, including random, circular, square-grid, and S-shaped, under various network parameters such as node densities, anchor rates, population sizes, and communication ranges. The results show that the proposed FHODV-Hop model achieves competitive performance in Average Localization Error (ALE), localization ratio, convergence behavior, computational, and runtime efficiency. Specifically, FHODV-Hop reduces the ALE by up to 35% in random deployments, 25% in circular networks, and nearly 45% in structured square-grid layouts compared to the classical DV-Hop. Even under highly irregular S-shaped conditions, the algorithm achieves around 20% improvement. Furthermore, convergence speed is accelerated by approximately 25%, and computational time is reduced by nearly 18%, demonstrating its scalability and practical applicability. Therefore, these results demonstrate that the proposed model offers a promising balance between accuracy and practicality for real-world WSN deployments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization Techniques for Wireless Sensor Networks)
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