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Multi-Agent Sensors Systems and Their Applications
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Multi-Agent Sensors Systems and Their Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 5583

Special Issue Editor

Dr. Salman Jalalifar

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney 2109, Australia
Interests: CFD; process optimisation; Pyrolysis; heat transfer; biosensors

Special Issue Information

Dear Colleagues,

The evolution of multi-agent sensor systems represents a paradigm shift in sensor network design, enabling collaborative and intelligent sensing capabilities across distributed environments. These systems, composed of multiple autonomous sensor nodes that can communicate, cooperate, and adapt to dynamic conditions, possess the potential to revolutionize various applications ranging from environmental monitoring and smart cities to industrial automation and healthcare.

This Special Issue aims to explore the latest developments and applications of multi-agent sensor systems. topics of interest include, but are not limited to, the following:

  • Design and optimization of multi-agent sensor networks;
  • Collaborative sensing and information fusion techniques;
  • Applications of multi-agent sensor systems in IoT, smart environments, and industrial automation;
  • Energy-efficient protocols and algorithms for multi-agent sensor networks;
  • Security and privacy considerations in multi-agent sensor systems;
  • Machine learning and AI approaches for enhancing multi-agent sensor system performance;
  • Case studies and real-world deployments of multi-agent sensor systems.

Dr. Salman Jalalifar
Guest Editor

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. Sensors 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

  • multi-agent sensor systems
  • collaborative sensing
  • information fusion
  • industrial automation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

34 pages, 3406 KB  
Article
Reconstructing Spatial Localization Error Maps via Physics-Informed Tensor Completion for Passive Sensor Systems
by Zhaohang Zhang, Zhen Huang, Chunzhe Wang and Qiaowen Jiang
Sensors 2026, 26(2), 597; https://doi.org/10.3390/s26020597 - 15 Jan 2026
Viewed by 537
Abstract
Accurate mapping of localization error distribution is essential for assessing passive sensor systems and guiding sensor placement. However, conventional analytical methods like the Geometrical Dilution of Precision (GDOP) rely on idealized error models, failing to capture the complex, heterogeneous error distributions typical of [...] Read more.
Accurate mapping of localization error distribution is essential for assessing passive sensor systems and guiding sensor placement. However, conventional analytical methods like the Geometrical Dilution of Precision (GDOP) rely on idealized error models, failing to capture the complex, heterogeneous error distributions typical of real-world environments. To overcome this challenge, we propose a novel data-driven framework that reconstructs high-fidelity localization error maps from sparse observations in TDOA-based systems. Specifically, we model the error distribution as a tensor and formulate the reconstruction as a tensor completion problem. A key innovation is our physics-informed regularization strategy, which incorporates prior knowledge from the analytical error covariance matrix into the tensor factorization process. This allows for robust recovery of the complete error map even from highly incomplete data. Experiments on a real-world dataset validate the superiority of our approach, showing an accuracy improvement of at least 27.96% over state-of-the-art methods. Full article
(This article belongs to the Special Issue Multi-Agent Sensors Systems and Their Applications)
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32 pages, 4040 KB  
Article
Self-Supervised WiFi-Based Identity Recognition in Multi-User Smart Environments
by Hamada Rizk and Ahmed Elmogy
Sensors 2025, 25(10), 3108; https://doi.org/10.3390/s25103108 - 14 May 2025
Cited by 6 | Viewed by 3049
Abstract
The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective in capturing spatial and contextual information, often face challenges related to high deployment costs, privacy concerns, and susceptibility to [...] Read more.
The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective in capturing spatial and contextual information, often face challenges related to high deployment costs, privacy concerns, and susceptibility to environmental variations. To address these limitations, we propose IdentiFi, a novel AI-driven human identification system that leverages WiFi-based wireless sensing and contrastive learning techniques. IdentiFi utilizes self-supervised and semi-supervised learning to extract robust, identity-specific representations from Channel State Information (CSI) data, effectively distinguishing between individuals even in dynamic, multi-occupant settings. The system’s temporal and contextual contrasting modules enhance its ability to model human motion and reduce multi-user interference, while class-aware contrastive learning minimizes the need for extensive labeled datasets. Extensive evaluations demonstrate that IdentiFi outperforms existing methods in terms of scalability, adaptability, and privacy preservation, making it highly suitable for AI agents in smart homes, healthcare facilities, security systems, and personalized services. Full article
(This article belongs to the Special Issue Multi-Agent Sensors Systems and Their Applications)
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21 pages, 8079 KB  
Article
Adaptive Communication Model for QoS in Vehicular IoT Systems Using CTMC
by Adeel Iqbal, Tahir Khurshaid, Ali Nauman and Sung Won Kim
Sensors 2025, 25(6), 1818; https://doi.org/10.3390/s25061818 - 14 Mar 2025
Cited by 4 | Viewed by 1208
Abstract
Vehicular Internet of Things (V-IoT) systems will be critical in advancing intelligent transportation networks because of the easy communication they make possible between vehicles, roadside infrastructure, and other network entities. Integrating adaptive IoT-based communication models will increase resource utilization and allow multiple communications [...] Read more.
Vehicular Internet of Things (V-IoT) systems will be critical in advancing intelligent transportation networks because of the easy communication they make possible between vehicles, roadside infrastructure, and other network entities. Integrating adaptive IoT-based communication models will increase resource utilization and allow multiple communications in vehicular networks. This work proposes an Adaptive Multi-mode Spectrum Access (AMSA) approach for optimal Quality of Service (QoS) in multi-class V-IoT networks. Unlike traditional static spectrum access methods, AMSA switches between interweave, underlay, and coexistence modes based on network conditions. Our results indicate that AMSA improves spectrum usage by 56% over static spectrum access improves throughput by 110%, and reduces delay for low-priority traffic by up to 47.5%. This new integration offers robust vehicular communication with optimal resource allocation under different network scenarios. Full article
(This article belongs to the Special Issue Multi-Agent Sensors Systems and Their Applications)
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