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Collaborative Integrated Sensing and Localization in Autonomous Systems
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Collaborative Integrated Sensing and Localization in Autonomous Systems

A Special Issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708) belonging to the section "Actuators, Sensors and Devices".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2661

Editors

Dr. Tanveer Ahmad

E-Mail Website
Guest Editor
Department of Computer Science, University of Cyprus, Nicosia, Cyprus
Interests: IoT; smart cities; congestion; data dissemination model; vehicular communication; request processing
Prof. Dr. Xuejun Li

E-Mail Website
Guest Editor
School of Engineering, Computer and Mathematical Sciences (SECMS), Faculty of Design and Creative Technologies (DCT), Auckland University of Technology (AUT), Auckland, New Zealand
Interests: antenna design; sensor localization; healthcare and digital transformation
Dr. Muhammad Usman Hadi

E-Mail Website
Guest Editor
School of Engineering, Ulster University, Belfast BT15 1ED, UK
Interests: machine learning for IoT; optical communication; wireless communication; UAV/drones; 5G and beyond 5G
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous systems, including ground vehicles, drones, marine robots, and industrial machines, significantly rely on precise sensing and localization to operate effectively within intricate and dynamic environments. Recent advancements in sensor fusion, edge intelligence, and wireless communication allow these systems to work together and share situational awareness in real time. This Special Issue highlights the most recent methods and technologies that facilitate collaborative sensing and localization among autonomous platforms. It will unite innovations that enhance the accuracy, scalability, robustness, and efficiency of localization in multi-agent and heterogeneous settings.

We invite contributions on topics such as the following:

  • Multi-sensor fusion techniques for real-time localization;
  • Cooperative SLAM (simultaneous localization and mapping) among vehicles and drones;
  • V2V (vehicle-to-vehicle), D2D (drone-to-drone), and hybrid communication frameworks;
  • Edge and distributed computing for localization and mapping;
  • AI-driven context-aware sensing in dynamic environments;
  • Localization in GPS-denied or signal-degraded conditions;
  • Experimental validation in urban, aerial, underwater, or indoor environments.

This Special Issue will serve as a reference point for researchers, developers, and practitioners working on next-generation autonomous systems that rely on collaboration and intelligent localization strategies.

Dr. Tanveer Ahmad
Prof. Dr. Xuejun Li
Dr. Muhammad Usman Hadi
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Sensor and Actuator Networks 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 2000 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

  • drones
  • UAV
  • autonomous systems
  • localization
  • vehicle
  • edge computing
  • distributed computing
  • GPS
  • AI

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

Published Papers (3 papers)

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20 pages, 5840 KB  
Article
Data-Driven Inversion Method for Human Body Electrostatic Potential from Noncontact Measurements
by Menghua Man, Bo Wu, Yazhou Chen, Guilei Ma, Erwei Cheng and Tianzhu Cui
J. Sens. Actuator Netw. 2026, 15(5), 73; https://doi.org/10.3390/jsan15050073 - 7 Sep 2026
Abstract
Human body static electricity is one of the major hazards in electrostatic-sensitive industrial environments. Existing noncontact measurement methods often rely on explicit physical modeling and generally require accurate prior knowledge of the sensor layout, target motion state, and scene geometry, which limits their [...] Read more.
Human body static electricity is one of the major hazards in electrostatic-sensitive industrial environments. Existing noncontact measurement methods often rely on explicit physical modeling and generally require accurate prior knowledge of the sensor layout, target motion state, and scene geometry, which limits their applicability in complex dynamic scenarios. To address this issue, this paper proposes a neural network-based data-driven method for estimating human body electrostatic potential from noncontact electrostatic measurements. The proposed method uses four-channel noncontact electrostatic sensor signals as inputs and the synchronously measured reference body potential as the target output. Sixteen neural network architectures, including recurrent neural networks, convolutional neural networks, attention-based networks, and hybrid models, are systematically evaluated. The Gray Wolf Optimizer is further used to optimize key hyperparameters of each model. A 5 m × 5 m experimental scene is established, and 36 groups of synchronized time-series signals are collected from three subjects under two motion states. Training and validation datasets are constructed using a sliding time-window method, and the effects of model architecture, window length, and subject–motion condition on inversion performance are analyzed. The results show that the proposed method can effectively estimate human body electrostatic potential from noncontact measurements. Among the evaluated models, Trans-LSTM achieves the best performance. With a window length of 1 s, it obtains a validation normalized root-mean-square error of approximately 0.139 and a coefficient of determination of approximately 0.72. Compared with a representative existing method under the same coverage area and sensor layout, the proposed approach reduces the NRMSE from 0.22 to 0.139. These results demonstrate that the proposed method provides a feasible approach for remote, real-time, and noncontact monitoring of human body electrostatic potential in complex environments. Full article
(This article belongs to the Special Issue Collaborative Integrated Sensing and Localization in Autonomous Systems)
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16 pages, 2550 KB  
Article
Enhancing Robustness to Device Heterogeneity in WiFi-Based Indoor Localization
by Adrián García, Jorge Beltrán, Noelia Hernández, Ignacio Parra and Euntai Kim
J. Sens. Actuator Netw. 2026, 15(4), 49; https://doi.org/10.3390/jsan15040049 - 27 Jun 2026
Viewed by 727
Abstract
Indoor localization systems based on WiFi are gaining popularity due to their low implementation cost and the widespread availability of WiFi infrastructure. However, the wide variety of existing hardware poses a significant challenge in developing systems that maintain robust and consistent performance regardless [...] Read more.
Indoor localization systems based on WiFi are gaining popularity due to their low implementation cost and the widespread availability of WiFi infrastructure. However, the wide variety of existing hardware poses a significant challenge in developing systems that maintain robust and consistent performance regardless of the device used. Recent research has addressed this issue of device heterogeneity by building datasets that include data from a diverse set of devices. In this paper, we tackle this challenge by presenting a novel, multi-device, WiFi Received Signal Strength dataset collected along unconstrained trajectories using nine Android devices over a three-month period with precise ground truth positions obtained using Simultaneous Localization And Mapping. We then study the effect of heterogeneity in the localization performance using an LSTM-based neural network that leverages the temporal nature of sequential WiFi scans, and introduce two mitigation strategies: per-device Received Signal Strength normalization and the incorporation of temporal features as additional input. Our results show that these methods significantly improve cross-device performance with a mean average localization error reduction of 56% and enable generalization to previously unseen hardware with a mean average localization error 8% higher for the unseen devices. Full article
(This article belongs to the Special Issue Collaborative Integrated Sensing and Localization in Autonomous Systems)
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28 pages, 2970 KB  
Article
UGV Path Optimization in UAV-Assisted Environments Using Visibility-Aware Path Simplification
by Isuru Munasinghe, Asanka Perera, Sreenatha Anavatti and Matt Garratt
J. Sens. Actuator Netw. 2026, 15(3), 41; https://doi.org/10.3390/jsan15030041 - 22 May 2026
Viewed by 1213
Abstract
This study proposes a modular path optimization framework for uncrewed ground vehicles (UGVs) in uncrewed aerial vehicle (UAV)-assisted navigation environments to improve the efficiency, smoothness, and executability of paths generated by classical grid-based path planning algorithms. The principal innovation of this work is [...] Read more.
This study proposes a modular path optimization framework for uncrewed ground vehicles (UGVs) in uncrewed aerial vehicle (UAV)-assisted navigation environments to improve the efficiency, smoothness, and executability of paths generated by classical grid-based path planning algorithms. The principal innovation of this work is the Visibility and Line-of-Sight Path Simplification (VLoSPS) algorithm, an algorithm-independent post-processing method that removes redundant waypoints through long-range axis-aligned visibility analysis while preserving path feasibility. VLoSPS is integrated with the Direction-Aware Path Planning Approach (DAPPA) to reduce angular deviations and improve directional continuity. The proposed framework is applicable to standard algorithms, including A*, Dijkstra, Breadth-First Search (BFS), and Depth-First Search (DFS), without modifying their internal search mechanisms. The main academic contributions comprise the formulation of a generalized post-processing architecture for UAV-derived occupancy maps, the introduction of a visibility-aware waypoint reduction strategy, and extensive validation using two synthetic maze datasets and three UAV-derived semantically segmented real-world datasets. On the Göttingen Maze Dataset, the VLoSPS and DAPPA pipeline reduced the average path lengths of A*, Dijkstra, BFS, and DFS by 5.42%, 9.46%, 10.44%, and 86.00%, respectively. The consistent improvements across real-world datasets demonstrate the effectiveness, computational feasibility, and general applicability of the proposed framework for UAV-assisted UGV path planning. The implementation code and benchmark resources developed in this study are publicly released to promote reproducibility and facilitate future research. Full article
(This article belongs to the Special Issue Collaborative Integrated Sensing and Localization in Autonomous Systems)
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