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Wireless Sensor Networks for Localization and Tracking Systems: Technologies
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Wireless Sensor Networks for Localization and Tracking Systems: Technologies

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

Deadline for manuscript submissions: 25 November 2025 | Viewed by 2341

Special Issue Editor

Prof. Dr. Faouzi Derbel

E-Mail Website
Guest Editor
Faculty of Engineering, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, Germany
Interests: wireless sensor networks; IoT; smart grids; smart metering; smart diagnostic; predictive maintenace
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) have become a key technology for the development of advanced localization and tracking systems, offering significant improvements in accuracy, scalability and cost-efficiency.

This Special Issue focuses on the latest advances in WSN technologies related to localization and tracking systems. It covers a wide range of topics, including the development and deployment of novel sensor nodes, energy-efficient algorithms and protocols for accurate positioning and real-time tracking. The Issue also explores the integration of WSNs with other technologies such as the Internet of Things (IoT), machine learning and edge computing to improve system capabilities and robustness. It also addresses the challenges related to sensor data fusion, network optimization, security and privacy. By showcasing cutting-edge research and innovative applications, this Special Issue aims to provide a comprehensive overview of current trends and future directions in the use of WSNs for localization and tracking, highlighting their role in various fields.

Prof. Dr. Faouzi Derbel
Guest Editor

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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

  • WSN
  • localization
  • tracking
  • energy-efficient algorithms
  • scalability

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Published Papers (3 papers)

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Research

20 pages, 1434 KiB  
Article
Secure Fusion with Labeled Multi-Bernoulli Filter for Multisensor Multitarget Tracking Against False Data Injection Attacks
by Yihua Yu and Yuan Liang
Sensors 2025, 25(11), 3526; https://doi.org/10.3390/s25113526 - 3 Jun 2025
Viewed by 161
Abstract
This paper addresses multisensor multitarget tracking where the sensor network can potentially be compromised by false data injection (FDI) attacks. The existence of the targets is not known and time-varying. A tracking algorithm is proposed that can detect the possible FDI attacks over [...] Read more.
This paper addresses multisensor multitarget tracking where the sensor network can potentially be compromised by false data injection (FDI) attacks. The existence of the targets is not known and time-varying. A tracking algorithm is proposed that can detect the possible FDI attacks over the networks. First, a local estimate is generated from the measurements of each sensor based on the labeled multi-Bernoulli (LMB) filter. Then, a detection method for FDI attacks is derived based on the Kullback–Leibler divergence (KLD) between LMB random finite set (RFS) densities. The statistical characteristics of the KLD are analyzed when the measurements are secure or compromised by FDI attacks, from which the value of the threshold is selected. Finally, the global estimate is obtained by minimizing the weighted sum of the information gains from all secure local estimates to itself. A set of suitable weight parameters is selected for the information fusion of LMB densities. An efficient Gaussian implementation of the proposed algorithm is also presented for the linear Gaussian state evolution and measurement model. Experimental results illustrate that the proposed algorithm can provide reliable tracking performance against the FDI attacks. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Localization and Tracking Systems: Technologies)
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17 pages, 658 KiB  
Article
A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
by Weizhong Ding, Lincan Li and Shengming Chang
Sensors 2025, 25(7), 2028; https://doi.org/10.3390/s25072028 - 24 Mar 2025
Cited by 1 | Viewed by 325
Abstract
Accurate and reliable localization is crucial for various wireless communication applications. A multitude of studies have presented accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different [...] Read more.
Accurate and reliable localization is crucial for various wireless communication applications. A multitude of studies have presented accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different anchor nodes, which may not accurately reflect real-world scenarios with varying noise distributions. In this paper, we propose a simple and efficient localization method based on hybrid RSS-AOA measurements that accounts for the varying measurement noises of different anchor nodes. We develop a closed-form estimator for the target location employing the linear-weighted least squares (LWLS) algorithm, where the weight of each LWLS equation is the inverse of its residual variance. Due to the unknown variances of LWLS equation residuals, we employ a two-stage LWLS method for estimation. The proposed method is computationally efficient, adaptable to different types of wireless communication systems and environments, and provides more accurate and reliable localization results compared to existing RSS-AOA localization techniques. Additionally, we derive the Cramer–Rao lower bound (CRLB) for the RSS-AOA signal sequences used in the proposed method. Simulation results demonstrate the superiority of the proposed method. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Localization and Tracking Systems: Technologies)
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24 pages, 522 KiB  
Article
Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models
by Salwa Sahnoun, Rihab Souissi, Sirine Chiboub, Aziza Chabchoub, Mohamed Khalil Baazaoui, Ahmed Fakhfakh and Faouzi Derbel
Sensors 2025, 25(2), 475; https://doi.org/10.3390/s25020475 - 15 Jan 2025
Viewed by 1530
Abstract
This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a [...] Read more.
This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models. We evaluate these models in terms of localization accuracy and prediction time. The RNN model shows the best performance, achieving a localization error of 0.247 m with a delay of 0.077 s per position location for an area of 12 m × 9.5 m using four anchors. This research highlights the importance of selecting AI models for effective mobile tracking according to test and validation data. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Localization and Tracking Systems: Technologies)
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