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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 1718

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

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. 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

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

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Published Papers (2 papers)

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Research

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
Viewed by 215
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 1224
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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