sensors-logo

Journal Browser

Journal Browser

Distributed Sensor Networks: Emerging Technologies, Methods and Applications

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

Deadline for manuscript submissions: 10 June 2026 | Viewed by 1601

Special Issue Editors


E-Mail Website
Guest Editor
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210095, China
Interests: distributed system; system security; database

E-Mail Website
Guest Editor
School of Software, Shandong University, Jinan 250100, China
Interests: information security; data security; AI security

E-Mail Website
Guest Editor
School of Computer Science, Nanjing University of Post and Telecommunication, Nanjing 210042, China
Interests: data management; privacy preservation

Special Issue Information

Dear Colleagues,

Distributed sensor networks (DSNs) have become pivotal in enabling intelligent, large-scale monitoring systems across diverse domains, from smart infrastructure to environmental conservation. As these networks evolve, researchers face challenges in optimizing energy consumption, ensuring robust security, managing massive data flows, and achieving seamless scalability. This Special Issue seeks to consolidate cutting-edge advancements and interdisciplinary approaches that address these challenges while unlocking new possibilities for DSN deployment.

We invite original research articles, comprehensive reviews, and case studies that explore innovative technologies, methodologies, and applications in DSNs. Contributions should emphasize both theoretical rigor and practical relevance, bridging the gap between academic research and real-world implementation.

Potential topics include but are not limited to the following:

  • Low-power hardware designs (e.g., MEMS, bio-inspired sensors);
  • Energy-efficient sensing modalities;
  • Self-organizing network topologies and fault-tolerant architectures;
  • Distributed signal processing algorithms;
  • Mobility-aware data compression and aggregation;
  • Distributed machine learning for edge intelligence;
  • Cyber-physical system resilience against attacks (e.g., DoS, GPS spoofing);
  • Privacy-preserving data sharing in heterogeneous networks;
  • Dynamic clustering for large-scale heterogeneous WSNs;
  • Cross-layer protocols for latency-sensitive applications;
  • Network coding for efficient wireless communication;
  • Fault-tolerant routing strategies;
  • Hybrid architectures integrating wired/wireless/optical links.

Dr. Liang Liu
Dr. Chunpeng Ge
Prof. Dr. Hua Dai
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-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

  • energy efficiency
  • security
  • scalability
  • edge intelligence
  • distributed sensing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 3041 KB  
Article
Machine Learning-Enhanced NDIR Methane Sensing Solution for Robust Outdoor Continuous Monitoring Applications
by Yang Yan, Lkhanaajav Mijiddorj, Tyler Beringer, Bilguunzaya Mijiddorj, Alex Ho and Binbin Weng
Sensors 2025, 25(24), 7691; https://doi.org/10.3390/s25247691 - 18 Dec 2025
Cited by 3 | Viewed by 1051
Abstract
This work presents the development of a low-cost and high-performance multi-sensory gas detection instrument named the AIMNet Sensor, with the integration of a machine learning-based data processing method. The compact and low-power instrument (8.5 × 11.5 cm, 1.4 W) houses the core sensing [...] Read more.
This work presents the development of a low-cost and high-performance multi-sensory gas detection instrument named the AIMNet Sensor, with the integration of a machine learning-based data processing method. The compact and low-power instrument (8.5 × 11.5 cm, 1.4 W) houses the core sensing hardware module, Senseair K96, that integrates both a non-dispersive infrared (NDIR)-based gas sensing unit and a BME280 environmental sensing unit. To address the outdoor operation challenges caused by environmental fluctuation due to the varying temperature, humidity, and pressure, from the software aspect, multiple machine learning-based regression models were trained in this work on 13,125 calibration data points collected under controlled laboratory conditions. Among ten tested algorithms, the Multilayer Perceptron (MLP) and Elastic Net models achieved the highest accuracy, with R-squared coefficient R2>0.8 on both indoor and outdoor scenarios, and with inter-sensor root mean square error (RMSE) within 1.5 ppm across four identical instruments. Moreover, field mobile validation was performed near a wastewater management facility using this solution, confirming a strong correlation with LI-COR reference measurements and a reliable detection of CH4 leaks with concentrations up to 18 ppm at the test site. Overall, this machine learning-integrated NDIR sensing solution (i.e., AIMNet) offers a practical and scalable solution towards a more robust distributed CH4 monitoring network for real-world field-deployable applications. Full article
Show Figures

Figure 1

Back to TopTop