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Feature Papers in the ‘Sensor Networks’ Section 2026

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 3956

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Editors

Prof. Dr. Wei Yi

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Guest Editor

School of Information and Communication Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China
Interests: multi-target tracking; sensor networks; resources management; multi-sensor information fusion
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chase Wu

E-Mail Website
Guest Editor

Department of Data Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: big data; data-intensive computing; parallel and distributed computing; high-performance networking; large-scale scientific visualization; wireless sensor networks; cyber security
Special Issues, Collections and Topics in MDPI journals

Dr. Guoxin Zhang

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Guest Editor Assistant

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: wireless sensor networks; distributed radar systems; integrated sensing and communication

Special Issue Information

Dear Colleagues,

We are pleased to announce that the “Sensor Networks” Section is now compiling a collection of papers submitted by the Section’s Editorial Board Members (EBMs) and outstanding scholars in this research field. We welcome contributions and recommendations from EBMs.

This section covers theoretical and experimental problems, particularly considering the rise of the Internet of things (IoT) applications that allow several devices to connect in a smart way. In general, this section aims to provide researchers with a platform to publish their scientific work that can influence the scientific community as well as the general public.

We would also like to take this opportunity to call on more excellent scholars to join the Sensor Networks Section so that we can work together to further develop this exciting field of research.

Potential topics include, but are not limited to, the following:

Smart sensor networks;
Power consumption/energy-harvesting sensor networks;
Energy autonomous and low-power systems for the IoT;
Machine learning for sensors;
Cross-layer optimization;
Wireless sensor networks;
Routing protocols in sensor networks;
Embedded networked sensors;
Software-defined networks;
Underwater sensor networks;
Distributed sensor networks;
Ad hoc networks;
Industrial sensor networks;
Sensor network security, privacy, and threat detection;
Data calibration and fault tolerance;
Sensor network data fusion and data aggregation;
Sensor node localization;
Medium access control (MAC) protocols for sensor networks;
Artificial intelligence in sensor networks;
Edge computing in wireless sensor networks;
AI/ML for integrated sensing and communication;
Applications of sensor networks in area monitoring, healthcare monitoring, habitat monitoring, environmental/Earth sensing, etc.;
Advanced and intelligent sensor applications.

Prof. Dr. Wei Yi
Prof. Dr. Chase Wu
Guest Editors

Dr. Guoxin Zhang
Guest Editor Assistant

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. 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

sensor networks
wireless sensor networks
sensing and communication
IoT

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Published Papers (7 papers)

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Research

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39 pages, 26289 KB

Open AccessArticle

Argus: A Sparse-Label Machine-Learning Workflow for Passive DAS Seismic Catalogue Expansion in CO₂ Storage Monitoring—Application to the CO2CRC Otway Stage 4 Dataset

by Ilgiz Almukhametov, Olivia Collet, Boris Gurevich, Roman Isaenkov, Pavel Shashkin, Konstantin Tertyshnikov, Mikhail Vorobev, Nepomuk Boitz and Roman Pevzner

Sensors 2026, 26(16), 5084; https://doi.org/10.3390/s26165084 - 11 Aug 2026

Viewed by 441

Abstract

Passive distributed acoustic sensing (DAS) is an attractive tool for monitoring geological CO₂ storage, but its dense, continuous recordings create a data-volume problem: a multi-month, multi-well archive yields enormous numbers of detector triggers, of which confirmed seismic events form a vanishingly small fraction, and conventional supervised classification is ill-posed when labels remain scarce and the negative class undefined because the non-event population is open-ended and spans noise families that vary over time and between wells. We present Argus, a sparse-label machine-learning workflow that converts continuous DAS recordings into a reproducible, auditable catalogue of event candidates. A deterministic front end reduces the archive to comparable trigger objects, each described by a 67-feature interpretable representation of its two-dimensional time–channel character (e.g., duration and channel span, detector-mask morphology, apparent moveout, inter-channel waveform coherence, and spectral shape); a retrieval-first machine-learning layer then ranks these triggers by their similarity, in this interpretable feature space, to a small seed catalogue of independently confirmed events, within an iterative human-in-the-loop process that introduces local supervised noise-rejection gates only for recurrent artefact families once they have been labelled. Applied to the CO2CRC Otway Stage 4 dataset—120 days of recordings on two wells, comprising roughly 23 TB and 14.14 million raw triggers—the workflow expanded a 39-event seed catalogue into 631 analyst-reviewed events, demonstrating complementarity with an independent template-matching analysis: two additional induced-event candidates were recovered, one within the CRC4 template-matching coverage and one on CRC7 during a CRC4 data gap. The induced-event class itself grew only from four to six candidates, and its counts are reported as a reviewed lower bound rather than a complete census. The result is a provenance-preserving, conservatively interpreted event inventory rather than an opaque classifier output, an outcome aligned with the reproducibility and audit requirements of CO₂ storage assurance. Full article

(This article belongs to the Special Issue Feature Papers in the ‘Sensor Networks’ Section 2026)

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36 pages, 3449 KB

Open AccessArticle

Joint Task Offloading and Resource Allocation with Data Caching in UAV-Aided Mobile Edge Computing Networks for Latency-Sensitive Applications

by Tanmay Baidya and Sangman Moh

Sensors 2026, 26(15), 4966; https://doi.org/10.3390/s26154966 - 5 Aug 2026

Viewed by 289

Abstract

The rapid growth of computing-intensive and latency-sensitive applications, including augmented reality, virtual reality, and self-driving systems, has increased the demand for low-latency and energy-efficient processing solutions. Mobile edge computing (MEC) has evolved as a transformative paradigm by relocating computation to the network edge, closer to end users. Unmanned aerial vehicles (UAVs) further strengthen MEC by offering flexible deployment, mobility, and reliable line-of-sight communication, making them suitable for temporary high-demand scenarios. Moreover, such latency-sensitive applications often generate numerous repetitive tasks and, thus, storing the results of these tasks can reduce both communication overhead and computational workload. However, jointly addressing the caching of task-results alongside offloading and resource allocation decisions in UAV-aided MEC networks remains a non-trivial challenge. In this study, an integrated task offloading and resource allocation with data caching (JORC) framework is proposed to address these challenges. The offloading and resource allocation problems are formulated as a Markov decision process and solved using the soft actor–critic reinforcement learning algorithm. In addition, dynamic and adaptive caching manages limited storage and reduces redundant computations by using a hybrid strategy that integrates the least-frequently used and least-recently used policies to reduce computational redundancy. Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches. Full article

(This article belongs to the Special Issue Feature Papers in the ‘Sensor Networks’ Section 2026)

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25 pages, 7249 KB

Open AccessArticle

Adaptive Probabilistic RREQ Rebroadcasting Using Thompson Sampling for Mobile Ad Hoc Sensor Networks

by Dimitra G. Kampitaki and Anastasios A. Economides

Sensors 2026, 26(15), 4870; https://doi.org/10.3390/s26154870 - 2 Aug 2026

Viewed by 381

Abstract

Route discovery in ad hoc on-demand distance vector (AODV)-based mobile ad hoc sensor networks relies on route request (RREQ) dissemination, which improves reachability, but generates redundant rebroadcasts, channel contention, delay, and energy waste. Fixed probabilistic rebroadcasting mitigates broadcast storms, but its performance depends on a manually selected forwarding probability applied uniformly across different local redundancy conditions. This work proposes the Thompson-sampling probabilistic AODV (TSP-AODV), a lightweight adaptive extension, in which each intermediate node selects among a small set of forwarding probability arms using a local duplicate-pressure context and delayed route reply (RREP) feedback. The reward function uses the local observation of a corresponding RREP as delayed feedback while penalising high forwarding probability in locally redundant contexts. TSP-AODV requires no additional control packets, no topology exchange, and only a small number of local Beta belief distribution parameters per node. Evaluated against AODV, fixed probabilistic rebroadcasting, counter-based suppression, and dynamic probabilistic-counter suppression over 1600 simulation runs spanning four node densities and four mobility levels, TSP-AODV achieves the lowest normalised routing overhead and the highest RREQ suppression ratio, while no statistically significant PDR difference relative to the fixed probabilistic baseline was observed under the tested conditions. End-to-end delay is also reduced significantly relative to fixed probabilistic rebroadcasting. The learned belief behaviour confirms context-dependent adaptation, with the dominant forwarding-probability arm decreasing as duplicate pressure increases. An additional 800-run sensitivity analysis characterises the PDR–NRO–delay trade-off across the tested penalty coefficients and feedback-window durations in two representative scenarios. These results are limited to the evaluated parameter grid and do not establish scenario-independent parameter robustness. Full article

(This article belongs to the Special Issue Feature Papers in the ‘Sensor Networks’ Section 2026)

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23 pages, 16319 KB

Open AccessArticle

Optimization of Communication Tasks in an Energy-Efficient Swarm and the Spatial Distribution of Robots

by Amir Ijaz, Hashem Haghbayan, Ethiopia Nigussie and Juha Plosila

Sensors 2026, 26(15), 4742; https://doi.org/10.3390/s26154742 - 26 Jul 2026

Viewed by 241

Abstract

Energy-efficient coordination of robotic swarms requires effective integration of task scheduling, motion planning, and communication management, particularly in resource-constrained environments where computation and wireless communication compete for limited energy resources. Existing multi-robot approaches typically address these concerns in separate stages: task-allocation methods (e.g., market- and auction-based schemes) price assignments by distance, and computation-offloading methods decide execution placement after a route has been fixed. This paper’s specific contribution is to fold the execution-placement decision (local computation versus offloading to a peer) into the edge-relaxation step of an A* path search, using a composite cost whose communication term is derived from the instantaneous neighborhood of each node; routing and compute placement are therefore co-optimized within a single search rather than in decoupled stages. The framework is evaluated in simulation with a swarm of 25 robots against two decoupled baselines: a path-only planner that ignores workload and communication costs, and a workload-only scheduler that ignores travel and communication costs. Across 20 randomized trials, the proposed heuristic reduces total swarm energy consumption by approximately 22% relative to the path-only baseline and 9% relative to the workload-only baseline, shortens average task completion time by roughly 20%, and lowers the load imbalance factor from 6.7 (path-only) and 3.2 (workload-only) to 1.9. We report these gains for the tested configurations and delimit their scope: the search retains the asymptotic complexity of standard A*, but path optimality does not extend to the compute-placement decisions, which are locally greedy, and all results are obtained in simulation rather than on hardware. Full article

(This article belongs to the Special Issue Feature Papers in the ‘Sensor Networks’ Section 2026)

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26 pages, 1911 KB

Open AccessArticle

Topology Control in Spherical 3D Sensor Networks

by Nikolaos Zarifis and Dimitrios Katsaros

Sensors 2026, 26(13), 4085; https://doi.org/10.3390/s26134085 - 27 Jun 2026

Viewed by 403

Abstract

The deployment of three-dimensional Wireless Sensor Networks (3D WSNs) in complex environments demands robust topological control to ensure both reliable and fault-tolerant sensing and communication. In order to simultaneously achieve the two objectives over time, an even distribution of the sensors’ energy consumption is essential. Achieving optimal sensor distribution on non-planar surfaces (3D shapes), such as spheres, while maintaining reliable network routes is a significant algorithmic challenge. While many approaches effectively and efficiently addressed the aforementioned goals in 2D environments, and there exists a significant body of work on coverage, connectivity, or energy efficiency in 3D sensor networks, the solutions for either can not straightforwardly be adapted to the 3D case (e.g., some coverage problems are optimally solved for 2D but are still open problems in the 3D case), or the solutions to the individual problems in the 3D case are not integrated gracefully to solve the entire problem. Moreover, these problems have not been address for the realistic spherical 3D case. This paper presents a novel holistic algorithm designed to generate energy-efficient, optimal sensor topologies over spherical 3D sensor networks that guarantee redundant coverage to deal with sensor failures, connectivity with controlled redundancy support for more efficient communication, and the creation of a hierarchy over the flat network to deal with energy issues, at would be appropriate for real-world tasks. The proposed methodology is executed in three primary phases. First, it approaches the geometric part of the problem to determine the optimal placement of sensor nodes on the surface of a sphere, guaranteeing k-coverage for the target area. Second, it creates a reliable inner-layer backbone network of sensors that establishes k-connectivity ensuring a reliable network for data transmission and distribution of total power in the whole network. Finally, after formulating sensors into clusters, a mathematical formula to change each cluster head is created so that we achieve even distribution of energy consumption across the network. To validate the proposed approach, a 3D WSN software simulator was developed. This tool provides a dynamic visual simulation of the network, enabling the execution, visualization and simulation of the hybrid algorithm and any other 3D WSN. Full article

(This article belongs to the Special Issue Feature Papers in the ‘Sensor Networks’ Section 2026)

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21 pages, 1968 KB

Open AccessArticle

Edge-Friendly UAV Wildfire Smoke and Flame Detection Using Transfer Learning-Enhanced Lightweight Deep Learning Models

by Giovanny Vazquez, Shengjie (Patrick) Zhai and Mei Yang

Sensors 2026, 26(10), 3197; https://doi.org/10.3390/s26103197 - 19 May 2026

Cited by 2 | Viewed by 603

Abstract

Edge computing on unmanned aerial vehicles (UAVs) enables low-latency wildfire monitoring by performing visual inference onboard; however, practical deployment is constrained by limited labeled data and resource budgets that often preclude reliance on large GPU servers. This work investigates transfer learning (TL) for UAV-based wildfire smoke and flame detection and evaluates its impact on both detection accuracy and edge deployment performance. We introduce the Aerial Fire and Smoke Essential (AFSE) dataset (282 aerial-view images; classes—smoke and fire), compiled from publicly available wildfire footage and FLAME2. Lightweight YOLO models are fine-tuned using heterogeneous (MS COCO) and homogeneous (FASDD) source pretraining and are assessed using mAP@0.5 together with frames per second (FPS), average inference power, energy consumption, and the normalized energy–delay product (EDP) on an edge computing platform. Results show that TL substantially improves detection accuracy on AFSE, achieving up to 79.2% mAP@0.5, while reducing training time, and improving cross-validation stability. On the tested edge platform, TL does not materially change inference speed or energy use, indicating that accuracy gains from TL do not automatically translate to improved efficiency without additional optimization. Among the evaluated lightweight detectors, YOLOv5n achieves the best mAP@0.5 while maintaining the highest edge device throughput, processing images nearly twice as fast as YOLO11n without hardware acceleration. More broadly, the measured throughput and energy differences among lightweight YOLO variants show that edge model selection should be guided by application-specific accuracy, latency, and energy constraints. Full article

(This article belongs to the Special Issue Feature Papers in the ‘Sensor Networks’ Section 2026)

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Review

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60 pages, 36058 KB

Open AccessReview

A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security

by Wajiha Zaheer, Chukwunonso Henry Nwokoye, Seyedeh Negar Afrasiabi, Khalil El-Khatib and Li Yang

Sensors 2026, 26(12), 3886; https://doi.org/10.3390/s26123886 - 18 Jun 2026

Viewed by 770

Abstract

The increasing deployment of IoT-enabled electric-vehicle charging networks has created a rapidly evolving cyber–physical environment in which security mechanisms must operate amid ever-changing data patterns and resource constraints. In these environments, static Machine Learning (ML) pipelines are often insufficient because they struggle to adapt to concept drift issues, emerging attacks, and real-time operational requirements. We analyzed cybersecurity vulnerabilities, challenges of conventional ML approaches, and the possibilities of AI-powered, adaptive security measures. This paper examines Online AutoML and its advantages, including automated adaptation to streaming data, reduced human intervention, and privacy-preserving, resource-aware learning. Furthermore, this paper discusses adversarial attacks and defences in Online AutoML systems, highlighting the need for frameworks that jointly address concept drift, scalability, privacy, and adversarial threats. Finally, this study emphasizes the importance of establishing comprehensive public benchmarks for Online AutoML research. Full article

(This article belongs to the Special Issue Feature Papers in the ‘Sensor Networks’ Section 2026)

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