International Journal of Sensor Networks (22 papers in press)

Regular Issues

• Optimizing Power Management in Wireless Sensor Networks Using Machine Learning: An Experimental Study on Energy Efficiency  
  by Mohammed Amine Zafrane, Ahmed Ramzi Houalef, Miloud Benchehima 
  Abstract: Wireless sensor networks (WSNs) have emerged as essential components across various fields. Comprising small, self-sustaining devices known as "Nodes," they play a critical role in data collection and analysis. However, ensuring optimal longevity without compromising data collection timeliness is a fundamental challenge. Regular data aggregation tasks, while essential, consume substantial energy resources. Furthermore, constraints in computation power, storage capacity, and energy supply pose significant design challenges within the Wireless Sensor Network domain. In pursuit of optimizing energy efficiency and extending the operational lifetime of nodes through artificial intelligence, we have developed a prototype for data collection to create a comprehensive dataset. Our approach leverages both current and precedent measurements, triggering data transmission only in the presence of significant changes. This intelligent strategy minimizes unnecessary communication and conserves energy resources. Based on the
  Keywords: Artificial intelligent; WSN; Power optimization; data acquisition.
  DOI: 10.1504/IJSNET.2024.10068162
   
  
• A Path Optimization Method Based on Dynamic Clustering Strategy and Nondominated Sorting Genetic Algorithm II in Wireless Sensor Networks  
  by Liying Zhao, Jin Zhu, Chao Liu, Yu Wang, Sinan Shi, Chao Lu, Qi Luan 
  Abstract: The traditional wireless sensor network data transmission path selection often considers only single-objective optimization, such as energy consumption or transmission delay, which leads to problems such as unbalanced node load and insufficient path reliability. Therefore, this study proposed a wireless sensor network routing optimization method that integrates a dynamic clustering strategy with the nondominated sorting genetic algorithm II. First, the network nodes of the wireless sensor network were divided into clusters of varying scales using the MiniBatchKMeans method. Then, the residual energy of nodes, their distance to the cluster center, and historical load were comprehensively evaluated to elect a cluster head for each cluster. Subsequently, the nondominated sorting genetic algorithm II algorithm was employed to generate Pareto-optimal paths, with the objective functions encompassing minimization of energy consumption, reduction of transmission delay, and maximization of signal strength (Received Signal Strength Indication).
  Keywords: Clustering strategy, nondominated sorting genetic algorithm II, path optimization, wireless sensor network.
  
  
• Performance Analysis of Ensemble Learning Classifiers for Intrusion Detection in IoT Paradigm  
  by Aishwarya Vardhan, Prashant Kumar, Lalit Kumar Awasthi 
  Abstract: IoT has emerged as a transformative paradigm connecting billions of smart devices, but its rapid expansion raises critical challenges such as security vulnerabilities, data breaches, and large-scale cyberattacks. Intrusion Detection Systems (IDS) play a vital role in mitigating these issues by monitoring network traffic and identifying malicious behavior to enhance IoT resilience. In recent years, machine learning (ML) and ensemble learning (EL) have significantly impacted IDS by enabling adaptive and efficient detection of sophisticated threats. While ML based approaches improve attack detection to a certain extent, EL further outperforms standalone ML models by combining multiple learners to enhance classification accuracy, robustness, and generalisation. To validate this claim, we conduct experiments on NF-UNSW-NB15v2 dataset, where results reveal that EL approaches consistently achieve superior detection performance compared to conventional ML techniques. Comparative analysis reveals that ensemble-based IDS significantly reduces false alarms while achieving higher accuracy and balanced detection rates across diverse attack categories.
  Keywords: Intrusion Detection Systems; Internet of Things; Machine Learning; Ensemble Learning; Network-based Datasets; Network Security; False Positive Alarms.
  DOI: 10.1504/IJSNET.2025.10074640
   
  
• A Survey on the recent methodologies of Secure Social Internet of Things (SIoT)  
  by Divya S, Tanuja R, Manjula S. H. 
  Abstract: The integration of social networks with internet of things has led to the emergence of the Social Internet of Things (SIoT), where intelligent objects interact socially to discover and share services. However, unreliable nodes can disrupt operations by spreading malicious information, compromising service quality and trust. This survey provides a comprehensive review of SIoT approaches focusing on trust management, security, and privacy. It outlines a generic SIoT framework, categorises existing research, and highlights key challenges and gaps to guide future developments. The analysis reveals that most studies employ trust management techniques with network simulators and social network datasets for evaluation, using trust score and accuracy as primary performance metrics.
  Keywords: Social Internet of Things (SIoT); trust management; privacy preservation; Deep Learning (DL); Attack detection.
  DOI: 10.1504/IJSNET.2025.10074737
   
  
• Sparse-LSTM for Sports Fatigue Assessment in a Wearable Sensing Network  
  by Ziping Meng, Jingya An, Linpeng Xiao 
  Abstract: Accurate assessment of exercise fatigue has become a critical requirement for enhancing the scientific rigor of athletic training. However, traditional methods face challenges such as insufficient feature extraction capabilities and low evaluation accuracy. To address this, this paper first collects exercise fatigue data through a wearable sensor network, then performs window segmentation to obtain effective keyframe data. Building upon an enhanced Inception network architecture, the feature map dimensions are expanded to enable estimation of exercise fatigue actions. To capture key motion trajectories, a sparse distribution-enhanced long short-term memory network is employed for temporal feature extraction. Finally, a similarity evaluation method based on dynamic time warping and the longest common subsequence is designed to analyse angular distance differences, thereby enabling the assessment of athletic fatigue. Experimental results demonstrate that the proposed model achieves an improvement in evaluation accuracy of 4.08% to 13.97%.
  Keywords: exercise fatigue assessment; wearable sensor network; sparse distribution-enhanced long short-term memory network; dynamic time warping; longest common subsequence.
  DOI: 10.1504/IJSNET.2025.10075384
   
  
• Optimizing the Adaptive Design of Wireless Sensor Networks Using an Enhanced Particle Swarm Optimisation Algorithm  
  by Mina Mirhosseini, Fereshteh Forouzesh, H. Nezamabadi-pour, Ali Darijani 
  Abstract: One of the key challenges in wireless sensor networks (WSNs) is reducing energy consumption to enhance lifespan and performance. In this paper, an improved Particle Swarm Optimization algorithm, called NBPSO, is adapted for optimal WSN design under both static and dynamic optimal design frameworks. The algorithm reduces energy consumption, extends network lifespan, and satisfies communication constraints and application requirements. Each sensor operates in one of four states: cluster head, active with long range, active with short range, or inactive. Simulation results show that NBPSO outperforms Genetic Algorithm, Particle Swarm Optimization, and Quantum Gravitational Search Algorithm, achieving significantly better network lifetime. To improve scalability, a multi-sink deployment strategy is introduced and evaluated, demonstrating NBPSO's practicality for larger networks. In addition, a brief sensitivity analysis of the NBPSO parameters has been conducted to examine their influence on performance.
  Keywords: Particle Swarm Optimization; Wireless sensor networks; energy optimization; adaptive design.
  DOI: 10.1504/IJSNET.2025.10075663
   
  
• Experimental Demonstrations of Visible Light Communication Using Quantum Noise for High Security  
  by Ning Xiao, Sihui Chen, Feifei Chen, Shuai Shi 
  Abstract: This paper reports on a symmetric-key direct-data encryption technique that ensures the security of optical signals at the physical layer in a visible light transmission system. Security is achieved by masking the signal with quantum noise. Encryption with quantum noise masking is realized by converting the data into a multilevel optical signal of intensity modulation. The effect of the quantum noise masking is easily superimposed on the ciphertext signal, which also fully uses the inevitable characteristics of quantum noise. We have demonstrated experimentally a 100Mbit/s visible light cipher system with quantum noise masking. The visible light transmission system implements high security with adequate quantum noise masking.
  Keywords: quantum noise; physical layer security; encryption; intensity modulation; visible light communication.
  DOI: 10.1504/IJSNET.2025.10075734
   
  
• NOMA-VLC Power Allocation Optimisation assisted by UAV  
  by Ting Liu, Guangzhao Wang, Jingyu Zhang, Yunshan Sun, Yanqin Li, Teng Fei, Zhanbo Wang 
  Abstract: The integration of unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and visible light communication (VLC) advances future communication technologies. Despite its potential to overcome spectrum limitations and extend coverage, challenges such as link attenuation and power allocation imbalances hinder performance. This study focuses on UAV-assisted NOMA-VLC systems and proposes dynamic UAV positioning to address limitations of fixed-light-source deployments. A dual marine predator algorithm (DMPA) for power allocation optimisation is introduced, featuring dual-predator reinforcement search, adaptive acceleration factors for improved convergence, and dynamic thresholds based on rate standard deviation and Jain fairness index. Experimental results show that the DMPA outperforms competing schemes in both ideal and obstructed environments. Dynamic UAV positioning enhances signal coverage and system robustness, particularly in obstacle-rich environments
  Keywords: Non-Orthogonal Multiple Access; Visible Light Communication; Unmanned Aerial Vehicles; Swarm Intelligence; Marine Predator Algorithm.
  DOI: 10.1504/IJSNET.2025.10075935
   
  
• Federated Learning and Dynamic Game-Based Collaborative Optimisation for Resource Allocation in IoT Data Acquisition  
  by Yunqi Wang, Liangliang Ding 
  Abstract: Internet of things networks are expanding rapidly, making efficient resource allocation for data acquisition a critical challenge. The resources considered include communication bandwidth, energy, and computational capabilities. Traditional centralised optimisation methods face significant difficulties due to limitations in these resources, as well as privacy concerns. This paper proposes a collaborative optimisation framework combining federated learning and dynamic game theory to achieve decentralised and adaptive resource allocation in IoT data acquisition systems. The approach enhances privacy protection while reducing communication overhead. Existing federated learning methods have shown reductions in communication costs specifically, the number of communication rounds and data volume by up to 94.89%. Dynamic game approaches in IoT have demonstrated improvements, including 42% higher packet delivery ratios and up to 32% lower latency, in environments with moderate node density and interference levels. The proposed framework helps balance the growing energy demands of IoT networks while ensuring data security and transmission efficiency.
  Keywords: federated learning; dynamic game theory; resource allocation; Internet of Things; data acquisition; collaborative optimization; privacy preservation.
  DOI: 10.1504/IJSNET.2025.10075936
   
  
• Aerial Muling As a Service For Terrestrial Wireless Sensor Networks  
  by Soumaya B.E.L. HadaJ Youssef, Slim Rekhis 
  Abstract: In this paper, we propose a system architecture based on the use of multi-broker public cloud of unmanned aerial vehicles (UAVs) to offer a muling as a service (MuaaS) to largely deployed terrestrial wireless sensor networks (WSNs). The proposed system is designed to coordinate the UAVs arrival and manage their mobility during the collection of sensed data from terrestrial data collectors. To address this, we describe the muling data problem and present an algorithm designed to achieve minimal data delivery delay. In addition, we address a grid-based WSN deployment, enabling the computation of model parameters. Therefore, a mathematical expression for minimising data delivery delay is provided. Furthermore, we present a scheme for selecting the best UAV broker offer based on a decision-making criteria that consider minimisation of data delivery delay, UAV energy consumption, and cost of delivery service. Finally, a simulation is conducted to evaluate the performance of the solution.
  Keywords: Wireless sensor network; Unmanned Aerial Vehicle Network; cloud computing; Muling As A Service; data delivery delay minimisation.
  DOI: 10.1504/IJSNET.2025.10076118
   
  
• SGSCP: Subnetwork-based Grid Search Cooperative Positioning for Irregular Wireless Sensor Networks  
  by Xingsheng Xia, Yilin Xia, Xiaoyong Yan, Zhi-E Lou, Jiajia Yan 
  Abstract: Wireless sensor networks are widely deployed in complex and unstructured environments, where accurate node location information is fundamental for network operation and data interpretation. In practical scenarios, obstacles, signal attenuation, and uneven node distributions often lead to irregular network topologies, under which conventional hop-based positioning methods suffer from degraded performance due to their reliance on homogeneous assumptions. To address this issue, a subnetwork-based grid search cooperative positioning (SGSCP) algorithm is proposed. The method allows beacon nodes to exchange hop information using the Bellman-Ford protocol and independently estimate local hop-distance characteristics. Each unknown node then constructs a constrained subnetwork composed of its four nearest beacons and performs a bounded grid search guided by hop similarity. Extensive simulations demonstrate that SGSCP achieves improved localisation accuracy, enhanced robustness to beacon density variations, and stable performance across diverse irregular topologies, outperforming existing methods in terms of median error and error dispersion.
  Keywords: Grid Search; Cooperative Positioning; Irregular Wireless Sensor Networks.
  DOI: 10.1504/IJSNET.2025.10076201
   
  
• NTOS: Nature-inspired Task Offloading Strategies in IoT-Fog Environment for Improved Response Time  
  by Ritarani Sahu, Aniket Ganvir, Suchismita Chinara 
  Abstract: The rapid growth of internet of things devices and the increasing demand for low-latency communication and high-performance applications have brought forth the emergence of fog computing as a viable solution. Fog computing takes advantage of the nearness of fog nodes to the edge of the network to offload compute-intensive tasks from resource-limited devices to fog nodes with the aim of optimising the response time and balancing the workload of fog nodes. The task offloading problem has been already proven to be NP-hard and it is very challenging to address this issue. The current work aims to handle this offloading problem by proposing two nature-inspired meta-heuristic optimisation algorithms: offloading algorithm-ant colony optimisation and offloading algorithm-artificial bee colony. Simulations has been performed considering various scenarios and it is observed that both the proposed algorithms perform wonderfully better as compared to Round Robin, grey wolf optimiser, particle swarm optimisation, and sparrow search algorithm, to achieve minimum average response time. Also simulations confirm that both algorithms are very good at maintaining the workload balance among fog nodes and quality of task offloading.
  Keywords: Internet of Things; compute-intensive; resource-limited; task offloading.
  DOI: 10.1504/IJSNET.2025.10076236
   
  
• Optimized Monocular Vision Ranging via Parallel-Line Feature Rectification  
  by Jingwen Qian, Jin Zhang, Kangwei Wang, Jie Sheng, Cheng Wu 
  Abstract: To address the degradation of distance estimation accuracy caused by perspective distortion in long-range imaging scenarios, this paper proposes a monocular vision ranging method via parallel-line feature rectification, termed PLFR. The method leverages the geometric regularity of parallel lines under perspective projection, using their pixel widths at different distances as stable reference scales to mitigate distortion-induced errors. Specifically, PLFR first employs an improved YOLOv5 to extract the position and size information of targets, and then integrates the geometric attributes of parallel lines to dynamically calibrate the model parameters via recursive least squares (RLS), enhancing accuracy and robustness in long-range scenarios. This strategy integrates object detection outputs with scene geometry information to effectively suppress distance estimation errors at extended ranges. Experimental results demonstrate that PLFR maintains an average ranging error within 5% over a 200-metre range. Notably, in the long-distance range of 150200 metres, the average error can be reduced to 2.68 m, significantly outperforming representative existing methods
  Keywords: long-range; object detection; monocular ranging; parallel elements.
  DOI: 10.1504/IJSNET.2025.10076395
   
  
• CIC-LQR Power Control Strategy for Reliable Wireless Communication in Cable Tunnel Inspection Robots  
  by Xue Liu 
  Abstract: With the expansion of urban power grids and the rapid development of underground infrastructure, the reliability of wireless communication for cable tunnel inspection robots faces major challenges. The tunnel environment, characterised by severe obstructions, strong electromagnetic interference, and strict energy constraints, makes it difficult for traditional power control methods to ensure both link stability and energy efficiency. To address these issues, this paper proposes a confidence interval compensation linear quadratic regulator (CIC-LQR) power control strategy. A state-space model is established based on the logarithmic path loss model, and a skew-normal distribution is employed to capture the skewness and heavy-tailed characteristics of received signal strength indicator (RSSI) samples. A confidence interval compensation mechanism is then introduced to dynamically adjust the reference RSSI, enhancing robustness against channel disturbances. Simulation and real-world tunnel experiments show that CIC-LQR significantly outperforms conventional MPC, LQR, LQG, and CI-MPC methods. Specifically, it reduces the proportion of samples falling below the required RSSI threshold to 2.8% (compared with 12.8% for LQR and 20.2% for MPC) and achieves a clear reduction in average transmit power. These results demonstrate that CIC-LQR enables reliable and energy-efficient wireless communication in harsh tunnel environments.
  Keywords: Cable Tunnel; Inspection Robot; Wireless Communication Reliability; RSSI; Skew-Normal Distribution;Power Control.
  DOI: 10.1504/IJSNET.2025.10076523
   
  
• Wavelet Entropy-Based Adaptive Compression of Transient Waveform Data in Distribution Networks  
  by Chenglong Ye, Wei Sun, Xinyu Qiu, Zhiqiang Zhang, Yue Li, Zhen Zhang 
  Abstract: Integration of distributed photovoltaic in distribution networks increases abnormal conditions, requiring efficient data compression for fault monitoring and early warning under limited communication bandwidth. In this case, it is necessary to strengthen the monitoring of distributed photovoltaic systems, which greatly increases the amount of data. However, the resource limitations of the distribution network require efficient compression of fault monitoring and early warning data within constrained communication bandwidth. Therefore, efficient compression of fault monitoring and early warning data is essential under limited communication bandwidth. Existing compression techniques treat transient waveform data as regular data, neglecting targeted compression. They fail to account for the energy characteristics of waveform data across different frequency bands critical for fault detection. To tackle this challenge, this study introduces an adaptive data compression and reconstruction method based on wavelet entropy. Initially, different types of transient waveform data are adaptively selected for wavelet transform using appropriate wavelet basis functions, enhancing the transform's adaptability to various data types.
  Keywords: Wavelet transform; Data compression; Photovoltaic.
  DOI: 10.1504/IJSNET.2025.10076639
   
  
• Collaborative Adaptive Pricing (CAP) Framework: a Dynamic Approach to AI Model Trading  
  by Milon Biswas, Yifan Guo, Weixian Liao, Wei Yu 
  Abstract: Artificial Intelligence (AI) model trading has become an emerging paradigm, enabling organizations and researchers to access high-quality pre-trained models. Traditional static pricing mechanisms often fail to align with dynamic market conditions, leading to inefficiencies in affordability and revenue generation. This paper introduces the Collaborative Adaptive Pricing (CAP) Framework, an innovative pricing strategy that incorporates adaptive noise injection, real-time pricing adjustments, and auction-based mechanisms to optimize revenue while ensuring accessibility. We present a mathematical formulation, experimental evaluation, and a scalability analysis demonstrating CAP's effectiveness over fixed and tiered pricing strategies. Our results show that CAP maximises revenue while maintaining buyer engagement and preventing market arbitrage. The framework is adaptable to various AI trading ecosystems, including federated learning and decentralised AI markets.
  Keywords: Collaborative Adaptive Pricing (CAP) Framework; AI Model Trading; Algorithm Design.
  DOI: 10.1504/IJSNET.2025.10076693
   
  
• A Multi-Sensor Attention-Enhanced Temporal Convolution Network for Hydropower Plant Equipment Fault Prediction  
  by Ruilin Yang, Yan Jin, Jiada Wei, Liyang Jiang, Sija Wang 
  Abstract: Hydropower systems depend critically on the continuous operation of generators, bearings, and control equipment, which endure complex multi-physical stresses. Accurate fault prediction is crucial for preventing catastrophic failures, yet it remains challenging due to the high dimensionality of sensor data and the presence of long-term temporal dependencies. Traditional approaches cannot model long-range patterns and dynamically weight sensor features. To overcome these challenges, we propose an attention-enhanced temporal convolutional network that integrates dilated convolutions for efficient long-sequence modeling, along with a multi-head self-attention mechanism for adaptive feature selection. The model captures multi-scale temporal features and dynamically emphasizes informative sensors and time steps. Evaluated on the National Aeronautics and Space Administration Commercial Modular Aero-Propulsion System Simulation dataset, our method achieves a root mean square error of 12.56 and a prognostic score of 356, outperforming the vanilla temporal convolutional network by 8.7% and 15.3%, respectively.
  Keywords: fault prediction; temporal convolutional network; attention mechanism; multi-sensor data; remaining useful life.
  DOI: 10.1504/IJSNET.2025.10076812
   
  
• A Differential Game-Based Security Mechanism for Protecting Data Elements Circulation  
  by Lei Wang, Mengmeng Li, Haitao Xu, Haoyu Shi, Xianwei Zhou 
  Abstract: In intelligent connected vehicles (ICVs), driver-users transmit sensitive information to roadside units (RSUs) to obtain value-added services. Attackers may compromise RSUs to exfiltrate sensitive data, thereby posing severe privacy threats to vehicular networks. Most existing defence mechanisms rely on passive protection strategies, which exhibit limited adaptability against dynamic attacks. Recent studies have applied differential game models to describe attacker-defender dynamics in vehicular networks. However, these works often assume static payoffs, adopt passive defence strategies, and lack explicit mechanisms linking defence strength to real-time packet behaviour, limiting their practicality in complex environments. To address this, this paper proposes a data-element circulation security mechanism based on differential game theory, where the RSUs health level is modelled as a contested resource. The proposed framework introduces packet-marking strength (PMS) as an active defence variable within a feedback differential-game formulation, enabling the defender to dynamically adjust PMS in response to variations in attack frequency. By deriving the feedback Nash equilibrium (NE), the defender can remove malicious packets, enhance RSUs health, and protect data privacy. Simulation results confirm that the proposed mechanism achieves greater adaptability and faster convergence than existing passive defence models, validating its theoretical soundness and practical effectiveness.
  Keywords: Intelligent connected vehicle; privacy protection; strategy optimization; data packet marking technology; differential game.
  DOI: 10.1504/IJSNET.2025.10076817
   
  
• Dynamic prediction of photovoltaic maximum power based on sensor network multimodal data and Transformer-LSTM  
  by Kelai Zhang, Xiduan Chen, Ru Qiu 
  Abstract: Accurate prediction of maximum power is crucial for enhancing generation efficiency in photovoltaic power generation. However, traditional forecasting methods often struggle to comprehensively reflect the real-time variations and combined effects of multiple environmental factors such as sunlight, temperature, and humidity, resulting in limited prediction accuracy. To overcome this limitation, this paper first proposes a photovoltaic data monitoring system based on wireless sensor networks. By deploying Zigbee network nodes on each photovoltaic module, the system collects and transmits environmental and operational data in real time, ensuring the reliability of photovoltaic power generation data acquisition. Building upon this foundation, this paper developed a predictive model that integrates long short-term memory networks with an enhanced Transformer model. Experimental results demonstrate that the method achieves a data acquisition efficiency of 96.5% and improves model fitting accuracy by at least 5.75%. It enables faster and more precise predictions of maximum photovoltaic power.
  Keywords: photovoltaic power generation system; maximum power prediction; multimodal sensor network; Transformer model; long short-term memory network.
  DOI: 10.1504/IJSNET.2025.10076824
   
  
• A Univariate Time-series Anomaly Detection Method for Sensor Network based on Conditional Variational AutoEncoder  
  by Ping He, Zhifeng Wu, Sirui Hao, Hao Liu, Jiangchuan Chen, Xuefeng Song, Yipeng Liu, Junjiang He 
  Abstract: With the rapid advancement of Industry 4.0, anomaly detection secures critical infrastructure by monitoring and analysing sensor time-series data. However, existing anomaly detection methods are often suffering from incomplete temporal feature extraction and delayed detection responses. To overcome these issues, we introduce conditional variational AutoEncoder-based anomaly detection (CVAD). CVAD enhances temporal pattern learning by utilising an improved CVAE that jointly processes raw time-series data and their corresponding frequency-domain representations. To maintain robustness under concept drift, we implement a self-adjusting threshold mechanism that dynamically adjusts detection criteria, eliminating the overhead of model retraining. Experimental results on the SensorScope dataset, NASA-SMAP dataset, and NASA-MSL dataset demonstrate that the proposed model outperforms seven baseline methods in terms of the F1 score: compared with the second-best baseline TimesNet (89.45% on SensorScope, 82.99% on NASA-SMAP, 89.79% on NASA-MSL), CVAD achieves 93.35%, 93.45%, and 89.84%, respectively representing improvements of 3.9%, 10.46%, and 0.05% in F1 score.
  Keywords: Sensor Networks; Univariate Time-series Data; Anomaly Detection; Conditional Variational AutoEncoder.
  DOI: 10.1504/IJSNET.2025.10076827
   
  
• A Lightweight Depthwise Separable Convolution Network for Event Detection in Phase-Sensitive Optical Time Domain Reflectometry  
  by Jing Wang 
  Abstract: Phase-Sensitive Optical Time Domain Reflectometer (?-OTDR) leverages Rayleigh scattering for long-distance, real-time monitoring, continuously sensing along the fiber to detect events like environmental disturbances. However, existing detection methods are often compromised by dynamic noise and signal drift, leading to reduced accuracy. This study introduces Lite-PhiOTDR, a streamlined detection network for ?-OTDR. Initially, a trend-separation and denoising module using one-dimensional convolution is developed. Large-kernel convolution removes low-frequency trends, while a small convolutional network suppresses high-frequency noise, resulting in an input signal that is zero-mean, smooth, and possesses a high signal-to-noise ratio. Additionally, a lightweight feature-extraction structure is implemented to characterize various time-series patterns, including short-term mutations, periodic disturbances, and slow drifts. By employing depthwise separable convolution and a lightweight design, the proposed method significantly reduces parameter count and computational complexity, thereby enhancing detection accuracy and enabling feasible embedded deployment.
  Keywords: distributed fiber sensing; lightweight network; ?-OTDR; depth separable convolution; multi-scale feature fusion.
  DOI: 10.1504/IJSNET.2025.10076995
   
  
• An Edge-Driven Based Sensor Selection and Sensory Data Prediction Model for Minimizing Energy Consumption in WSNs  
  by Vipin Maurya, Sumit Kumar, Ruchir Gupta 
  Abstract: Owing to the limited storage and battery power, wireless sensor nodes frequently encounter challenges in achieving long-term energy sustainability. This problem is exacerbated when all sensors are activated simultaneously, resulting in unnecessary energy consumption and a reduced network lifetime. Sensor selection can be a promising solution because it activates only a subset of sensors while keeping others in sleep mode, thereby conserving energy. Consequently, sleeping sensors result in missing parameter data, which can degrade sensing coverage. To align energy efficiency with adequate coverage, this paper proposes a sensor selection approach that selects the optimal sensors based on the available energy of sensor nodes and the normalised mutual information among sensors for sensing environmental parameters. The proposed model is theoretically analysed, and it is observed that the selected sensor set is stable and Pareto optimal. The sleep sensor parameters are predicted at the edge node using a temporal convolutional network-bidirectional long short-term memory (TCN-BiLSTM)-attention-based deep learning prediction model. Simulations are performed to validate the effectiveness of the proposed model. It is observed that the proposed approach reduces the sensing energy of the sensor node by 54%, and the prediction model reduces the error by 21% in the prediction of sleep sensor parameters.
  Keywords: Bidirectional-LSTM; Edge Computing; GPR; Normalized Mutual Information; Pareto Optimal; Sensor; Sensing Energy; Shapley Value; Temporal Convolutional Network (TCN).
  DOI: 10.1504/IJSNET.2026.10076996