International Journal of Sensor Networks (16 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 Decoupling Algorithm for Three-Dimensional Electric Field Sensors Based on Extreme Learning Machines Optimised by Bat Algorithm  
  by Wei Zhao, Zhizhong Li 
  Abstract: During measuring the spatial electric field intensity using a three-dimensional electric field sensor, due to the electric field components coupling effect caused by the electric field distortion, a certain coupling error exists in the electric field intensity components measurement. Aiming at the problem of insufficient decoupling accuracy of the traditional extreme learning machine method, an optimised extreme learning machine method based on the combination of maximum inter-class variance and the bat algorithm is proposed to decouple the three dimensional electric field sensor. The Bat algorithm optimised the extreme learning machine methods optimal initial weight and threshold. The maximum inter-class variance method was used to analyse the inherent coupling characteristics of the sensor. The coupling effect was classified according to the varying coupling contribution degree. The traditional extreme learning machine decoupling network was extended. The calibration experiments and decoupling calculations show that the extreme learning machine algorithm optimised by the bat algorithm and maximum inter-class variance can effectively reduce the error, which is between the electric field components obtained by the model calculation and the actual electric field components, and can effectively reduce the interference generated by the inter-dimensional coupling effect of the sensor, and further improve the measurement accuracy of the electric field intensity.
  Keywords: bat algorithm; decoupling; extreme learning machine; maximum inter-class variance; three-dimensional electric field sensor.
  DOI: 10.1504/IJSNET.2025.10072003
   
  
• A New Hyperchaotic Image Encryption Scheme Based on DNA Computing and SHA-512  
  by Shuliang Sun, Xiping Wang, Zihua Zhao 
  Abstract: Smartphones and digital cameras are becoming more and more widespread in the world. Massive images are generated every day in the world. They are easily transmitted on the insecure channel-Internet. Encryption technique is usually adopted to protect sensitive images during communication. A new cryptosystem is constructed by six-dimensional (6D) chaotic system and deoxyribonucleic acid (DNA) techniques. Firstly, the hash value is calculated. It keeps the encrypted result closely connected with the original image. The initial conditions of the cryptosystem are produced with the generated hash value and secret key. Secondly, the pixel is divided into four parts, forming a large matrix. Scrambling is performed on the new image. Subsequently, DNA coding, modern DNA complementary rules, DNA computing, and DNA decoding are utilised. Diffusion operation is also executed to improve the security, and the ciphered image is achieved finally. The experimental performance reveals that the designed algorithm has some advantages. It also signifies that the designed algorithm could protect against common attacks and is more secure than some existing methods.
  Keywords: hyperchaotic system; DNA computing; SHA-512.
  DOI: 10.1504/IJSNET.2025.10072159
   
  
• 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.
  
  
• Long-term Wind Power Prediction based on Feature Fusion Model and Temporal Pattern Attention Mechanism  
  by Li Liu, Ze Wang, Siwen Lei, Shengchi Liu, Hao Wang, Yue Jiang 
  Abstract: With the increasing global demand for clean energy, wind power has rapidly expanded as a renewable resource. However, the multidimensionality, long time series, and high volatility of wind power data pose significant challenges for long-term forecasting. This paper proposes a long-term wind power prediction model that utilises a feature fusion method and an attention mechanism. It integrates the strengths of the Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM) algorithms, employing the temporal attention mechanism for fusion. The LightGBM algorithm handles multidimensional data and selects critical spatial features from wind farm data, while the LSTM network captures long-term dependencies in time-series data. The attention mechanism dynamically assigns weights to predictions based on specific conditions, allowing the model to focus on more relevant features during different periods and fluctuation regions. Experiments on data from multiple regions demonstrate that the proposed model outperforms existing methods, especially in long-term predictions.
  Keywords: wind power; long-time series; spatial multi-features; temporal attention mechanism; feature fusion model.
  DOI: 10.1504/IJSNET.2025.10073135
   
  
• A Behaviour Detection Algorithm Integrating Lightweight Networks and Feature Recombination  
  by Gen Liang, Yu Zhang, Guoxi Sun, Xinchao Li 
  Abstract: Traditional behaviour detection methods often have problems such as low accuracy and slow processing speed, making it difficult to meet the practical application needs of industrial production scenarios. This study proposes a behaviour detection algorithm that integrates lightweight networks and feature recombination. First, we replace you only look once (YOLO) backbone with an enhanced MobileNetV3, reducing model complexity and accelerating inference. Second, we introduce content-aware reassembly of features, replacing conventional upsampling to improve precision. Further, switchable atrous convolution in the neck network enhances adaptability to multi-scale features, while vision transformer with deformable attention strengthens spatial modelling. Ablation experiments demonstrate the algorithms effectiveness, achieving a 75.2% mAP, with gains of 2.8% and 4.8% in precision and recall, respectively. Compared to existing technologies, this method offers the advantages of fast speed and high accuracy, making it suitable for real-time detection scenarios, such as those in the petrochemical industry.
  Keywords: behaviour detection; lightweight network; feature reorganisation; dilated convolution; deformable attention.
  DOI: 10.1504/IJSNET.2025.10073426
   
  
• Sparse Signal Recovery via Reweighted Shrinkage Thresholding: Applications in Compressed Sensing MRI  
  by Yuze Liu, Xiaokun Zhou, Mingjun Feng, Da Cao, Wei Wang 
  Abstract: Compressed sensing (CS) is a powerful technique for rapid magnetic resonance imaging (MRI). The iterative shrinkage thresholding algorithm (ISTA) is widely used due to its computational efficiency, but conventional versions lack adaptive adjustment of regularisation parameters, which limits both accuracy and speed. We propose a novel algorithm that dynamically updates regularisation weights according to the convergence status of previous iterations. To mitigate staircase artefacts commonly produced by wavelet-based reconstructions, the method incorporates the contourlet transform, which more effectively captures edges and contours. The approach is designed for sensor-based MRI systems, where efficient data acquisition and processing are critical for real-time imaging. Experiments on MR images from multiple anatomical regions and sampling rates demonstrate that the method achieves faster convergence and superior reconstruction quality compared to traditional ISTA, highlighting its potential in real-time medical imaging applications, including wireless health monitoring and sensor-network-based MRI systems.
  Keywords: Compressed Sensing; Iterative Shrinkage Thresholding Algorithm; Regularization Weight Adjustment; Contourlet Transform.
  DOI: 10.1504/IJSNET.2025.10073600
   
  
• A Temporal Property Graph Data Model Compatible with Static Graphs and its Temporal Graph Query Language  
  by Tiantian Jiang, Guanlin Chen, Haoye Wang, Mingli Song 
  Abstract: To address the issues of the disconnection between static and dynamic data in existing temporal graph data models and the insufficient usability of temporal graph query languages, this paper proposes TPGMSG (the Temporal Property Graph Model Compatible with Static Graphs), a temporal property graph model compatible with static graphs, along with its query language TGQLSG (the Temporal Graph Query Language Compatible with Static Graphs). The model distinguishes between dynamic and static elements, supports the temporal evolution of nodes, edges, and attributes. TGQLSG, as a temporal extension of GQL (the graph query language standard), offers a declarative syntax and comprehensive temporal query capabilities. Furthermore, a Neo4j-based implementation scheme is proposed, where a converter transforms TGQLSG queries into GQL queries. Experimental results show that this model achieves 20.7% and 40.3% improvements in time performance compared to traditional property graphs and static-incompatible models, respectively, while reducing storage space by 79.7%.
  Keywords: temporal graph; static graph; graph data model; graph query language; sensor.
  DOI: 10.1504/IJSNET.2025.10074270
   
  
• Routing Techniques using Reconfigurable Intelligent Surfaces  
  by Majed Abdouli 
  Abstract: As we advance toward the sixth generation of mobile networks (6G), the demand for more efficient, high-capacity, and flexible communication systems is becoming increasingly evident. One of the most promising technologies to address these demands is Reconfigurable Intelligent Surfaces (RIS). RIS are smart surfaces composed of a large number of passive reflecting elements that can be dynamically adjusted to enhance the wireless communication environment. These surfaces promise to revolutionise network performance by improving signal strength, reducing interference, and optimising the use of available spectrum. However, to fully leverage the benefits of RIS, it is crucial to develop and implement optimal routing protocols that can adapt to the dynamic nature of these surfaces. This paper explores the integration of RIS into routing protocols for 6G networks. Our approach involves the development of novel routing algorithms that incorporate RIS-based enhancements to improve overall network outage and enhance reliability.
  Keywords: routing; 6G; RIS.
  DOI: 10.1504/IJSNET.2025.10074637
   
  
• 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
   
  
• Dynamic Image Compression and Reconstruction via Tensor Decomposition on Edge Nodes  
  by Zhaohua Zeng, Xiaoxia Wang 
  Abstract: In wireless multimedia sensor networks, edge nodes are constrained by computational resources and energy supply, necessitating an efficient balance between image compression and reconstruction. To address this, this paper employs block-sparse tensor-based compression coding for images. A virtual codebook pool with block-sparse characteristics is trained based on image texture features, utilising Tucker decomposition and fractal coding for grayscale matching. Building upon this, a hierarchical clustered network topology is designed to collaboratively perform image decomposition, compression, and reconstruction at edge nodes. To enhance image reconstruction quality, a dynamic image reconstruction model based on block-sparse tensor decomposition and the Transformer architecture is proposed. Block-sparse tensor decomposition is embedded within the Transformer to learn global information of the image. Experimental results demonstrate that the proposed method achieves a network energy consumption of only 397.48 nJ/bit, with a Peak Signal-to-Noise Ratio of 38.86 dB.
  Keywords: wireless multimedia sensor network; edge nodes; tensor decomposition; image compression and reconstruction; Transformer model.
  DOI: 10.1504/IJSNET.2025.10075591
   
  
• Enabling Task Flexibility in Humanoid Robots through Multi-Modal Perception and Real-Time Control  
  by Xiaojuan Wei, Meng Jia, Yongkuan Zhu 
  Abstract: The rising demand for intelligent automation has highlighted the relevance of adaptive humanoid robotics in enhancing work flexibility within unstructured and dynamic contexts. Prior studies have predominantly concentrated on the implementation of humanoid robots in regulated, uniform environments, characterised by predetermined task sequences and consistent surroundings. Conventional systems, typically reliant on rule-based control and restricted sensing, encounter difficulties in functioning efficiently in real-world situations marked by variability and non-standardised processes. This paper presents FlexHuRo, an innovative humanoid robotic framework that improves adaptability by using multi-modal perception and real-time control algorithms. FlexHuRo integrates visual, aural, and tactile senses with adaptive motion planning and decision-making abilities, enabling robots to decipher intricate environmental signals and react dynamically to fluctuating surroundings. The framework was assessed in semi-structured testing environments, revealing a 23% enhancement in task success rates and a 31% decrease in execution time relative to baseline models. It demonstrated robust tolerance to environmental variability, including dynamic impediments and fluctuating illumination conditions. The results underscore FlexHuRos capacity to facilitate the effective operation of humanoid robots in sectors including healthcare, logistics, and service industries, where task generalisation and autonomous flexibility are essential for operational success.
  Keywords: Humanoid Robotics; Unstructured Environments; Task Adaptability; Multi-modal Perception; Real-Time Control Algorithms; Human-Robot Interaction.
  DOI: 10.1504/IJSNET.2025.10075648
   
  
• 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