International Journal of Sensor Networks (25 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.
  
  
• 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
   
  
• 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
   
  
• 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
   
  
• Competitive Physical Action Recognition via Inertial Sensing and Graph Convolutional Transformer  
  by Bingjie Sun, Rongjiao Hu 
  Abstract: Based on wearable-sensing Internet of Things technology, this paper proposes a complete solution encompassing data acquisition, processing, and intelligent recognition for the identification of athletic physical movements. A wireless inertial measurement unit sensor network is deployed across key body segments to capture motion data synchronously. After preprocessing, including low-pass filtering and sensor calibration, a spatio-temporal graph structure is constructed and fed into an innovative graph convolutional-Transformer fusion model. This architecture fully leverages graph convolution to extract spatial correlation features from multiple sensors, while the Transformer component effectively captures long-range temporal dependencies within movement sequences. Experimental results on the publicly available University of California, Irvine Human Activity Recognition dataset demonstrate that our method achieves 94.2% recognition accuracy. This performance confirms its practical value in wearable-sensing Internet of Things systems and provides an effective technological pathway for advancing smart sports training platforms.
  Keywords: inertial sensing; graph convolution; Transformer; action recognition.
  DOI: 10.1504/IJSNET.2026.10076439
   
  
• On the Physical Layer Security of CR-NOMA Assisted Intelligent Transportation Networks  
  by Rongyu Wang, Enyu Li, Xiaofei Zhai, Yinuo Zhao, Afei Dai 
  Abstract: For the intelligent transportation system driven by mobile communication, we study the physical layer security (PLS) performance of the wiretap cooperative non-orthogonal multiple access (NOMA) network with an untrusted near primary user. To protect communication security, we propose an artificial noise-assisted overlay cognitive cooperation framework. The closed-form expressions of the outage probability (OP) for all users, the intercept probability (IP) of the untrusted primary user eavesdropping on primary and secondary network security information, and the approximate results under the high signal-to-noise ratio (SNR) are derived. Numerical results verify the theoretical analysis, and we conclude: i) The OPs of all users decrease with the SNR increasing, and the OP of the untrusted user exists a ceiling; ii) Increasing the SNR, the IPs of the untrusted user to the primary and secondary network confidential information increase until convergence to the same fixed constant; iii) Reasonable configuration of parameters can optimize system performance.
  Keywords: Non-orthogonal Multiple Access; Overlay Cognitive Radio; Cooperative Relaying; Physical Layer Security; Artificial Noise.
  DOI: 10.1504/IJSNET.2026.10076441
   
  
• 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.
  
  
• 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 method's 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; ELM; electric filed 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
   
  
• 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
   
  
• 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; ISTA; regularisation weight adjustment; contourlet transform.
  DOI: 10.1504/IJSNET.2025.10073600
   
  
• 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 algorithm's 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; DAttention.
  DOI: 10.1504/IJSNET.2025.10073426
   
  
• 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; reconfigurable intelligent surfaces; RIS.
  DOI: 10.1504/IJSNET.2025.10074637