International Journal of Sensor Networks (17 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
   
  
• Multi-agent deep reinforcement learning edge task scheduling algorithm with migratable service environment  
  by Zengwei Lyu, Yu Zhang, Zhenchun Wei, Juan Xu, Lei Shi, Yuqi Fan 
  Abstract: The multi-edge collaborative computing approach stores task service environments in edge nodes closer to end-users and uses multi-edge networks for collaborative offloading, overcoming long transmission distances and slow response times in traditional cloud computing. However, existing fixed-storage task offloading methods cannot dynamically schedule service environments for regional task preferences, leading to unbalanced multi-edge loads and reduced execution efficiency. We propose a container-based migratable service environment scheduling model to dynamically meet regional service demands via real-time task and environment scheduling. To address process coupling and storage replacement issues, we integrate offloading, environment migration, and content replacement into a unified scheduling action using reinforcement learning. Our improved multi-agent deep RL algorithm employs centralised training with distributed execution and an attention mechanism to optimise policy learning. Simulations show the approach enhances multi-edge load balancing and reduces average task delay.
  Keywords: multi-agent reinforcement learning; edge computing; task scheduling; edge computing; migratable service environment.
  DOI: 10.1504/IJSNET.2025.10073784
   
  
• Enhanced data rates with solar energy harvesting  
  by Emna Bouazizi 
  Abstract: The incorporation of energy harvesting into wireless sensor and communication networks offers an exciting pathway toward sustainable and self-sufficient operations. Among the various options available, solar energy is particularly appealing due to its abundance, scalability, and eco-friendliness. Nonetheless, optimising energy harvesting alongside data sensing remains a significant hurdle, especially with the fluctuations in solar irradiance and varying energy requirements. This study explores the balance between energy harvesting and sensing durations in solar-powered wireless systems, with the goal of maximising long-term data throughput while maintaining energy sustainability. We have formulated a time allocation optimisation problem in which a predetermined time slot must be divided between solar energy harvesting and wireless data sensing/transmission. By establishing optimal durations for harvesting and sensing based on realistic solar intensity models, we show that adaptive scheduling provides a notable advantage over static time allocation methods.
  Keywords: spectrum sensing; solar energy; throughput enhancement.
  DOI: 10.1504/IJSNET.2026.10074882
   
  
• Stochastic network calculus-based energy harvesting model for underwater agriculture  
  by S.R. Vignesh, Rajeev Sukumaran 
  Abstract: An underwater wireless sensor network (UWSN) is a specialised wireless sensor network for marine monitoring, where energy sustainability is critical since node batteries are difficult to replace. Energy harvesting (EH) from the underwater environment provides a practical solution. This research presents a novel EH framework for underwater agriculture monitoring by applying stochastic network calculus (SNC) to model and analyse the impact of delay and storage constraints on energy harvesting rate (EHR). The objective is to ensure reliable energy availability for continuous monitoring of parameters such as pH, salinity, temperature, dissolved oxygen, and water quality. The main contribution lies in integrating SNC with piezoelectric-based EH to derive probabilistic delay and energy performance bounds, an approach rarely explored in underwater agriculture. Simulation results using discrete-event simulators validate the analytical model, showing that larger packet sizes increase the minimum EHR, while stricter delay requirements lower the minimum EHR under a fixed traffic rate.
  Keywords: underwater wireless sensor network; UWSN; energy harvesting; EH; stochastic network calculus; SNC; energy harvesting rate; EHR.
  DOI: 10.1504/IJSNET.2025.10073911
   
  
• ToI-Model: trustworthy objects identification model for social-internet-of-things  
  by Rahul, Venkatesh 
  Abstract: The social-internet-of-things (SIoT) paradigm integrates social concepts into IoT systems. Identifying trustworthy SIoT objects, as well as managing trust, are essential for promoting cooperation among them. The current state-of-the-art methods inadequately quantify the trustworthiness of SIoT objects and fails to evaluate trustworthiness of SIoT objects. This paper comprehensively considers specific features of SIoT objects and integrates them with the theory of social trust. The proposed Trustworthy-objects identification model (ToI-Model) captures comprehensive trust, proficiency, readiness, recommendation, reputation, honesty and excellence metrics for identifying trustworthy objects in SIoT. Service requester (SR) uses trust score of service providers (SP) before initiating service delegation. A series of experiments are conducted to evaluate the proposed trust model's effectiveness in the successful completion of services, convergence, accuracy, and resilience against deceitful activities. Results of experiment shows that trust model identifies trustworthy service provider that has 19.89% more trust score and a 27.61% less latency than state-of-the-art models.
  Keywords: trustworthy; proficiency; readiness; recommendation; reputation; honesty; excellence.
  DOI: 10.1504/IJSNET.2025.10070868
   
  
• An edge computing offloading strategy based on multi-dimensional attributes and distributed deep learning  
  by Dong She 
  Abstract: With the explosive growth of smart terminal devices and the wide adoption of latency-sensitive applications, the traditional cloud computing model is difficult to meet the demands of ultra-low latency and high privacy protection. Therefore, this paper proposes a distributed deep reinforcement learning offloading strategy based on multi-dimensional joint modelling of task, device, and environment attributes. A state space integrating multi-dimensional attributes is constructed to achieve comprehensive awareness of the system state; a distributed asynchronous deep Q-network framework is designed to realise knowledge sharing through local model co-training among multiple edge nodes. Experimental results show that this approach can reduce the average task processing latency by 4.8% and the overall system energy consumption by 5.3%. This research provides a practical solution for computational offloading in resource-constrained edge scenarios that balances efficiency and energy consumption.
  Keywords: multi-dimensional attributes; distributed; deep learning; edge computing offloading.
  DOI: 10.1504/IJSNET.2025.10073899