International Journal of Sensor Networks (20 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
   
  
• ToI-Model: Trustworthy Objects Identification Model for Social-Internet-of-Things (S-IoT)  
  by Rahul Gaikwad, Venkatesh R 
  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 models 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; and Excellence.
  DOI: 10.1504/IJSNET.2025.10070868
   
  
• 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.
  
  
• Nonlinear Least Squares-Based Localisation Method for WSN-based Smart Agriculture Systems using Range Measurements  
  by Emad Hassan 
  Abstract: Accurate source localisation in Wireless Sensor Networks (WSN) is critical for applications requiring precise target tracking, environmental monitoring, and security surveillance. Traditional localisation techniques suffer from multipath interference, leading to degraded accuracy. This paper proposes an enhanced localisation algorithm leveraging multipath exploitation to improve position estimation. The proposed approach utilises time difference of arrival (TDOA) and direction-of-arrival (DOA) measurements, incorporating a hybrid scheme that can mitigate noise and enhance accuracy. A space division multiple access (SDMA) spread spectrum receiver is employed to extract DOA estimates, while TDOA information is utilised to differentiate between line-of-sight (LOS) and non-line-of-sight (NLOS) components. By associating multipath signals with corresponding reflectors, the scheme significantly improves localisation performance, even in environments where LOS paths are obstructed. Simulation results demonstrate that the proposed scheme significantly improves localisation accuracy compared to conventional schemes. The root mean square error (RMSE) is reduced by 30%, and the overall localisation success rate is increased by 25%, showcasing the robustness of the proposed scheme. These findings suggest that integrating multi-path components constructively rather than treating them as interference can enhance WSN localisation performance, making it suitable for real-world deployment.
  Keywords: WSNs; source localization; DOA; NLOS; TDOA; smart irrigation systems.
  DOI: 10.1504/IJSNET.2025.10072995
   
  
• A Blockchain-Based Privacy Protection Model for a Spatial Crowdsourcing Platform  
  by Amal Albilali, Maysoon Abulkhair, Manal Bayousef 
  Abstract: Spatial crowdsourcing (SC) involves collecting geographic information from a crowd of people using mobile devices, raising critical privacy issues regarding participants' location data. In this article, we propose an efficient privacy protection task assignment model (ePPTA) as a novel method that combines centralised and decentralised platforms to achieve privacy protection for worker location, worker identity, and task location during the task assignment (TA) process. Through a centralized SC platform, we achieve privacy protection using an elliptic curve cryptography (ECC), ensuring low user computational and communication overheads. The task assignment process and its data integrity are managed via blockchain technology. We evaluate our model on a real dataset, comparing it with state-of-the-art methods. The ePPTA model demonstrates low user computational and communication overheads and theoretically prevents task-tracking and eavesdropping attacks from external entities. Performance evaluation results confirm that the proposed model's efficiency is reasonable, providing robust privacy protection for SC.
  Keywords: Crowdsourcing; Privacy; Location Privacy; Spatial Crowdsourcing (SC); Blockchain.
  
  
• 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
   
  
• OCH-MAC: an Optimised Channel Hop MAC Protocol with Dynamic Blacklist for Wireless Sensor Networks  
  by Vandenberg B. Da Paixao, Renato De Moraes 
  Abstract: This paper presents two sets of algorithms for optimizing data exchange from a sensor network's medium access control (MAC) perspective. The Optimized Channel Hopping MAC (OCH-MAC) seeks that, in case of the inoperability of the current communication channel in use, the next chosen channel presents the best conditions of the spectrum because the communicating sensor pair employs a quality scale for each available channel. Accordingly, we propose a dynamic blacklist (D-Blacklist) algorithm, where three factors determine the blocking or unblocking of the channel: the analysis of the previous and current signal-to-noise plus interference ratio (SNIR) records, the SNIR levels of the active and neighboring channels, and the seasonality in the SNIR variations, analyzing the current record and comparing it with the recorded minimum and maximum. Results show that these algorithms enhance the reliability of blocking or unblocking the channel and outperform other sensor technologies, such as WirelessHART, ISA100.11a, and IEEE802.15.4e/A-TSCH.
  Keywords: Channel blacklist; MAC protocols; sensor networks.
  DOI: 10.1504/IJSNET.2025.10073277
   
  
• 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
   
  
• 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: To address the problem that computing task offloading cannot dynamically schedule the service environment according to the task preferences of different regions, we implement real-time scheduling of service environments and computing tasks based on container technology. This approach can dynamically meet the service demands of users in different regions. In order to solve the coupling problem of the scheduling process and the replacement problem of storage content, we integrate task offloading, service environment migration, and storage content replacement into a task scheduling action. Additionally, we design an attention-based decentralised-actor centralised-critic network to address this problem. The proposed algorithm uses a centralised training and distributed execution framework and optimises the policy learning process by introducing an attention mechanism. Simulation experiments demonstrate that the proposed algorithm can effectively improve the load balancing level of the multi-edge collaborative system and achieve a lower average task execution delay.
  Keywords: Multi-Agent Reinforcement Learning; Edge Computing; Task Scheduling; Edge Computing; Migratable Service Environment.
  DOI: 10.1504/IJSNET.2025.10073784
   
  
• 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 modeling 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 realize 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
   
  
• A Dynamic Sensing-Based, Adaptive Energy-Efficient Transmission Scheme for Wireless Sensor Networks in Perishable Goods Monitoring  
  by Li Chen 
  Abstract: The cold chain is a temperature-controlled supply chain crucial for preserving perishable goods such as pharmaceuticals by maintaining low temperatures throughout storage and transport. Wireless sensor networks play a crucial role in monitoring these conditions in real-time. However, their performance is hindered by harsh environments, limited node energy, and dynamic channel conditions, which result in a shortened network lifetime and unreliable transmissions. Existing transmission schemes often employ static parameters and lack adaptability, leading to energy waste and poor reliability under fluctuating cold chain conditions. To overcome these limitations, this paper proposes an adaptive energy-efficient transmission scheme based on dynamic sensing. The core approach involves constructing a sensing model to quantify key parameters and designing a fuzzy logic-based adaptive transmission algorithm. Experimental results demonstrate that the proposed method reduces energy consumption by up to 32%, extends network lifetime by 40%, and maintains a data transmission success rate of over 98%, outperforming conventional methods.
  Keywords: wireless sensor networks; dynamic sensing; adaptive transmission; energy efficiency.
  DOI: 10.1504/IJSNET.2025.10073900
   
  
• Stochastic Network Calculus based Energy Harvesting Model for Underwater Agriculture  
  by Vignesh S. R, 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: UWSN; Energy Harvesting; SNC; Energy Storage Constrain; Sensor Node.
  DOI: 10.1504/IJSNET.2025.10073911
   
  
• 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
   
  
• WSN-Based Distributed Collaborative Sensing for Consistency Evaluation of a Photovoltaic Energy Storage Battery Cluster  
  by Yujin Xiang, Chen Liang, Chen Yuan, Wujun Kui, Guiwen Zhang 
  Abstract: Photovoltaic energy storage systems typically convert solar energy into electricity via photovoltaic cells. However, in practical applications, parameter variations among individual cells within battery clusters often lead to uneven energy distribution and reduced system efficiency. Simultaneously, the inherent intermittency and volatility of photovoltaic power generation exacerbate challenges in maintaining stable energy supply. To address this, this paper first designs an energy-sensing unit and corresponding sensing strategy. By adopting a hybrid storage architecture combining photovoltaic cells and supercapacitors, the system enhances transient energy buffering capabilities and reduces power conversion losses. Furthermore, a mathematical model for wireless sensor network energy scheduling constrained by battery cluster consistency was established and solved using a multi-layer iterative decoupling optimization method. Experimental results demonstrate that the proposed method achieves a 98.3% energy utilization rate, effectively mitigates the impact of photovoltaic storage fluctuations on the network, and significantly enhances system energy efficiency.
  Keywords: wireless sensor network; photovoltaic energy storage batteries; energy harvesting; dispatch optimization; iterative decoupling.
  DOI: 10.1504/IJSNET.2025.10074717
   
  
• 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