International Journal of Sensor Networks (18 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 Relational Triplet Extraction Method for Constructing Network Security Knowledge Graph  
  by Guanlin Chen, Jiacong Xu, Tieming Chen, Wujian Yang, Wenyong Weng 
  Abstract: Faced with the challenges brought by the rapid growth of cyber threat intelligence (CTI) data, traditional information extraction methods have shown limitations regarding efficiency, accuracy, intelligence, and scalability. To help network security experts develop more solid security strategies based on reliable intelligence and improve network security defence and deterrence capability, this paper focuses on constructing a CTI knowledge graph based on a relational triplet. Besides, this paper provides a ternary extraction method for constructing a network security knowledge graph associated with a sensor system, which reduces the labour consumption of constructing a network security knowledge graph. Compared with the traditional method, the method is more efficient and accurate and can improve the performance of extracting entity relationships from complex text.
  Keywords: network security; knowledge graph; sensor; named entity recognition; NER; relationship extraction.
  DOI: 10.1504/IJSNET.2024.10068843
   
  
• Data Acquisition Systems for Alternating Current Switch Machine Prediction and Health Management  
  by Xiongsheng Wu, Hanqing Tao 
  Abstract: Alternating current (AC) switch health predictive maintenance is crucial for reducing downtime and improving efficiency. The system analyses operational data like pressure, temperature, vibration, and voltage to predict potential failures using various learning techniques. However, it faces challenges such as slow convergence, suboptimal accuracy, and high computational costs. These issues are addressed by the optimised neural model (ONM), which employs a sequence-to-sequence neural model and grasshopper optimisation. Data is processed through windowing and lag feature procedures, followed by feature engineering to extract domain-specific statistics. The optimised algorithm fine-tunes parameters and captures temporal dependencies, achieving 98.56% accuracy and a loss function of 0.012. This enhances prediction robustness and reliability, ultimately optimising maintenance schedules and operational efficiency.
  Keywords: AC switch machine; predictive maintenance; optimized neural model; windowing; lag features; exploration-exploitation; robustness; and reliability.
  DOI: 10.1504/IJSNET.2024.10068923
   
  
• Fault Diagnosis for Wireless Sensor Networks based on Belief Rule Base with Attributes Computing  
  by Ming Yang, Guanghai Li, Jie Wang, Siyang Gao, Guanyu Hu, Manlin Chen 
  Abstract: Due to the law and mechanism of wireless sensor networks, faults are complex and changeable. Wireless sensor network fault information contains many various types of uncertain information. Wireless sensor network fault diagnosis has attracted extensive attention in wireless sensors. Therefore, this paper suggests a fault diagnosis method based on a belief rule-based expert system. By analyzing the sensor data characteristics of wireless sensor networks, the fault diagnosis of wireless sensor networks can be studied from time, space, and attribute. The antecedent attribute of the belief rule-based expert system can be constructed on this basis. Finally, a fault diagnosis model for wireless sensor networks based on belief rule base and attribute computation is proposed. An Intel Labs sensor dataset case validates the proposed model's effectiveness. The results reveal that the approach can diagnose wireless sensor network issues well.
  Keywords: Fault Diagnosis; Wireless Sensor Networks; Belief Rule Base.
  DOI: 10.1504/IJSNET.2024.10069385
   
  
• A Dynamic Routing Algorithm for VANET with Graph Neural Networks and Deep Reinforcement Learning  
  by Xiang Bi, Lingjie Huang, Benhong Zhang, Zhen Chen, Zengwei Lyu 
  Abstract: In Vehicular Ad-hoc Networks (VANET), direct vehicle-to-vehicle communication provides a necessary supplement to the data transmission of intelligent transportation systems. However, the transient and volatility of VANET topology bring challenges to establishing an efficient and reliable end-to-end routing. To this end, a dynamic routing algorithm for VANET integrating graph neural networks and deep reinforcement learning is proposed. Firstly, the network topology is delineated from VANET according to the routing request, and graph features are extracted based on the routing establishment objective. Then, the routing relay selection problem is modelled as a Markov decision process, and the network topology information is learned using graph neural networks and solved using a deep Q-learning algorithm framework. Specifically, in order to better evaluate actions, a new fuzzy logic-based reward function is present. Simulation results show the algorithm has better performance in terms of average end-to-end delay, hop count and packet delivery rate compared to other algorithms.
  Keywords: VANET; routing; multi-objective optimization; fuzzy logic; graph neural networks; deep reinforcement learning.
  DOI: 10.1504/IJSNET.2024.10069476
   
  
• Hybrid Feature Extraction and Enhanced Intrusion Detection Classification in Industrial Control Networks  
  by Hanlin Chen, Entie Qi, Jin Si, Hui Yan, Tong Zhou 
  Abstract: In the rapidly developing industrial ecosystem, more and more malicious security attacks against industrial control come one after another. To address growing threats effectively, intrusion detection is essential in the multi-layer defense of communication networks. It helps prevent network attacks, policy violations, unauthorized access, and other security issues. It is very important to integrate this technology into the Industrial Control Network. For anomaly identification, deep inspection of packets is required to extract appropriate features to identify attacks. Data usage and demand in industrial control networks are increasing daily; accurate anomaly detection with low data testing and training time is still challenging. This paper uses a hybrid feature extraction model consisting of a chi-square test, AutoEncoder, and Principal Component Analysis. This paper presents a hybrid feature extraction-based intrusion detection model enhanced by a deep neural network.
  Keywords: Industrial control network; deep neural network; feature extraction; chi-square test; autoencoder; principal component analysis; intrusion detection.
  DOI: 10.1504/IJSNET.2025.10069603
   
  
• Buffer Management Approach for Efficient 6TiSCH Network Formation  
  by Remli Mohamed, Oualid Demigha, Ali Yachir 
  Abstract: IETF IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) is an open communication protocol stack tailored for low-power wireless networks in the Industrial Internet of Things (IIoT). 6TiSCH recommends using a single shared cell to transmit all generated control packets while prioritising the Enhanced Beacon (EB) frame transmission. However, these recommendations result in increased contention, high buffer occupancy, and significant delays for lower-priority packets. Consequently, this leads to extended formation time and increased energy consumption. We analyse this issue through simulations and a queuing theory model and propose a dynamic scheduling approach for shared cells. The proposed scheme shortens packet waiting time and alleviates buffer occupancy, by dynamically allocating multiple cells, enabling faster synchronisation and formation with lower energy consumption. Our approach is evaluated through Cooja simulations and implemented in a real testbed using FIT IoTLab. By comparing our results with recent approaches, we validate the effectiveness of our proposed scheme.
  Keywords: IEEE 802.15.4e; TSCH mode; 6TiSCH-MC; Network Formation; Queuing Theory; Buffer Management.
  DOI: 10.1504/IJSNET.2025.10069629
   
  
• FLVAMatch: an Economic Matching Game Theory-Based Vehicle Selection in Federated Learning for the Next-Generation of Internet of Vehicles  
  by Jai Vinita, Vetriselvi V 
  Abstract: The future generation of the internet of vehicles (IoV) in intelligent transportation systems (ITS) leverages advanced communication and intelligent data analysis. Federated learning (FL) enhances IoV privacy, but conventional random-based vehicle selection for local training is inefficient due to varying client resources and data quality. Existing methods prioritise server preferences, overlooking FL-vehicle incentives. To address this, we formulate a profit maximisation problem to enhance FL-vehicle revenues and propose FLVAMatch, an economic framework based on the hospital-resident (H-R) matching problem. In a three-layer software-defined vehicular fog (SDVF) computing setup, FLVAMatch considers both FL-vehicle and aggregator preferences. Simulation results demonstrate its effectiveness in maximising FL-vehicle revenues and reducing network latency, outperforming state-of-the-art FL approaches, with the global model achieving 88% accuracy.
  Keywords: Internet of Vehicles; Fog Computing ; Federated Learning ; Vehicle Selection; Matching Game Theory.
  DOI: 10.1504/IJSNET.2025.10069720
   
  
• FSFDS: Enhancing Flight Sensor Fault Diagnosis via Diffusion and Self-Attention Networks  
  by Jiaojiao Gu, Ping Gao, Xue Li, Bei Hong, Tao Sun 
  Abstract: Aircraft rely on multiple flight sensors for fault diagnosis, critical for safe operation. Deep neural networks (DNNs) have shown success in various fields, including fault prediction systems. However, current implementations face challenges: 1) DNN-generated fault data significantly differ from actual data, and 2) existing DNN-based models achieve suboptimal accuracy. This paper proposes a flight sensor fault diagnosis system (FSFDS) based on diffusion and attention networks. We enhance data quality by improving a diffusion model with a scoring function for error reduction. Generated data is manually annotated and used to train a diagnostic model with a weight-sharing multi-twin neural architecture and attention mechanisms, effectively capturing parameter relationships from time series. The FPGA-deployed model achieves high energy efficiency. Experiments show the proposed method reduces errors by 0.551, improves diagnostic accuracy by 15.3%, and achieves inference speeds 14.23
  Keywords: flight sensor; diagnosis system; diffusion; self-attention; FPGA.
  DOI: 10.1504/IJSNET.2025.10069837
   
  
• Automatic Detection of Rectal Cancer Lesion Images Based on the Improved MR-U-Net Deep Learning Network  
  by Fang Wu, Yihua Gu, Haifei Zhang 
  Abstract: Traditional image processing methods are prone to segmentation and overlap when processing medical images of rectal cancer, which makes it difficult for algorithms to distinguish surrounding tissues, organs, and lesions accurately. In addition, the small size of the cancerous area may also lead to errors and inaccuracies in the segmentation results. This study proposed an enhanced mechanical residual U-shaped network (MR-U-Net) model for automatically detecting rectal cancer lesions in computed tomography (CT) images to address these issues. The model achieves high segmentation accuracy by combining a deep supervision mechanism with attention guidance. Experimental results showed a significant improvement in the dice coefficient and other indices compared to the baseline U-shaped network (U-Net) model. After comparing the corresponding index values, it can be seen that the designed system has achieved accurate segmentation of lesions in general. At the same time, we also hope to achieve better results by continuously improving research directions in the future.
  Keywords: deep learning; medical image segmentation; MR-U-Net; attention mechanisms; In-depth monitoring mechanism.
  DOI: 10.1504/IJSNET.2024.10069914
   
  
• 6G-Enhanced Context-Aware Systems in Adaptive Ubiquitous Learning Environments for Music Education via Edge Intelligence  
  by Hua Wei, Jianhui Lv, Adam Slowik 
  Abstract: This study proposes an enhanced wireless 6G communications architecture for context-aware systems to enable adaptive u-learning environments for music education. A centralized data processing center at edge nodes analyzes user behaviors and network conditions to enable coordinated control across the core network, transport network, and radio access network, which fully exploits the feature of edge intelligence. The architecture supports self-consistent capabilities within each network function entity and flexible multi-level couplings between entities based on real-time user needs. For radio resource management, an AI-driven intelligent controller is introduced to enable intelligent and automated management of wireless resources. Experiments compared learning effectiveness between groups with and without the proposed enhanced 6G context-aware capabilities in an adaptive u-learning music learning environment. Results demonstrated significantly improved task completion times and learning accuracy with the 6G-enhanced context-aware system in adaptive u-learning environments for music education via edge intelligence.
  Keywords: 6G; Edge computing; Context-aware; Ubiquitous learning; Music education.
  DOI: 10.1504/IJSNET.2025.10070024
   
  
• Named Entity Recognition for Function Point Descriptions in Software Cost Estimation Processes  
  by Boyan Zhao, Xiaofei Zou, Shijie Xin, Di Liu 
  Abstract: With the advancement of software technology, the industrys informatisation level has improved, but the growing size and complexity of software have raised costs. Consequently, assessing software project costs early is crucial. Function point analysis, the primary method for cost evaluation, quantifies functional elements like external data inputs and outputs to measure software size from the users perspective. However, it heavily relies on manual effort, especially in extracting function point descriptions, leading to errors and inefficiency. This paper proposes an entity recognition model to address these challenges, integrating a BiLSTM-CRF framework with CNN layers and hierarchical learning. A domain-specific dictionary is developed to enhance the models performance. Experimental results show that the proposed method outperforms BERT by improving accuracy by 0.42% and recall by 1.04%. The method achieves 95.37% accuracy in entity recognition for a sensor data system, demonstrating its effectiveness and reliability in software cost evaluation.
  Keywords: Software cost evaluation; Named entity recognition; Convolutional neural network; Bidirectional long-short memory network; Hierarchical learning.
  DOI: 10.1504/IJSNET.2025.10070067
   
  
• Delay Optimisation using Intelligent Omni-Surfaces (IOS)  
  by Faisal Alanazi 
  Abstract: In this paper, we compute the total delay and the waiting time for wireless networks using intelligent omni-surfaces (IOS). The delay and waiting time using IOS have not been yet derived and is the main objective of this paper. IOS phase shifts are optimised to maximise the signal to interference plus noise ratio (SINR) at users Ut and Ur located at the transmission and reflection spaces of IOS. The diversity of IOS is equal to the number of its elements N that can be very high up to N = 1,024. We also increase number of reflectors to minimise the total delay. Delay minimisation using IOS has not been yet proposed.
  Keywords: Delay analysis and optimization; Intelligent Omni-Surfaces (IOS); 6G networks.
  DOI: 10.1504/IJSNET.2025.10070070
   
  
• Multi-Tier Ensemble based Approach for Threat Detection in IoT Security  
  by Sriram Parabrahmachari, Srinivasan Narayanasamy 
  Abstract: With the rise of IoT devices, security vulnerabilities have increased, making traditional measures inadequate. Soft computing and machine learning offer adaptive threat detection by identifying patterns and anomalies. This paper presents a hierarchical ensemble-based approach for IoT security, where multiple classifiers collaborate to detect various threats. Trained on diverse datasets, these classifiers enhance accuracy through ensemble learning. Evaluated on a public dataset, the proposed approach outperforms state-of-the-art methods in accuracy, precision, and recall. It ensures high detection rates with low computational costs, making it a promising solution for securing IoT networks against cyber threats.
  Keywords: Threat Detection System; IoT Security; Soft Computing; Machine Learning; Hierarchical Ensembling.
  DOI: 10.1504/IJSNET.2025.10070156
   
  
• Advanced Air Quality Prediction Modelling using Intelligent Optimisation Algorithm in Urban Regions  
  by Wendi Tan, Zhisheng Li 
  Abstract: Ambient air contamination is a significant environmental challenge threatening human well-being and quality of life, especially in urban areas. Technical breakthroughs in artificial intelligence models offer more accurate air quality predictions by analysing significant data sources, including meteorological factors like humidity, wind speed, and pollution data. Also, existing methods of air quality prediction often lack due to their dependency on statistical models that may not adequately capture the complexities of environmental data like pollutants and meteorological factors. Thus, the research introduces a grey wolf optimised variational autoencoder to enhance the air quality prediction by effectively capturing complex relationships in environmental data. The model acquires the probabilistic nature of variational latent representations from historical air quality input data and prevents overfitting. The relevant features are selected using the grey wolf technique, identifying the appropriate variables to enhance the data quality. Additionally, it optimises critical hyperparameters like learning rates and greedy layer sizes, leading to better convergence during model training and improved performance in air quality index prediction. Experimental results demonstrate improved prediction accuracy, reduced error rate, and faster convergence.
  Keywords: Air Quality Index; Prediction Model; Optimization; Artificial Intelligence; Urban Region; Environmental factors.
  DOI: 10.1504/IJSNET.2025.10070252
   
  
• Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces with RF energy harvesting  
  by Majed Abdouli, Eman Bouazizi 
  Abstract: Reconfigurable Intelligent Surfaces (RIS) represent a transformative approach to wireless communication, enabling enhanced signal transmission and reception through programmable reflective elements. This study explores the concept of simultaneously transmitting and reflecting (STAR) RIS integrated with radio frequency (RF) energy harvesting capabilities. By leveraging the dual functionalities of RIS, we propose a novel framework that not only improves communication efficiency but also supports sustainable energy management in wireless networks. Through theoretical analysis and simulations, we demonstrate the performance advantages of STAR RIS in terms of coverage, spectral efficiency, and energy sustainability. Our findings indicate that integrating RF energy harvesting with STAR RIS can significantly enhance the overall performance of future wireless communication systems.
  Keywords: STAR RIS; Energy Harvesting; throughput maximization.
  DOI: 10.1504/IJSNET.2025.10070254
   
  
• LSB-DSN: Sensor-Assisted Deep Learning for Robust English Speech Recognition  
  by Aili Tang, Dezi Zeng 
  Abstract: In modern communication, the English speech recognition system is essential for improving the personalised user experience and global communication. The recognition systems use sensor devices and deep learning techniques to ensure the system's robustness in all diverse environments. The traditional system efficiency is reduced due to accents, varying pronunciations, and limited contextual considerations. The paper introduces the lion-swarm boosted deep sesame networks, a new speech recognition framework that fuses sensor technologies and deep learning to improve accuracy and robustness. This model combines acoustic signals with sensor-based inputs from accelerometers, gyroscopes, and electromyography devices to capture the delicate speech-related modulations for better recognition across diversified environments. The hierarchical attention mechanism and lion swarm optimisation enable optimal feature selection, reducing the recognition error and computational overhead. The experiments show that it achieves a 9.5% word error rate in clean conditions, a low cross-entropy loss of 0.65, and 100 ms of processing latency far superior to baseline models for noisy environments. The proposed framework can adapt to different accents and pronunciations, making it a strong solution for real-world applications in speech recognition.
  Keywords: English Speech Recognition; signal modulation; sensor devices; subnetworks; lion swarm optimization; similarity measures; deep sesame networks.
  
  
• Security using Reconfigurable Intelligent Surfaces and Wind Energy Harvesting  
  by Faisal Alanazi 
  Abstract: Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology for enhancing wireless communication performance by intelligently manipulating the propagation environment. When integrated with wind energy harvesting, RIS can operate sustainably, making them an attractive option for remote and environmentally conscious deployments. This paper investigates the physical layer security of RIS-assisted communication systems powered by wind energy harvesting. Specifically, we evaluate the Secrecy Outage Probability (SOP) and the Strictly Positive Secrecy Capacity (SPSC) as key performance metrics to assess the system’s resilience against eavesdropping attacks. By modeling the random nature of wind energy and incorporating its impact on the RIS operation, we derive analytical expressions for SOP and SPSC under various environmental and system configurations. Our results reveal the interplay between wind energy availability, RIS configurations, and physical layer security, providing valuable insights into designing secure and sustainable RIS-based communication systems.
  Keywords: Wind energy harvesting; RIS; PLS; SOP; SPSC; Rayleigh channels.