International Journal of Sensor Networks (16 papers in press)

Regular Issues

• Optimizing Power Management in Wireless Sensor Networks Using Machine Learning: An Experimental Study on Energy Efficiency  
  by Mohammed Amine Zafrane, Ahmed Ramzi Houalef, Miloud Benchehima 
  Abstract: Wireless sensor networks (WSNs) have emerged as essential components across various fields. Comprising small, self-sustaining devices known as "Nodes," they play a critical role in data collection and analysis. However, ensuring optimal longevity without compromising data collection timeliness is a fundamental challenge. Regular data aggregation tasks, while essential, consume substantial energy resources. Furthermore, constraints in computation power, storage capacity, and energy supply pose significant design challenges within the Wireless Sensor Network domain. In pursuit of optimizing energy efficiency and extending the operational lifetime of nodes through artificial intelligence, we have developed a prototype for data collection to create a comprehensive dataset. Our approach leverages both current and precedent measurements, triggering data transmission only in the presence of significant changes. This intelligent strategy minimizes unnecessary communication and conserves energy resources. Based on the
  Keywords: Artificial intelligent; WSN; Power optimization; data acquisition.
  DOI: 10.1504/IJSNET.2024.10068162
   
  
• A 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
   
  
• 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
   
  
• 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
   
  
• 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
   
  
• 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.
  DOI: 10.1504/IJSNET.2025.10070349
   
  
• 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.
  DOI: 10.1504/IJSNET.2025.10070353
   
  
• ESFM-Net: Low-dose CT Artifact Suppression Method based on Edge-Guided Spatial-Frequency Mutual Network  
  by Xueying Cui, Weisen Song, Xiaoling Han, Lizhong Jin, Hong Shangguan, Xiong Zhang 
  Abstract: Deep learning has shown superior performance in low-dose CT artefact suppression. However, the existing deep network has a limited perception of edge and texture information, and the transformer-based approaches have high computational complexity in calculating self-attention. To alleviate these issues, an artefact suppression method based on an edge-guided spatial-frequency mutual network (ESFM-Net) is proposed, which can enhance edge and texture information with low computational complexity. Specifically, an edge decoder is designed to supplement multi-scale edge details for reconstructing high-frequency in a single-encoder dual-decoder. For obtaining rich edge and texture information, a spatial-frequency feature extraction module is developed to obtain local spatial and global frequency features with little computational complexity. Considering the complementarity of information, a spatial-frequency mutual module is further constructed to enhance the feature representation capability by adaptive fusion. High and low-frequency features with different scales are also gradually fused through a multi-scale fusion module to obtain final denoised images. The comparative experiments and ablation results show the superior performance of our method in edges, texture preservation, and artefact suppression.
  Keywords: Low Dose CT; Artifact suppression; Edge guidance; Spatial-frequency mutual fusion.
  DOI: 10.1504/IJSNET.2025.10070503
   
  
• A Dynamic Context-Based Routing Protocol for Effective Packet Forwarding in Opportunistic Network  
  by Amit Dutta, Satya Jyoti Borah, Jagdeep Singh, Kekuokho Mor 
  Abstract: Opportunistic networks (OppNets) pose unique challenges and difficulties in message forwarding due to node mobility and intermittent connectivity. The major challenge in OppNets is the selection of a suitable neighbor node to carry-forward data packets from the source to the destination node. To address this, a novel routing protocol, termed as Dynamic Context-based Routing Protocol for Effective Packet forwarding in OppNet (DCRP), is proposed, which considers contexts of a node such as frequency of encounter, mean contact duration, mean inter-contact time, distance, buffer space, packet forwarding rate, and energy. The simulations using the Opportunistic Network Environment (ONE) simulator show that DCRP demonstrates better routing capabilities compared to other benchmark protocols like EBC, EDR, and Epidemic, achieving lower average latency, reduced hop counts, minimum messages dropped, and improved buffer time averages, making it a better solution for efficient message routing in opportunistic networks.
  Keywords: Opportunistic Networks; Context Aware; Routing Protocol; Information Centric; Security; Inter Contact Time; ONE Simulator; Real Mobility Data Trace.
  DOI: 10.1504/IJSNET.2025.10070711
   
  
• PULP-Lite: a More Light-weighted Multi-core Framework for IoT Applications  
  by Yong Yang, Yuyu Lian, Yanxiang Zhu, Shun Li, Wenhua Gu, Ming Ling 
  Abstract: The increasing volume of data generated by real-time applications and sensors places significant performance demands on processors. Single-core processors are constrained by the inherent limitations of their architecture in terms of parallel processing capability, making it challenging to handle real-time applications. To address this, we propose parallel ultra low power lite (PULP-Lite), a tightly coupled multi-core on-chip system to efficiently handle near-sensor data analysis in the Internet of Things endpoint devices. PULP-Lite uses low-latency interconnections and an innovative address-mapping mechanism to connect central processing units (CPU), ensuring high-performance processing while maintaining flexibility with a lightweight multi-core programming. We evaluate a field-programmable gate array (FPGA) implementation of PULP-Lite with 8 cores, showing a speedup of 6
  Keywords: multi-core optimization; parallel processing; computer systems organization.
  DOI: 10.1504/IJSNET.2025.10070743
   
  
• 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
   
  
• Spectral-Energy Efficient Resource Allocation for Multi-User RIS-FD-MIMO Systems using Deep Learning  
  by Charanjeet Singh 
  Abstract: Reconfigurable Intelligent Surfaces (RIS) and Full-Duplex (FD) communications have emerged as promising technologies to enhance the performance of Multiple Input Multiple Output (MIMO) systems. However, there are several methods for optimal resource allocation, accurate prediction of resource requirements, and efficient allocation that remain challenging. To overcome these issues, this paper introduces a resource allocation technique by considering spectral efficiency (SE) and energy efficiency (EE). The proposed Double Attention-based Dilated LSTM is first used to predict resources. The proposed Improved Pufferfish Optimization (IPufO) Algorithm is used to allocate resources optimally based on the predicted resource. The proposed IPufO is designed to integrate the chaotic Chebyshev mapping within the conventional pufferfish algorithm to enhance performance. Furthermore, the proposed DA_DiLSTM+ IPufO model improves performance by taking SE and EE into account when allocating resources. The maximum EE and SE determined by the proposed technique are 218.531 bits/Joule and 98.8521 bits/Hz/cell, respectively.
  Keywords: Pufferfish Optimisation; Long Short-Term Memory; Resource Prediction; Spectral Efficiency; Energy Efficiency; Full Duplex; Dilated Deep Learning.
  DOI: 10.1504/IJSNET.2025.10070911
   
  
• A Data Validity and Energy Efficiency Sensitive Method for In-Node Multi-Parameter Collaborative Sensing in Internet of Things for Agriculture  
  by Zhaokang Gong, Xiaomin Li, Rihong Zhang, Yongxin Liu 
  Abstract: In the agricultural IoT, there are problems such as low data value and high energy consumption. To this end, a cloud-edge intelligence-supported intra-node multi-parameter collaborative snesing strategy that is sensitive to data validity and energy efficiency is proposed. Firstly, a node intra-node data sensing framework supported by cloud-edge intelligence is proposed, a mathematical model of multi-parameter data sensing tasks is established, and evaluation indicators such as energy efficiency are designed. Secondly, an in-node multi-parameter data sensing based on correlation is proposed. The correlation between tasks and parameters is analysed using grey correlation to quantify the data value and validity of parameters. Using edge intelligence technology, the intra-node sensors are optimised with high value data as the target. Simulation experiments show that this method is superior to traditional methods in data value density, data validity rate and energy efficiency, with improvements of 100%, 20.59% and 300% respectively.
  Keywords: Multi-parameter collaborative sensing; Data validity; Energy efficiency; Internet of Things.
  DOI: 10.1504/IJSNET.2025.10070952
   
  
• A Feature-Driven Approach for ADAS Using Real-Time Smartphone Inertial Sensor Data  
  by Sakshi ., M.P.S. Bhatia, Pinaki Chakraborty 
  Abstract: With rapid advancement of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, ensuring safe interaction between human drivers and ADAS system has become a critical challenge for accurately predicting driver behaviour. The study addresses the problem of developing robust systems, with limited raw features from smartphone Inertial Measurement Unit (IMU) sensors. This study proposes a feature-driven approach to predict driver behaviour using smartphone IMU data. The objective is to enhance prediction accuracy by utilizing two novel features namely, Statistical deep features and Cross-correlated features. These features capture more intrinsic patterns in driver behaviour and improves the performance. The proposed methodology was experimented on three benchmark datasets with machine learning models like Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Using cross- correlated features, accuracies of 99%, 100%, and 99% were obtained. This demonstrates that this approach outperforms existing methods, capturing detailed patterns and providing more reliable predictions in complex traffic.
  Keywords: Driving Behaviour; Deep learning; Signal Data; Autonomous Driving; Feature Engineering; Inertial Sensors.
  DOI: 10.1504/IJSNET.2025.10071148