International Journal of Sensor Networks (10 papers in press)

Regular Issues

• A Beamspace Channel Estimation based on Deep Convolutional Reconstruction Networks  
  by Teng Fei, Zhengyu Zhu, Jingyu Zhang, Lanxue Liu, Xinzong Yang 
  Abstract: One major challenge in millimetre-wave massive multiple-input multiple-output (MIMO) systems is achieving precise channel estimation, which still faces low accuracy and reliance on prior channel information. This paper proposes a novel beamspace channel estimation algorithm using a deep convolutional reconstruction network called DeRePixNet without requiring prior channel information. The multi-scale fusion module (MSFM) is designed to form a rich feature mapping in this network. MSFM and residual block (RB) are organically combined to prevent gradient vanishing while the network depth increases, to identify efficient local sparse structures in a convolutional visual network and replicate it spatially. The inverse transformation process from measurement vectors to the original channel is solved directly using DeRePixNet in a data-driven manner. We conducted theoretical derivations and system simulations based on the Saleh-Valenzuela channel model. The proposed DeRePixNet demonstrates superior performance compared to most existing methods. Compared to the orthogonal matching pursuit, approximate message passing learned approximate message passing, and Gaussian mixture learned approximate message passing algorithms, DeRePixNet reduces the average normalised mean squared error by approximately 11.14 dB, 8.95 dB, 1.98 dB, and 1.19 dB, respectively.
  Keywords: deep convolutional reconfiguration networks; millimetre wave; massive MIMO; channel estimation; multi-scale fusion module; MSFM.
  DOI: 10.1504/IJSNET.2024.10066759
   
  
• Channel Estimation in OFDM Systems based on the Mamdani Fuzzy Genetic Algorithm  
  by Lanxue Liu, Teng Fei, Jingyu Zhang, Zhengyu Zhu, Xiaolin Wang 
  Abstract: Utilising compressive sensing technology for channel estimation can effectively enhance the spectrum efficiency of orthogonal frequency division multiplexing (OFDM) systems. However, the computational efficiency of conventional sparse channel estimation algorithms is a concern, and their performance is highly dependent on the quality of the measurement matrix and the sparsity level of the channel. Metaheuristic algorithms, currently, are among the commonly used methods for solving optimisation and search problems. Based on the principles of compressive sensing theory, this paper introduces a novel algorithm, the Mamdani fuzzy genetic algorithm (MGA), for sparse channel estimation by incorporating metaheuristic algorithms. Under basic testing conditions, the MGA algorithm can overcome the drawbacks of excessive reliance on measurement matrices, performing well, particularly in low sparsity scenarios. Experimental results indicate that, compared to classical channel estimation algorithms, the proposed algorithm is more suitable for achieving estimation accuracy with lower pilot overhead.
  Keywords: Compressed sensing; Channel estimation; Metaheuristics; Orthogonal frequency division multiplexing.
  DOI: 10.1504/IJSNET.2024.10067585
   
  
• GM-YOLOV8-Based Safety Hazard Detection Method in Power Construction  
  by Entie Qi, Jialong Ge, Liying Zhao, Hongxia Ni, Cheng Li, Dianzhi Chen, Sinan Shi 
  Abstract: In power construction settings, the operation of heavy machinery and the risk of fire present substantial hazards to the safety of transmission lines. There is an urgent need for real-time surveillance of potential safety threats during the construction process. This paper proposes an generalised multi-scale-YOLOv8 (GM-YOLOv8) hazard detection algorithm. This algorithm introduces the reparameterised generalised feature pyramid network (RepGFPN) that improving the model’s capacity to capture overarching patterns and fine-grained details. It also introduces a multi-scale cross-axis attention module (MCA). This module enhancing the network's representational capabilities and improving the detection of distant hazards. Additionally, the adoption of the Powerful-IOU loss function, which includes a non-monotonic focus mechanism, enhances the model by adaptively penalizing object size and modulating gradients based on anchor box quality. Compared to a lightweight YOLOv8 model (YOLOv8n) algorithm, GM-YOLOv8 enhances detection precision by 5.3%, accuracy by 6.8%, and recall by 6.4%, ensuring improved safety in construction environments.
  Keywords: safety hazard detection; power construction; YOLOv8; reparameterised generalised feature pyramid network; RepGFPN; multi-scale cross-axis attention module; MCA; PIOU.
  DOI: 10.1504/IJSNET.2024.10067786
   
  
• A Student Performance Prediction Model Based on Multimodal Generative Adversarial Networks  
  by Junjie Liu, Yong Yang 
  Abstract: In recent years, blended learning has been widely applied in universities, introducing complex and diverse learning data. This study aims to use machine learning algorithms to extract useful information from this data for early student performance prediction. There are still some problems in current related research, including the neglect of short text data for online learning, data imbalance, and insufficient utilisation of multimodal data. To address the mentioned issues, this study proposes an innovative solution. Firstly, adjusting the generative adversarial network generators objective function solves data imbalance in student performance prediction, and the prediction ability for minority-category students is improved. Secondly, Using short text data from online learning to map the emotions of student learning states and enhance the models accuracy and generalisation ability. Finally, this study introduces a multimodal generative adversarial network performance prediction model, which achieves the fusion of multimodal data, improves the accuracy and comprehensibility of prediction.
  Keywords: hybrid teaching; performance prediction; multimodal; Generative Adversarial Network (GAN); short text sentiment.
  DOI: 10.1504/IJSNET.2024.10067900
   
  
• A Reinforcement Learning Algorithm for Mobile Robot Path Planning with Dynamic Q-value Adjustment  
  by Chang Hua, Hao Zheng, Bao YIqin 
  Abstract: Path planning is essential for mobile robots to execute various tasks across different fields, including intelligent systems. It primarily focuses on the interaction between the agent and its environment, allowing the agent to maximise total reward by an optimal strategy. Many path-planning algorithms that are not agent-based struggle with effectively exploring entirely unknown environments. To address these issues, we propose the Adam deep Q-learning network (ADQN) to solve such problems. ADQN introduces an innovative approach to choosing action and reward functions, optimising Q-value updates dynamically based on temporal-difference error changes for enhanced model convergence and stability. Evaluated across four simulations in two maze environments of varying complexities, ADQN shows significant improvements: reduced steps, increased rewards, faster and stable loss convergence, and notably higher success rates compared to Munchausen reinforcement learning, prioritised experience replay-double duelling deep Q-networks, max-mean loss in deep Q-network algorithms in grid-based experiments.
  Keywords: Adam deep Q-learning network; ADQN; path planning; agent; reward; selection strategy; Q-value.
  DOI: 10.1504/IJSNET.2024.10068014
   
  
• A Network Attack Traffic Identification Method for Power Sensor Network Based on Open-Set Recognition  
  by Wei Liu, Qigang Zhu, Xingshen Wei, Junjiang He, Qiang Zhang, Tian Jiang, Zeji Sun 
  Abstract: With the expanding range of services offered by power sensor networks, precise identification of network traffic is indispensable for ensuring network security management and prevention. While machine learning-based and deep learning-based network traffic identification technology has advanced considerably, it remains constrained to classifying predetermined categories. In real-world network environments, novel attack types can surface that the trained model has not accounted for. These unforeseen factors can significantly degrade the performance of existing methods, making them inadequate to navigate the complexities of network environments. We introduce a network attack traffic identification model within power sensor systems to address the above challenges, leveraging the autoencoder. By harnessing the intrinsic properties of the autoencoder alongside tailored thresholds, the model effectively accomplishes open-set recognition of network traffic. The experimental results highlight the model's strong performance in open-set recognition scenarios and confirm its effectiveness in power sensor applications.
  Keywords: Network Attack Traffic Detection; Open-Set Recognition; Power Sensor Network.
  DOI: 10.1504/IJSNET.2024.10068078
   
  
• 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
   
  
• RDBL-Net: Detection of Foreign Objects on Transmission Lines based on Positional Encoding Multiscale Feature Fusion  
  by Xiaoli Guo, Yifan Bao, Hao Jiang, Zichong Feng, Yuhan Sun 
  Abstract: Timely detection of foreign objects on transmission lines is the key to ensuring transmission lines safe and stable operation. This paper takes the dataset provided by the Guangzhou Pazhou Algorithm Competition as the data basis, proposing a transmission line foreign object detection model. First, a residual connection module combined with deformable convolution is introduced into the backbone, aiming to improve the feature extraction capability of the model for transmission line foreign objects. Second, a multiscale feature fusion structure is designed to enhance the fusion effect of multiscale features. Subsequently, a learnable position encoding is introduced into the multiscale feature interaction module to enhance the models ability to cope with complex environments interference. Finally, the effectiveness of the proposed method is demonstrated through experiments.
  Keywords: detection of foreign objects on transmission lines; RT-DETR; Deformable Convolution Network; multiscale feature fusion.
  DOI: 10.1504/IJSNET.2024.10068451
   
  
• 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