Forthcoming and Online First Articles

International Journal of Sensor Networks

International Journal of Sensor Networks (IJSNet)

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International Journal of Sensor Networks (12 papers in press)

Regular Issues

  • An Improved Sparrow Search Algorithm and Its Application in Wireless Sensor Node Coverage Problem   Order a copy of this article
    by Jianing Guo, Yunshan Sun, Ting Liu, Yanqin Li, Teng Fei 
    Abstract: Enhancing the sparrow search algorithm (SSA) to address the wireless sensor node (WSN) coverage problem requires resolving its challenges, including inadequate search precision, susceptibility to local optima, and premature convergence. The improved algorithm demands a more efficient deployment of sensor nodes to achieve network effectiveness and cost efficiency. This paper presents an improved sparrow search algorithm (ISSA). Primarily, the finders' exponential strategy prevents the algorithm from prematurely converging to the initial point. Furthermore, ISSA integrates addition and subtraction operations to bolster joiners' strategies, facilitating comprehensive exploration of near-optimal solutions. Moreover, by integrating multiplication and division operations into finders' and scouts' strategies, ISSA randomly refreshes the solution space and reduces the impact of population initialisation. In experiments, ISSA was compared with the original SSA, four new intelligent algorithms, and two improved SSAs across 23 standard test functions. Results indicate that the improvements proposed in this paper significantly enhance optimisation accuracy and speed. Additionally, ISSA optimised WSN coverage under three distinct test parameter sets. Observed growth rates were 15.40%, 7.17%, and 17.79%, respectively. These results underscore the enhanced algorithm's superior performance in addressing the WSN coverage problem compared to the original algorithm.
    Keywords: sparrow search algorithm; SSA; arithmetic optimisation algorithm; AOA; local optima; wireless sensor node coverage; WSN.
    DOI: 10.1504/IJSNET.2024.10064943
     
  • Improving Fault Diagnosis in Elevator Systems with GAN-Based Synthetic Data   Order a copy of this article
    by Xiaomei Lv, Zhibin Lu, Zhihao Huang, Zhanhao Wei 
    Abstract: Elevator maintenance and fault diagnosis are critical in ensuring reliable and safe operation. Elevator systems are complex electromechanical systems prone to various faults, such as sensor failures, motor malfunctions, and mechanical wear and tear. Detecting these faults promptly and accurately ensures elevators' safe and reliable operation. However, there is a lack of labelled data that may be used to train machine learning models, making it difficult to diagnose problems with elevators. This paper presents a novel approach for elevator fault diagnosis based on optimised generative adversarial networks (GANs). The proposed method employs a GAN model that generates synthetic data to augment the limited amount of labelled data and then trains a classifier on the augmented dataset. To improve the performance of the GAN, the authors introduce an optimisation algorithm that combines gradient ascent and descent, resulting in better-quality synthetic data. The efficiency of the system is evaluated using real-world elevator sensor data and compared its performance to traditional fault diagnosis methods. The results show that the proposed system can accurately diagnose faults with high accuracy and can potentially reduce maintenance costs and downtime. The proposed system provides a promising solution for elevator fault diagnosis, especially when labelled data is limited.
    Keywords: fault diagnosis; optimised generative adversarial networks; GANs; elevators; augmented dataset; and maintenance costs.
    DOI: 10.1504/IJSNET.2024.10066136
     
  • A Road Traffic Sign Recognition Method Based on Improved YOLOv5   Order a copy of this article
    by Lu Shi, Haifei Zhang 
    Abstract: With the rapid development of artificial intelligence technology, the automatic driving of intelligent vehicles has gradually entered people's lives. The traditional vision will fail in many scenarios, such as snow, lane line wear, occlusion, or haze weather. In addition, there are still errors in the identification of traffic signs, leading to missed and erroneous detection in the intricate road network of the city. This study aims to provide an accurate and efficient method for recognising traffic signs in their natural surroundings. This paper thoroughly explores the network architecture of YOLOv5 (You Only Look Once version 5) and the ideas underpinning its loss function, considering the limitations of the existing YOLOv5-based traffic sign recognition technology. It then modifies the YOLOv5 network model to enhance its performance. According to the most recent experimental data, the enhanced YOLOv5 model performs exceptionally well at recognising traffic signs in various natural settings.
    Keywords: deep learning; object detection; traffic sign recognition; YOLOv5.
    DOI: 10.1504/IJSNET.2024.10066756
     
  • Temperature and Humidity Monitoring and Communication System for Coal Mine Working based on LoRa   Order a copy of this article
    by Baofeng Zhao, Kaiyuan Zhu 
    Abstract: Aiming at the complexity of the underground coal mine environment and the safety of workers facing geothermal disasters, a LoRa-based communication system for real-time monitoring and control of temperature and humidity in the working environment of underground coal mines is studied and designed. Based on the introduction of the fair-access criterion of MAC protocol, the time division multiplexing protocol is adopted and improved, and the nodes complete the self-clock synchronisation after sending the information packets by redesigning the LoRa data frame structure. While adopting linear topology networking, LoRa relay nodes are adopted and designed for packet sensing, transmission and forwarding functions, and the distance arrangement of LoRa relay nodes is experimentally investigated. The experimental test proves that the system realises the expansion of the transmission distance and the improvement of the success rate of packet reception, which meets the wireless communication requirements in underground coal mines.
    Keywords: LoRa technology; wireless communication technology; coal mine environmental monitoring; Low power consumption.
    DOI: 10.1504/IJSNET.2024.10066757
     
  • A Beamspace Channel Estimation based on Deep Convolutional Reconstruction Networks   Order a copy of this article
    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
     
  • CNN-Based Lane-Level Positioning with Only On-Board Camera   Order a copy of this article
    by Li Chen, Liu Zhengqiong, Zhou Momiao, Sun Yanshi, Zhizhong Ding 
    Abstract: Lane-level positioning is a vital prerequisite for realising autonomous driving in complex scenarios. Existing methods for lane-level positioning mostly rely on the global positioning system (GPS) and vision-based approaches. Although the positioning accuracy of civil GPS can reach up to metre-level in an environment with good signal, it is hardly to meet the precision requirement for the lane-level vehicle's positioning in which centimeter-level precision is desired. Some traditional vision-based methods can achieve decimeter-level accuracy, they usually suffer from the weakness of low detection speed and the difficulty in handling multi-lane detection tasks. This paper proposes a one-camera low-cost approach that utilises convolutional neural network (CNN)-based segmentation for lane detection and traditional image processing techniques for lane determination. The effectiveness and robustness of the proposed approach have been tested and verified on the widely-used dataset TuSimple. It is shown that our method can achieve high detection speed while maintaining a certain detection accuracy.
    Keywords: lane-level vehicle’s positioning; lane detection; CNN; instance segmentation.
    DOI: 10.1504/IJSNET.2024.10067172
     
  • Energy Efficient Link-Delay Aware Data Forwarding in Low-Duty-Cycle LoRa Mesh based IoT Networks   Order a copy of this article
    by Gary Sun, Jing Zhang 
    Abstract: LoRa Mesh networks must consider network reliability, latency, and longevity to be successful, but it is often challenging to design energy-efficient and delay-sensitive data forwarding protocols. Many existing data forwarding protocols in mesh networks operate based on the quality of links and node forwarding capabilities. Ultimately, this can result in excessive delay and cause data packets to be relayed by a fixed number of nodes, leading to rapid energy depletion and earlier network disconnection. To address these problems, we propose End-to-End Delivery Ratio and Delay (E2EDRD), a metric that combines Internet of Things (IoT) devices’ duty cycles, link qualities, and remaining energies. Using a data forwarding protocol based on E2EDRD, each node decides on a set of data-forwarding candidates. Simulation results demonstrate that our proposed metric improves upon existing data forwarding protocols by achieving a desirable trade-off between End-to-End Delay (E2ED), Packet Delivery Ratio (PDR), and network lifetime.
    Keywords: IoT; LoRa Mesh; Low-Duty-Cycle.
    DOI: 10.1504/IJSNET.2024.10067288
     
  • Efficient Connectivity Analysis in Underwater Wireless Sensor Networks: A Polynomial-Time Solution for the Connectivity between Nodes   Order a copy of this article
    by Youssef Altherwy 
    Abstract: Underwater Wireless Sensor Networks (UWSNs) are a focus of research due to challenges in the unpredictable underwater environment. This study delves into connectivity among sensor nodes, particularly the likelihood of communication between nodes adrift with water currents, termed the Two- Nodes connectivity (2Nodes connectivity) problem. Highlighting the computational complexity (2Nodes connectivity is #P-Hard), we propose an innovative polynomialtime approximation algorithm, namely the 2Nodes connectivity algorithm. The algorithm yields precise connectivity outcomes for graphs composed of node-disjoint paths and serves as a lower bound solution for graphs where node-disjoint paths can be extracted. Through simulations in realistic UWSN scenarios, our algorithm demonstrates remarkable efficiency, making it an optimal choice for time-sensitive UWSN applications. Our research contributes both theoretical understanding and a practical algorithmic solution, addressing critical communication challenges in UWSNs.
    Keywords: UWSN;Connectivity;Approximation Algorithm;Node-disjoint Paths.
    DOI: 10.1504/IJSNET.2024.10067408
     
  • Channel Estimation in OFDM Systems based on the Mamdani Fuzzy Genetic Algorithm   Order a copy of this article
    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
     
  • Lightweight Remote Sensing Road Detection with An Attention-Augmented Transformer   Order a copy of this article
    by Feng Deng, Hongyan Tian, Xu Zhao, Duo Han 
    Abstract: Road extraction is a critical task in computer vision. However, accurate road delineation faces challenges due to multiple factors, e.g., object occlusions and similar entities. This study proposes a lightweight road detection model with an attention-augmented transformer to create an effective encoder-decoder and semantic extractor to enhance the road extraction precision. The encoder optimises MobileNetv3 by improving the squeeze and excitation module and bottleneck structure. This modification exploits road global feature extraction efficiency, simultaneously decreasing parameters and computational demands. Moreover, we present an attention-augmented semantic extractor comprising the enhanced transformer blocks that merge depth-wise separable convolutions with an improved multi-head attention as well as efficient channel attention mechanism, thus boosting the model proficiency in capturing extensive dependencies within road semantics. Empirical assessments on the Massachusetts and DeepGlobe road datasets demonstrate that our method outperforms the alternative state-of-the-art solutions, attaining mean intersection over union scores of 80.41% and 79.14%, respectively.
    Keywords: Remote sensing imagery; road extraction; LiAT-Net; attention-augmented semantic extractor; improved multi-head attention.
    DOI: 10.1504/IJSNET.2024.10067671
     
  • PPSSDHE: Privacy Preservation in Smartphone Sensors Data using ElGamal Homomorphic Encryption   Order a copy of this article
    by Manimaran S, Umapriya D 
    Abstract: Smartphone sensors act as vital sensing organs, enabling various mobile applications and activities. However, protecting the privacy of sensor data presents significant challenges that affect smartphone users and artificial intelligence (AI) applications. Unauthorised access to sensitive sensor information poses a major privacy concern. This paper focuses on addressing these issues by introducing a novel scheme that utilises homomorphic encryption (HE) to secure smartphone sensor data and protect user privacy. The method converts decimal values generated by sensors into integers, applying homomorphic encryption to ensure confidentiality and prevent personal information leakage. The proposed scheme employs the ElGamal cryptosystem, particularly suited for multiplication operations, allowing users to securely operate on encrypted data outside the smartphones. Experimental results demonstrate that this approach is highly effective, achieving elevated levels of security, privacy, and data confidentiality while guarding against information leaks.
    Keywords: Sensors; Privacy Preservation; Homomorphic Encryption; Smartphone; Security.
    DOI: 10.1504/IJSNET.2024.10067672
     
  • GM-YOLOV8-Based Safety Hazard Detection Method in Power Construction   Order a copy of this article
    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.