International Journal of Wireless and Mobile Computing (28 papers in press)

Regular Issues

• A survey of lung nodule computer-aided diagnostic system based on deep learning  
  by Tongyuan Huang, Yuling Yang 
  Abstract: With the development of machine learning, especially deep learning, the research of pulmonary nodules based on deep learning has made great progress, which has important theoretical research significance and practical application value. Therefore, it is necessary to summarise the latest research in order to provide some reference for researchers in this field. In this paper, the related research, typical methods and processes in the field of pulmonary nodules are analysed and summarised in detail. Firstly, the background knowledge in the field of pulmonary nodules is introduced. Secondly, the commonly used data sets and evaluation indexes are summarised and analysed. Then, the computer-aided diagnostic system related processes and key sub problems are summarised and analysed. Finally, the development trend and conclusion of pulmonary nodule computer-aided diagnostic system are prospected.
  Keywords: machine learning; deep learning; pulmonary nodule; CAD system.
  
  
• Research on a laser cutting path planning method based on improved ant colony optimisation  
  by Naigong Yu, Qiao Xu, Zhen Zhang 
  Abstract: Laser cutting path planning for fabric patterns is critical to cutting efficiency. The ant colony optimisation algorithm commonly used in this field is constrained by the complete cutting and cannot plan a true global optimal path, resulting in large empty strokes. To solve this problem, this paper proposes an ant colony optimisation method based on virtual segmentation of multiple feature points for path planning of laser cutting. The method first changes the feature point selection strategy of traditional ant colony optimisation and increases the number of feature points in a single pattern. Then the single closed pattern is virtually divided into multiple open contours. Finally, the optimal cutting path is planned based on the solution of the travelling salesman problem. Experiments show that the cutting planning path obtained by the proposed method has a higher degree of compression on the idle stroke and significantly improves the laser cutting efficiency.
  Keywords: laser cutting; path planning; ant colony optimisation; virtual segmentation.
  
  
• Performance analysis of downlink precoding techniques in massive MIMO under perfect and imperfect channel state information in single and multi-cell scenarios  
  by Chanchal Soni, Namit Gupta 
  Abstract: The novel Optimised Max-Min Zero forcing precoder (OM2ZFP) scheme is proposed in this work. The optimization is incorporated with the chimp optimization strategy (CPO) to maximise the spectral efficiency, achievable sum rate, max-min rate, and minimise BER. The designed precoder model is contemplated under single cell perfect CSI, single-cell imperfect CSI and multiple cells perfect CSI, multi-cell imperfect CSI. Three pre-coding schemes, zero forcing (ZF), Maximum Ratio Pre-coding (MRT) and Minimum Mean Square Error (MMSE) precoder techniques, are implemented in the Matlab platform to manifest the effects of the novel designed precoder. The performance of the achievable sum rate is analysed under three cases, namely case I (fixed users and varying antenna), case II (fixed and varying) and case III (varying channel estimation error). The results show that the increasing number of antenna and users enhance the spectral efficiency, downlink transmits power and achievable sum rate performance.
  Keywords: massive MIMO; precoder; downlink transmission; antenna; optimisation; spectral efficiency; achievable sum rate.
  
  
• Preoperative staging of endometrial cancer based on decision tree model  
  by Jun Xu, Hao Zeng, Shuqian He, Lingling Qin, Zhengjie Deng 
  Abstract: Endometrial cancer is extremely common in gynaecological tumours. Ultrasound technology has become an important detection method for endometrial cancer, but the accuracy of ultrasound diagnosis is not high. Therefore, using data-driven methods to accurately predict the preoperative staging of endometrial cancer has important clinical significance. To build a more accurate diagnosis model, this paper uses a decision tree model to analyse the preoperative staging diagnosis indicators of endometrial cancer. Experimental results show that the three-detection data of tumour-free distance (TFD), ca125, and uterine to endometrial volume ratio are of high value for the diagnosis of endometrial cancer. The accuracy, sensitivity and specificity of the random forest (RF) model based on decision tree for preoperative staging of endometrial cancer were 97.71%, 94.11% and 100.00%, respectively. The comprehensive predictive ability based on the RF model has good application value for the prediction of preoperative staging of endometrial cancer.
  Keywords: random forest; decision tree; machine learning; endometrial cancer; preoperative staging.
  
  
• An improved fuzzy clustering log anomaly detection method  
  by Shuqian He, WenJuan Jiang, Zhengjie Deng, Xuechao Sun, Chun Shi 
  Abstract: Logs are semi-structured text data generated by log statements in software code. Owing to the relatively small amount of abnormal data in log data, there is a situation of data imbalance, which causes a large number of false negatives and false positives in most existing log anomaly detection methods. This paper proposes a fuzzy clustering anomaly detection model for unbalanced data, which can effectively deal with the problem of data imbalance and can effectively detect singular anomalies. We introduce an imbalance compensation factor to improve the fuzzy clustering method, and use this method to build an anomaly detection model for anomaly detection of real log data. Experiments on real data sets show that our proposed method can be effectively applied to log-based anomaly detection. Furthermore, the proposed log-based anomaly detection algorithms outperform other the state-of-the-art algorithms in terms of the accuracy, recall and F1 measurement.
  Keywords: distributed information system; log data; anomaly detection; artificial intelligence for IT operations; fuzzy clustering; imbalanced datasets; unsupervised learning; machine learning.
  
  
• Research on wireless routing problem based on dynamic polycephalus algorithm  
  by Zhang Yi, Yang Zhengquan 
  Abstract: The efficiency of the traditional Physarum Polycephalum Model (PPM) is low for wireless planning problems. Also, other heuristic algorithms are easy to fall into local optimum and usually require a large training set to find the optimal parameter combination. Aiming at these problems, we propose a new dynamic model of Physarum Polydynia (DMOP2) algorithm combined with PPM in this paper. This algorithm can judge the irrelevant nodes according to the traffic matrix after each iteration and then delete them and re-establish a new distance matrix when solving the routing problem. The improvements not only reduce the time consumed by calculation but also improve the accuracy of calculation pressure. Simulation experiments in random network and real road network prove the feasibility and effectiveness of the proposed algorithm in solving the path planning problem, and the experimental results show that the efficiency is significantly improved compared with PPM.
  Keywords: wireless planning; Physarum Polycephalum model; dynamic model.
  
  
• A trusted management mechanism based on trust domain in hierarchical internet of things  
  by Mingchun Wang, Jia Lou, Yedong Yuan, Chunzi Chen 
  Abstract: Existing trusted models usually authenticate the identity and behaviour of sensing nodes, without considering the role of sensing nodes in the process of interaction and transmission of information. Therefore, in view of the hierarchical wireless sensor network architecture of the internet of things, this paper proposes a new hierarchical trusted management mechanism based on trusted domain. The mechanism abstracts different nodes in the hierarchical structure of the internet of things, gives them different identities, and calculates the trust value of the sensing nodes by using similarity weighted reconciliation method. The experimental results show that the proposed scheme is feasible and effective.
  Keywords: trusted domain; trusted management; similarity weighted reconciliation; trust value; hierarchical structure.
  
  
• Automatic modulation recognition based on channel and spatial attention mechanism  
  by Tianjun Peng, Guangxue Yue 
  Abstract: With the complexity of the wireless communication environment, automatic modulation recognition (AMR) of wireless communication signals has become a significant challenge. Most existing researches improve the model recognition performance by designing high-complexity architectures or providing supplementary feature information. This paper proposes a novel AMR framework named CCSGNet. The convolutional neural network (CNN) and bidirectional gate recurrent unit (BiGRU) are employed in CCSGNet to reduce the spectral and time variation of the signals, furthermore, the channel and spatial attention are employed to fully extract local and global features of signals. In order to reduce the training time cost of the model, we propose a piecewise adaptive learning rate tuning method to improve the training of the model. The comparisons with several common learning rate tuning methods on CCSGNet show that the proposed method achieves convergence in 25 training epochs, reducing the training time cost of the model. Moreover, CCSGNet improves the recognition accuracy of 16QAM and 64QAM by 6.47%-50.95% and 4.54%-25.66%, respectively.
  Keywords: automatic modulation recognition; attention mechanism; learning rate; deep learning.
  
  
• Optimisation of a high-speed optical OFDM system for indoor atmospheric conditions  
  by B. Sridhar, S. Sridhar, Naresh K. Darimireddy 
  Abstract: VLC provides high security and broadband functionality for optical communication in free space. In particular, this proposed work focuses on analysing receiving power distribution patterns and signal-to-noise ratios for indoor and vehicle applications. The optical systems of indoor communications are more suitable than wireless radio systems. The significant advantage of optical wireless communication (OWC) is providing high-speed data up to 2.5 Gbps at a low cost. In indoor areas such as auditoriums and public places, the OWC systems are more suitable. But optical signals are distorted by the signal propagation effects due to obstacles, walls, etc. The proposed system is an OFDM-based system that can transmit multiple channels and connects many modems over a given indoor area. Proposed methods initially focus on the LED/LD transmitter sources placement at the ceiling of indoor space and observed signal power distribution; in an IM/DD-based OWC system, the information signal must be accurate and nonnegative. The proposed asymmetric optical OFDM (ACO-OFDM) system is implemented for indoor communications, and the system's performance is evaluated with the Bit error rate. In particular, the performance of the specific M-QAM ACO-OFDM method with adaptive frequency is assessed by using theoretical analysis and simulations. Compared to the M-QAM ACO-OFDM method, the ACO-OFDM and DCO-OFDM showed lower spectral efficiency performance for the OWC system in the frequency selective channel.
  Keywords: ACO-OFDM; indoor networks; power distribution; clipping; bit error rate.
  
  
• Res-THL: multimodal pre-training models for document understanding  
  by Lei Zhang, Yong Wang, Nan Yang, Bin Jiang 
  Abstract: Multimodal pre-training models based on the Transformer architecture have been widely adopted in visually-rich document understanding, achieving impressive results. However, existing models still face limitations in effectively extracting features from diverse modalities present in visually rich document data. In this paper, we propose a pre-training model called Res-THL that facilitates interactive modeling between image, layout, and text modalities. To address the inadequacy of existing methods in extracting image features, we introduce a new image feature extraction module called Res, which incorporates a residual structure to capture richer image representations. Additionally, existing studies often overlook the learning of features embedded within the hidden layers of multilayer attention networks. To overcome this limitation, we design the Transformer hidden layer learning (THL) module, which integrates a spatial attention mechanism to adaptively learn layout and text features embedded within the transformer hidden layers. Res-THL are experimented on the FUNSD dataset, and the results demonstrate that the proposed Res-THL network achieves enhanced performance, with the F1 score of 0.8325.
  Keywords: deep learning; multimodal natural language processing; pre-trained models; document understanding.
  DOI: 10.1504/IJWMC.2024.10069546
   
  
• Blockchain-empowered secure localisation scheme in WSN using trust assessment and deep adaptive extreme learning  
  by Moorthy Agoramoorthy, S. Maheswari, A. Hemlathadhevi, Hari Kumar Palani 
  Abstract: Wireless Sensor Networks (WSN) is one of the intrinsic factors of the current prevalent and universal computing. Moreover, the localising of the nodes in WSN becomes a challenging issue and also it needs more concentration on the trust of beacon nodes. Thus, an innovative deep learning-based localisation in WSN using blockchain technology is suggested. Here, the beacon nodes trust value is calculated by Deep Adaptive Extreme Learning Network (DAELNet). The attributes in DEL are optimised by the Enhanced Migration Algorithm. The trusted nodes utilise the blockchain to secure information. Further, the localisation process in WSN is optimally performed by EMA. The location of target nodes is determined by an optimisation algorithm concerning beacon nodes. Finally, the validation process is performed. The numerical findings of the developed model achieve 93% and 94% in terms of accuracy and sensitivity measures. From the validation, the developed model shows enriched performance over existing algorithms.
  Keywords: decision-making; big data; enhanced honey badger algorithm; adaptive cascaded long-short term memory and auto-encoder; map-reduce framework.
  DOI: 10.1504/IJWMC.2024.10070049
   
  
• A secure data routing protocol based on encryption and hybrid deep learning in MANET MANET  
  by Patra Suma 
  Abstract: Mobile ad hoc networks (MANET) permit mobile users to communicate with each other when fixed infrastructure is not possible. However, the vulnerability risks are the major problem in MANET routing. Hence, ResneXt Quantum Dilated Convolution Neural Networks (RQDCNN) is designed for secure data communication in MANET. The routing, secure node identification, and bi-filtering phases are considered. In the routing, the route discovery, and route reply are performed. To avoid route list modification, node’s address is encrypted by Rivest-Shamir-Adleman algorithm (RSA). The secure nodes are determined using the RQDCNN in the secure node identification, which is the integration of ResneXt and Quantum Dilated Convolutional Neural Networks (QDCNN). Finally, the important nodes are filtered in the bi-filtering phase using network-based parameters. The RQDCNN obtained better throughput of 9.746Mbps, delay of 0.635ms, and Packet Delivery Ratio (PDR) of 98.55% with black hole attack.
  Keywords: mobile ad hoc network; ResNeXt quantum dilated convolutional neural networks; ResNeXt; Rivest-Shamir-Adleman; quantum dilated convolutional neural networks.
  DOI: 10.1504/IJWMC.2024.10070366
   
  
• A highly accurate and fast road crack detection algorithm based on Yolov8  
  by Baishao Zhan, Xiong Zhou, Qiangqiang Zeng, Zhizhong Tan, Zhangwei Guo, Wei Luo, Hailiang Zhang 
  Abstract: Due to the limitations of traditional methods in road crack detection,this paper proposes a novel algorithm, RPDD, for road crack detection using drones, focusing on improving detection accuracy and speed. It enhances feature extraction with the RepViT module, improves low-resolution image processing and reduces the computational requirements of the model with the DySample upsampling technique, and adopts a DyHead structure with deformable convolution (DCNV4) to handle multi-scale and complex scenes. Experimental results show significant improvements in precision, recall, mAP, and FPS compared to the baseline model, while reducing missed detections and false positives. The algorithm provides an effective reference for drone-based crack detection in the present time.
  Keywords: road crack detection; YOLOv8; RepViT; DySample; DyHead.
  DOI: 10.1504/IJWMC.2024.10070720
   
  
• A deep adaptive learning model for online fault diagnosis of power distribution networks  
  by Ming Zhang, Cong Liu, Gongchen Wang, Chongfeng Fang, Shiyang Zheng 
  Abstract: Fault diagnosis of power distribution networks is a significant task for power system operation and maintenance, which can support rapid and accurate identification, localization and repair of various faults that may occur in power distribution networks. To improve the accuracy and efficiency of fault diagnosis, this paper proposes a deep adaptive learning model for dealing with the data-driven identification problem in power systems. In the deep adaptive learning model, an adaptive shortcut learning scheme is designed to adaptively adjust the aggregation between the convolutional layers and the attention modules. In this manner, the hidden features can be effectively captured by adaptive learning with the proposed scheme. Therefore, the proposed model can effectively make online decisions on the fault types in high-voltage AC and DC test transmission. The experimental case study and comparison study in this paper demonstrate the reliable performance of the proposed model for online fault diagnosis of power distribution networks, which also show its capability and feasibility for online implementation.
  Keywords: power distribution networks; adaptive learning; deep neural network; fault diagnosis.
  DOI: 10.1504/IJWMC.2025.10070786
   
  
• Optimization of single starting point path for offshore wind farm inspection based on MTSP  
  by Lei Wang, Lei Kou, Zhen Wang, Fangfang Zhang, Quande Yuan 
  Abstract: As the global economy grows and countries' demand for energy expands, many countries are looking to reduce their dependence on fossil fuels by developing offshore wind energy However, as wind farms expand in size and face challenges such as difficult maintenance and high construction costs, traditional inspection methods are inefficient and wasteful of human resources To address these challenges, this paper investigates inspection path optimization for offshore wind farms based on Genetic Algorithm (GA) and Ant Colony Algorithm (ACO) combined with Multiple Travelling Salesman Problem (MTSP) For medium and large-scale offshore wind farms, a single starting point path optimization model for inspection ships is designed In order to shorten the path taken by inspection ships, this paper adopts genetic algorithm and ant colony algorithm for optimization, taking the sum of the total distances taken by all inspection ships as the objective function, and finally compares the performance of the two algorithms for the optimisation of inspection paths of offshore wind farms through simulation experiments, and the results show that the optimised paths of inspection of wind farms with genetic algorithm are shorter than that of ant colony algorithm. Therefore, the application of genetic algorithm in offshore wind farm inspection path optimisation can reduce the operation and inspection cost.
  Keywords: offshore wind farm; multi-traveller problem; genetic algorithm; ant colony algorithm; inspection path.
  DOI: 10.1504/IJWMC.2025.10070957
   
  
• Routing and trust management in MANET using hybrid crayfish white shark optimisation  
  by Moresh M. Mukhedkar, Vaishali Jadhav, Priyanka Dhondiraj Halle, Uttam Waghmode, Nitin Ashok Dawande, Pallavi Vasant Sapkale 
  Abstract: In the current communication system, mobile ad hoc networks (MANETs) are considered as the individual nodes in mobile networking and they easily communicate with each other. The performance of MANETs is impacted by security issues. Hence, effectual routing and trust updation are required for upgrading the data transmission security level in MANET. In this research, the Crayfish White Shark Optimization (CFWSO)-based routing and trust updation is developed for MANET. The MANET is simulated by the energy, mobility, and trust models. The routing is carried out through the CFWSO with fitness functions like energy, delay, throughput, distance, and trust. Moreover, the Deep Neuro Fuzzy Network (DNFN) is used for trust updation. In addition, the performance computing measures like energy, delay, distance, packet loss, throughput, and Link lifetime are used to compute the efficacy of the model, and the finest outcomes of 0.140 J, 0.569 ms, 42.551 m, 1.489%, 85.196 Mbps, and 86.680 ms. are achieved.
  Keywords: COA; crayfish optimisation algorithm; DNFN; deep neuro-fuzzy network; MANETs; mobile ad hoc networks; WSO; white shark optimisation; routing.
  DOI: 10.1504/IJWMC.2025.10071204
   
  
• The agnostics way platform for data governance with metadata management on the big data ecosystem  
  by Ashish N. Patil, Prakash R. Devale 
  Abstract: This research focuses on enhancing the security of Metadata in big data environments by developing a novel Recurrent Neural Data Encryption Model (RNDEM). The model addresses critical data governance and security challenges, particularly in cloud computing, where traditional encryption methods often prove inadequate. RNDEM integrates advanced techniques such as Homomorphic encryption and blockchain mechanisms to protect against unauthorized access and denial-of- service (DoS) attacks. This model offers organizations a reliable framework for securing sensitive data in large-scale data ecosystems, supporting improved data governance and regulatory compliance. Combining neural networks with encryption algorithms in a hybrid system aims to strengthen data security while maintaining efficiency. Experimental results demonstrate significant improvements, achieving a confidentiality rate of 1.035%, a low error rate of 15.27%, an encryption time of 0.531ms, and a hash calculation time of 1.95s. Compared to existing models, RNDEM exhibits superior performance regarding privacy rate, error rate, and hash computation time.
  Keywords: blockchain; metadata; data encryption; data analysis; homomorphism; security analysis; DoS attack.
  DOI: 10.1504/IJWMC.2025.10071235
   
  
• Event-triggered robust model predictive control for trajectory tracking of omnidirectional mobile robot  
  by Changrong Zhang, Juntong Yun, Du Jiang, L. Huang, Ying Liu, Bo Tao, Yuanmin Xie 
  Abstract: With the widespread application of omnidirectional mobile robot (OMR) in fields such as industrial automation, logistics, and services, the demand for trajectory tracking accuracy is increasingly stringent. This paper addresses the motion characteristics of OMR by proposing an event-triggered model predictive control (MPC) approach, aiming to resolve the challenge of excessive computational resource requirements when solving the optimal control problem (OCP) online for omnidirectional mobile robots. The proposed method comprises two core components: a robust model predictive control (RMPC) controller and a variable time-domain event-triggering mechanism. The robust MPC controller ensures strong disturbance rejection capabilities for OMR under external disturbances by employing a tightened constraint strategy. Simultaneously, the introduction of a prediction horizon update strategy and an event-triggering mechanism effectively alleviates the computational burden of solving the OCP online. This method significantly reduces computational resource consumption while maintaining control performance, thereby enhancing the real-time trajectory tracking of OMR. Simulation experiments demonstrate that, compared to traditional event-triggered MPC (EMPC) methods, the computational load is reduced by up to 76.53%, validating the efficiency and feasibility of the proposed method in practical applications.
  Keywords: model predictive control; event-triggered mechanism; omnidirectional mobile robot; trajectory tracking.
  DOI: 10.1504/IJWMC.2025.10071498
   
  
• Fault location method for AC/DC distribution network based on wavelet transform and two-level artificial neural network  
  by Cong Liu, Ming Zhang, Rongliang Zhu, Shuang Ji, Lei Wang 
  Abstract: In view of the increasingly complex distribution network structure in new power systems, the limitations of traditional distribution line fault point troubleshooting technology have become more prominent This paper proposes a fault location method for AC/DC distribution networks based on wavelet transform and two-level artificial neural network Firstly, a series of distinguishable features are extracted from the fault signals recorded by the relays in order to make the faults in the distribution network understandable by the neural network A secondary component extraction algorithm of fault information based on wavelet transform is proposed Then, a distribution network fault identification and location model based on a two-level artificial neural network is constructed Used to achieve accurate estimation of fault range, fault location, and fault resistance Finally, the standard IEEE 15-bus system is used to carry out case analysis The results show that the proposed method has good identification performance for faults at different angles, different locations and different resistances.
  Keywords: wavelet transform; artificial neural networks; AC/DC distribution network; fault localization; fault identification; fault resistance.
  DOI: 10.1504/IJWMC.2025.10071528
   
  
• A new crowd anomaly detection model: optimisation aided detection and localisation  
  by Jyoti Ambadas Kendule, Kailash J. Karande 
  Abstract: Detecting anomalies in crowded scenes is a challenging and important part of the intellectual video supervision system. In this work, a novel crowd anomaly detection model is developed that includes 3 main phases. Firstly, Entropy based FCM (EFCM) is carried out to segment the frames from video. Further, Histogram of Gradients (HoG), Local Gradient Pattern (LGP), and improved motion estimation features via block matching technique features are derived. These features are then classified via hybrid classifiers that include DBN and LSTM models. The weights of DBN and LSTM classifiers are tunes in an optimal manner via Ant Lion Aided Grasshopper Optimization (ALA-GO) model. The suggested HC + ALA-GO model achieved the minimal cost of 0.65 and provides faster convergence rate compared to HC + GOA, HC + SSO, HC + SMO, HC + ALO, HC + SSA and HC + SI-DOX models.
  Keywords: crowded anomaly; EFCM; motion estimation; hybrid classifier; ALA-GO optimisation.
  DOI: 10.1504/IJWMC.2024.10072132
   
  
• Ice accretion prediction of transmission lines based on ICEEMDAN-xLSTM-transformer  
  by Hongbin Sun, Qiuzhen Shen 
  Abstract: This paper proposes a hybrid model for predicting ice thickness that integrates Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Extended Long Short-Term Memory (xLSTM), and Transformer. First, ICEEMDAN is utilised to decompose the ice accretion time series data, which is influenced by climate factors such as temperature, humidity, and wind speed, exhibiting uncertainty and nonlinearity. Then, the xLSTM network model is employed to extract features from the multiple Intrinsic Mode Functions (IMF) obtained after ICEEMDAN, enabling xLSTM to learn information across different time scales and establish temporal relationships between the data. Finally, the features extracted by the xLSTM module are fed into the Transformer module, which uses a multi-head attention mechanism to capture data features from different positions and concatenates them. A feedforward network then performs nonlinear transformations, resulting in the Transformer outputting the predicted ice thickness. Experimental results show that the proposed hybrid prediction model achieves better results compared with other methods.
  Keywords: ice accretion prediction; ICEEMDAN; xLSTM; Transformer.
  DOI: 10.1504/IJWMC.2025.10072188
   
  
• An empirical study on the relationship between positive emotions, recovery experience, and work enthusiasm among logistics practitioners  
  by Wenling Yu, Caigen Peng, Zixia Chen 
  Abstract: This study explored the relationship between positive emotions, recovery experiences, and work enthusiasm among logistics practitioners. Using the Positive and Negative Emotion Scale, Recovery Experience Scale, and Work Enthusiasm Scale, the study surveyed 690 employees from logistics companies in Jiangsu and Shanghai. The findings revealed that: (1) The positive emotions, recovery experiences, and work enthusiasm of logistics practitioners were all above average. (2) Positive emotions were positively correlated with the four dimensions of recovery experiences psychological detachment, psychological relaxation, psychological mastery and psychological control) and with two dimensions of work enthusiasm (learning and vitality). Work enthusiasm was also strongly related to recovery experiences. (3) Positive emotions, psychological mastery and psychological control have a significant positive predictive effect on work enthusiasm, while negative emotions and psychological detachment had a negative impact. The paper offers practical suggestions to enhance positive emotions among logistics practitioners from psychological, managerial, and sociological perspectives.
  Keywords: positive emotions; recovery experiences; work enthusiasms; logistics practitioners.
  DOI: 10.1504/IJWMC.2025.10072205
   
  
• Virtual MIMO-based cross-layer: optimisation strategy for routing in WSN  
  by Monali Prajapati, Jay Joshi, Maulin M. Joshi, Upena Devang Dalal 
  Abstract: Wireless Sensor Networks (WSNs) consist of numerous sensor nodes connected through wireless medium. The Virtual Multiple-Input Multiple-Output (V-MIMO) provides reliable communication over long distances. Since, V-MIMO ensures reliable communication across intermediate nodes becomes challenging and causes interference during transmission. To address this issue, a new Cluster-Based Multi-hop V-MIMO protocol is suggested, which significantly enhances communication performance. In this study, the Convolutional Neural Network (CNN) model is utilized for energy prediction, while considering type and location of nodes. Subsequently, employed K-Means algorithm for clustering the nodes within the network. Then, Cluster Heads (CHs) are chosen using a hybrid Coati Assisted Osprey Optimization (CAOsO) algorithm while considering constraints like node energy, QoS, modified trust evaluation, and risk. Then the CAOsO is employed for optimal routing and also optimizes BER in the data transmission phase. In comparison with traditional algorithms, the CAOsO shows faster convergence with a minimal cost rate of 0.824.
  Keywords: V-MIMO; CAOsO algorithm; K-means clustering algorithm; bit error rate performance; CNN model.
  DOI: 10.1504/IJWMC.2025.10072321
   
  
• Unsupervised machine learning for device clustering and dynamic power allocation in hybrid NOMA-MEC system  
  by Sandeep Singh Rana, Gaurav Verma, O.P. Sahu 
  Abstract: Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) are key 5G technologies aimed at increasing the capacity and efficiency of next-generation wireless networks. However, existing clustering methods face significant challenges, including poor computational efficiency, suboptimal clustering performance, and difficulty in ensuring minimum rate guarantees under dynamic network conditions. To address these issues, this paper proposes an Enhanced K-Means Clustering (EKC) algorithm which dynamically optimizes device clustering and power allocation to ensure minimum rate requirements in a hybrid NOMA-MEC system. Results demonstrate that the EKC algorithm surpasses other clustering methods, including Hierarchical, Density-based spatial clustering of applications with noise (DBSCAN), and Gaussian Mixture Model (GMM), in terms of computational efficiency and clustering performance. Theoretical analysis further supports these findings, showing that using Near-Far (NF) pairing as a benchmark, the sum-rate capacity improvements for Quadrature Near-Far (Q-NF), EKC, DBSCAN, Hierarchical, and GMM clustering are -0.005%, 9.14%, -47%, 8.23%, and 5.77%.
  Keywords: clustering algorithms; EKC; NOMA; MEC; ML.
  DOI: 10.1504/IJWMC.2025.10072352
   
  
• Optimising low-carbon supply chains in crossborder trade: a tripartite game model analysis  
  by Chuan Yang, Feng Ding, Qianqian Wang, Zixia Chen 
  Abstract: This paper focuses on optimizing low-carbon supply chains in cross-border trade. It develops a game model with government, enterprises, and consumers to explore optimal low-carbon implementation strategies under carbon tariffs. By constructing a dynamic game model and deriving a mixed-strategy Nash equilibrium, it analyzes the interactions between government regulation, corporate strategies, and consumer behavior. The study shows that government investment and social welfare maximization strongly influence corporate low-carbon strategies and consumer preferences for green products. This article innovatively proposes a tripartite game model to analyze the optimal strategies for low-carbon supply chain implementation under carbon tariffs. This study enriches the theoretical foundation of low-carbon supply chains in cross-border trade and offers a scientific basis for government policies and corporate low-carbon strategies.
  Keywords: cross-border trade; low-carbon supply chain; game model; optimal strategy.
  DOI: 10.1504/IJWMC.2025.10072589
   
  
• MCNN-SENet: Bearing fault diagnosis method based on multi-scale convolution and squeeze-and-excitation networks  
  by Lida Liu, Yingjie Chen, Mei Sun, Qimiao Wang, Peiguang Lin 
  Abstract: In modern automated mechanical systems, the health status of rolling bearings directly affects the performance and service life of the machine, which is the focus of fault diagnosis research Traditional bearing fault diagnosis methods rely on manual feature extraction and classifier design, which have limited efficiency and accuracy In view of the problems of limited labelled samples and noise in industrial data, this study proposed a bearing fault diagnosis method based on multi-scale convolution networks (MCNet) and squeeze-and-excitation networks (SENet) In this method, large convolutional kernel and multi-scale convolution are used for efficient feature extraction, and the squeeze-and-excitation blocks are combined to enhance the sensitivity and recognition ability of the network to fault features, so as to improve the accuracy and robustness of fault diagnosis The experimental results show that the average accuracy of the proposed method on the bearing dataset of Case Western is 99 7%, and has good performance in the noisy environment as well.
  Keywords: vibration signals; convolutional neural networks; attention mechanisms; bearing fault diagnosis.
  DOI: 10.1504/IJWMC.2025.10072801
   
  
• Interference suppression algorithm for wireless communication network  
  by Peng Yan, Yunjian Jia 
  Abstract: This paper examines the co-frequency full-duplex and multi-user systems of fullduplex relay stations, focusing on interference cancellation model design and application algorithms. Three adaptive algorithms Least Mean Square (LMS), Normalised Least Mean Square (NLMS), and Recursive Least Square (RLS) are introduced and analysed. Special emphasis is placed on LMS-based interference reconstruction and elimination in digital self-interference suppression. The study explores challenges in wireless communication technology development under big data and proposes optimisation strategies for network deployment, planning, and performance enhancement. The findings offer practical guidance for interference suppression algorithms in wireless communication networks, particularly within the context of big data and edge computing.
  Keywords: edge computing; big data analysis; wireless network; interference suppression algorithm; communication model.
  DOI: 10.1504/IJWMC.2025.10073015
   
  
• An intelligent system resource allocation and computing migration strategy based on edge cloud collaboration  
  by Jie Wang, Haiming Zhang, Weinan Liu, Jiangjun Yuan 
  Abstract: With the explosive growth of terminal data, more and more intelligent system devices are demanding higher computing resources and energy efficiency. Therefore, traditional computing migration methods cannot meet the requirements of low energy consumption for devices. This paper proposes an intelligent computing migration mechanism based on edge cloud collaboration. Specifically, by jointly considering migration decisions, computing offloading and scheduling, caching decisions, and cloud node computing resource allocation ratios, an optimization problem with minimal energy consumption was constructed to ensure that all computing tasks in intelligent terminal devices can be completed. To solve this resource management problem, this paper proposes an intelligent migration based on Chaotic Quantum Cuckoo Search (CQCS). The proposed algorithm integrates chaotic sequences and quantum strategies on the basis of the traditional cuckoo algorithm, thereby enhancing the scalability and flexibility of the algorithm, and improving its global convergence and local search ability.
  Keywords: edge cloud collaboration; intelligent systems; resource allocation and management; intelligent computing migration; computing offloading and scheduling; chaotic quantum cuckoo algorithm.
  DOI: 10.1504/IJWMC.2024.10073103