International Journal of Wireless and Mobile Computing (11 papers in press)

Regular Issues

• A novel trust-based approach for intrusion detection architecture in wireless sensor networks  
  by Mr. Jeelani, Kishan Pal Singh, Aasim Zafar 
  Abstract: Wireless sensor networks (WSNs) is a new technology that can be used to monitor the environment. Because sensor nodes in wireless sensor networks are installed in an open environment, they are more vulnerable to attacks. The sensor network lifetime improvement is dependent on minimum energy use. Protection is also a major concern when it comes to designing protocols for multi-hop secure routing. The results based on trust have proven to be more effective in addressing malicious node attacks. In this article, we propose a novel trust-based approach for intrusion detection architecture (IDA) in a wireless sensor network that is called the trust-based approach for varying nodes with energy (TBNE) model. TBNE finds the misbehaving nodes in the network. The structure is based on the trust model for secure communication in WSN and improves the performance of nodes. The simulation has been done with QualNet 5.0 simulator.
  Keywords: wireless sensor network; throughput; packet delivery ratio.
  
  
• 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.
  
  
• 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.
  
  
• Computer simulation research of lithium battery materials: fusion of first-principle calculation and ANSYS simulation  
  by Juan Cui, Cong Zhong 
  Abstract: This study provides theoretical support and technical guidance for optimizing the performance of lithium-ion battery anode materials by integrating first-principles calculations and ANSYS simulation technology. Addressing the shortcomings of existing simulation methods in terms of accuracy and efficiency in describing electronic structure and ion migration, and their inability to support high-throughput screening of new materials, a novel ANSYS-FP method is proposed, combining first-principles calculations and ANSYS multiphysics simulation. This method uses atomic-scale parameters calculated precisely in first-principles calculations as input to the ANSYS simulation and utilizes macroscopic simulation results as feedback to guide the optimization of the first-principles model, forming a cross-scale closed loop. This method outperforms methods without first-principles calculations in both simulation speed and mean absolute error, with most data points concentrated within the 98% to 99% accuracy range. This approach can efficiently support high-throughput screening of anode materials, significantly shortening the development cycle of new materials.
  Keywords: lithium batteries; negative electrode material; computer simulation; first principles; multi-physics field simulations; multiscale simulation.
  DOI: 10.1504/IJWMC.2026.10078385
   
  
• DRBC_Alexnet:a new method for detecting diseases of rice spikes  
  by Le Yang, Huibin Long, Zhengkang Zuo, Caihua Zhang 
  Abstract: In the field of smart agriculture, convolutional neural networks (CNNs) are increasingly been utilised for the recognition of crop diseases, significantly enhancing recognition efficiency. However, the base network may fall short of meeting expectations when dealing with diverse crop diseases due to variations in recognition efficiency across datasets or constraints inherent in the network architecture. To address this challenge, this study introduces a novel network model, DRBC_AlexNet (Double Residual Network with Coordinate Attention AlexNet). Secondly, a coordinate attention (CA) module is appended to each DRM block. This new approach is employed to recognise rice spike diseases and control the loss rate using a crossentropy loss function. Experimental results demonstrate that the proposed model achieves a remarkable recognition rate of 99.02%. To further evaluate generalisability, the model is tested on public datasets, achieving 97.01% accuracy on the PlantVillage dataset and 97.84% accuracy on the Kaggle dataset. These findings indicate that DRBC_AlexNet not only offers superior recognition accuracy but also maintains a satisfactory level of generalisation capability.
  Keywords: convolutional neural network; rice spike disease; DRBC_AlexNet; Coordinate attention; double residual module.
  DOI: 10.1504/IJWMC.2025.10079613
   
  
• Automatic early detection of tomato leaf disease using IoT and deep learning  
  by I. Sheik Arafat, S. Aswath, S.M. Haji Nishath 
  Abstract: Tomato plants are highly susceptible to fungi, bacteria, and viruses, and delayed disease detection adversely affects yield and quality. Traditional manual inspection has low early detection accuracy (5565%) and response times exceeding 2448 hours, leading to economic losses. This paper proposes an automated early warning system using Internet of Things (IoT) technology and deep learning to detect tomato leaf diseases in real time. IoT camera nodes capture leaf images and transmit them to a cloud server via LoRaWAN. The proposed framework integrates automated data acquisition, wireless transmission, and cloud-based inference for continuous crop monitoring. Mask R-CNN precisely segments leaves, and a fine-tuned ResNet50 performs binary disease classification. Using a benchmark dataset of 3,900 images, the proposed model achieved 99.87% classification accuracy with almost zero false negatives. The system also achieved an end-to-end latency of approximately 45 seconds and provides real-time mobile notifications, offering a scalable and practical solution for precision agriculture.
  Keywords: tomato leaf disease; IoT; internet of things; deep learning; Mask R-CNN; ResNet-50; LoRaWAN.
  DOI: 10.1504/IJWMC.2026.10080469