International Journal of Wireless and Mobile Computing (24 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.
  
  
• 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
   
  
• 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
   
  
• Game theory with Tuna-Sandcat optimisation-based robust and energy efficient spectrum sensing against malicious users in cognitive radio network  
  by Purushottam G. Chilveri, Manisha Ajaykumar Dudhedia 
  Abstract: In 5G Het-net, spectrum sensing (SS) efficiency and energy efficiency (EE) are balanced by the Cognitive Radio Network (CRN). By taking into account different SS circumstances, this effort aims to achieve EE. This work proposes a Tuna-Sandcat Optimization-based Spectrum Sensing (TSI-SCSA) method, integrating Stackelberg game theory to enhance spectrum sensing efficiency and energy efficiency in CRNs while mitigating the impact of MUs providing false information. The approach ensures robust decision-making and optimal channel selection, even under malicious conditions. The performance of the proposed algorithm is evaluated over conventional algorithms for analyses such as spectrum is busy, missed detection, false alarm, useful detection and throughput. The proposed TSI-SCSA model achieves minimal mission detection value of 0.25 at iteration 100, when compared to existing techniques for efficient spectrum sensing in CRN.
  Keywords: cognitive radio network; spectrum sensing; malicious users; Stackelberg Game theory; secondary user.
  DOI: 10.1504/IJWMC.2025.10073489
   
  
• Optimal pre-trained deep ensemble of classification model for multimodal sarcasm detection  
  by Dnyaneshwar Madhukar Bavkar, Ramgopal Kashyap, Vaishali Khairnar 
  Abstract: The practice of using words or sentences that have a meaning other than their literal meaning is known as verbal irony or sarcasm. Making a machine recognise sarcasm is not an easy task because it can take humans a while to comprehend it. Deep Learning (DL) is becoming increasingly necessary for operations involving detection and classification. The four essential steps in this method are pre-processing, feature extraction, improved modality level fusion, and ensemble classification technique. The very next stage is feature extraction, wherein n-gram, cosine similarity, and improved TF-IDF (ITF-IDF) features are extracted from the text. Through improved modality level feature fusion, all the input modality attributes that have been extracted are placed through fusion to produce the fused feature set. An ensemble classification model is proposed that uses Deep Maxout, DBN, CNN, and Bi-LSTM. Atom Search Assisted Bald Eagle Optimization (ASBEO) trains the Bi-LSTM by tuning the optimum weights.
  Keywords: sarcasm detection; ensemble model; deep learning; optimisation; tokenisation.
  DOI: 10.1504/IJWMC.2024.10073490
   
  
• A research on power optimisation of wireless vibration sensors by the embedded data pre-processing  
  by Xuejun Ni 
  Abstract: This thesis proposes a low power consumption and low-cost wireless sensor network for vibration signal monitoring. To achieve this, a local condition indicator algorithm is implemented within the vibration sensor, enabling the transmission of indicator data instead of raw data. This approach significantly reduces power consumption while maintaining accuracy. Extensive testing confirms the effectiveness of the proposed solution, demonstrating improved power efficiency by nearly 98% without compromising data accuracy. This innovative approach allows for scalable and distributed measurement of vibration signals, benefiting fault diagnosis and condition monitoring in industrial and engineering applications.
  Keywords: wireless vibration sensor; fault diagnosis; embedded system.
  DOI: 10.1504/IJWMC.2024.10073491
   
  
• Context-aware detection of selfish nodes in mobile ad-hoc networks using fusion of hybrid leader optimisation with barnacles mating optimiser and adaptive deep belief network  
  by K. Sudhaakar, K.T. Meena Abarna, E. Mohan 
  Abstract: A self-configuring network of mobile nodes linked by wireless connections is a Mobile Ad-hoc Network (MANET). Like the existence of an attack node, the selfish node is present in the network and cannot transfer the information to the neighbor nodes. Due to this reason, the performance gets affected. Hence, this work considers the attributes of nodes, such as like hop count, residual energy, and cooperation history, termed as the input factors. With the help of these constraints, the Adaptive Deep Belief Network (ADBN) is newly developed to determine the target value for the cooperation rate. Further, the hyper-parameters in the Deep Belief Network (DBN) are optimally chosen by proposing the Fusion of Hybrid Leader Optimization with Barnacles Mating Optimizer (FHLO-BMO). From the results, the accuracy and precision rate of the developed model are 92.86% and 93.29%. Finally, the effectiveness of the model is validated and measured with various metrics.
  Keywords: selfish node detection; mobile ad-hoc network; adaptive deep belief network; fusion of hybrid leader optimisation; barnacles mating optimiser.
  DOI: 10.1504/IJWMC.2024.10073498
   
  
• A secure transmission protocol using C-ACO routing selection in MANET  
  by Sandeep Lalasaheb Dhende, Suresh Damodar Shirbahadurkar 
  Abstract: Developing extremely effective routing protocols for Mobile Ad hoc NETworks (MANETs) is a challenging task. Specifically, the existing methods faced difficulty in satisfying the Quality of Service (QoS) factors and lacked proper adaptation to the changes in network topology with minimal control overhead, making the routing in MANETs an NP-hard problem. Hence, this research proposes an energy-efficient Cuckoo-Ant Colony Optimisation (C-ACO) routing protocol to achieve efficient routing in MANET. The proposed approach demonstrates that the data transmission ought to choose the best path when the repetition of computation occurs within the network. In addition, the AES-128 standard encrypts the routing messages, and the network communication occurs using the QoS-aware C-ACO algorithm that enhances the routing. Experimental results demonstrate that the proposed algorithm reached the end-to-end delay of 9 ms, Packet Delivery Rate of 95%, routing overhead of 0.020, and throughput of 4500 kbps on evaluating with blackhole attack.
  Keywords: MANET; C-ACO; cuckoo-ant colony optimisation; security; QoS; quality-ofservice; AES-128.
  DOI: 10.1504/IJWMC.2024.10073524