International Journal of Wireless and Mobile Computing (40 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.
  
  
• Optimised recurrent neural network based localisation in wireless sensor networks: a composite approach  
  by Shivakumar Kagi, Basavaraj S. Mathapati 
  Abstract: Localisation is one of the key techniques in the wireless sensor network. The location estimation methods can be classified into target/source localisation and node self-localisation. There are several challenges in some special scenarios. Therefore, the anchor node-based distance estimation scheme is used in this research work. In the anchor-based localisation technique, the unknown node uses the position of the anchor node to estimate its location. The trained Recurrent Neural Network (RNN) with the extracted Angle Of Arrival (AoA) and RSSI features of the anchor node and the estimated nodes makes the localisation of the unknown node more precise. Further, to lessen the localisation errors in RNN, its weights are fine-tuned by an Improved Whale optimisation Algorithm (IWOA).
  Keywords: WSN; node localisation; AoA and RSSI based feature computation; RNN; IWOA.
  
  
• A feature fusion pedestrian detection algorithm  
  by Nan Xiang, Lu Wang, Xiaoxia Ma, Chongliu Jia, Yuemou Jian, Lifang Zhu 
  Abstract: When pedestrians are in different angles and positions, The feature extraction and fusion capabilities are often limited of YOLO series model. Aimed at this problem, we propose an improved feature fusion pedestrian detection algorithm YOLO-SCr. To enhance the ability of cross-scale feature extraction and detection speed, we reconstruct the network structure of the YOLO algorithm in the backbone part and convolution layer part, respectively. Then, to strengthen the feature fusion ability of pedestrians at different scales ,we introduce the spatial pyramid pooling module and shuffle & CBAM(Convolutional Block Attention Module) attention mechanisms in different positions before YOLO layers. The experimental results show that compared with the detection algorithm such as YOLOv3, YOLO-SCr can performance effectively improve the detection accuracy , Recall and speed.
  Keywords: YOLO series ; feature extraction ; feature fusion ;spatial pyramid pooling; pedestrian detection ; shuffle & CBAM attention;.
  
  
• 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.
  
  
• Deep reinforcement learning multi-robot cooperative scheduling based on service entity network  
  by Xueguang Jin, Chengrui Wu, Yan Yan, Yingli Liu 
  Abstract: Multi-robots are increasingly deployed with the development of automation in agriculture, industry, and warehousing logistics. With the help of CPS virtualisation technology, services or tasks can be decomposed into a network with capability or function entity nodes and edges connecting nodes. In this paper, the service entity network is extended with human, robot, and IT resources as a task-decomposed network with public entities, private entities, and links. Based on the service entity network virtualisation architecture, it is possible to form a global service entity network corresponding to the correlated tasks. Meanwhile, deep reinforcement learning multi-robot cooperative scheduling based on a service entity network framework is studied, which makes it possible to jointly optimise the deployment of multi-robot tasks with multi-service entity networks. The results show that the model based on the artificial intelligence virtualisation architecture achieves a better performance.
  Keywords: service entity network; virtualisation technology; multi-robot cooperative scheduling.
  
  
• SBER: Stable and Balance Energy Routing Protocol to Enhance the Stability and Energy for WBANs  
  by Sara Raed, Salah Abdulghani Alabady 
  Abstract: Stability and reduced energy consumption are essential in the design requirements of Wireless Body Area Network (WBAN) routing protocols. For instance, many energy-efficient routing protocol solutions have been suggested for WBANs; however, the significant feature of stability in these existing solutions has not been effectively addressed. In this paper, we propose a Stable and Balance Energy Routing (SBER) protocol to improve the stability period and manage the limited power of the WBAN network efficiently. SBER consists of two solutions, namely, the next-hop node selection and adding awareness to the transmission of control packets techniques. For analysis of the performance of the SBER protocol, MATLAB has been used. The average improvements rate of the SBER in terms of network residual energy over ERRS, M-ATTEMPT, and SIMPL protocols are 35%, 52%, and 100% respectively, which proves SBER to be a more efficient and reliable approach for WBANs.
  Keywords: WBANs; stability period; routing protocol; SBER; ERRS; M-ATTEMPT; SIMPL.
  
  
• Research on fire alarm system of the intelligent building based on information fusion  
  by Sun Xuejing 
  Abstract: In order to effectively reduce the hazards caused by fire and improve the accuracy of fire alarm systems, this paper proposes to use STM32 microcontroller as the control core, use the communication method of Zigbee wireless communication technology combined with CAN bus technology, apply the QPSO-BP neural network algorithm based on multi-sensor information fusion method to fire alarm judgment, and use the fire protection partition in the building as the basis for the distributed intelligent building fire alarm system. The results show that the distributed intelligent building fire alarm system designed in this paper meets the design requirements of the system while fully considering the economic benefits and makes up for the shortcomings of the traditional fire alarm system. The algorithm output results are accurate and reliable, providing a reference for the design of building fire alarm systems.
  Keywords: intelligent building fire alarm; information fusion; QPSO-BP neural network algorithm; Zigbee technology.
  
  
• A hybrid meta-heuristic algorithm to detect malicious activity based on dynamic ON VANET environmental information  
  by Gagan Preet Kour Marwah, Anuj Jain 
  Abstract: VANET has the characteristics of self-organisation, rapid topology changes, and frequent link disconnection that perhaps led to challenging issues. In order to mitigate these issues, a highly effective technology is required; therefore, this work has adopted a Hybrid Firefly Optimisation Algorithm (FOA) and a Whale Optimisation Algorithm (WOA) named as HFWOA-VANET. The HFWOA-VANET has the features of both meta-heuristic algorithms and is implemented to enhance the performance of VANET. This process is mainly based on consideration of Quality of Service (QoS) parameters of each vehicle. Therefore, the performance of vehicle can be determined and the better service in VANET platform is enabled. The implementation of this work is done on NS2 platform and the obtained results are analysed for ensuring the performance of the proposed model. Moreover, the performance of the model is compared with the existing technology; therefore, the proposed model can be ensured as a more effective technique than the existing technique in terms of performance metrics.
  Keywords: VANET; firefly optimisation algorithm; whale optimisation algorithm; QOS; QMM-VANET; HFWOA-VANET.
  
  
• 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.
  
  
• OLSR-ETX: a parameterised solution for oscillatory network packet losses  
  by Kifayat Ullah, Ihtisham Ali 
  Abstract: Expected Transmission Count (ETX) has gained popularity due to identifying a high-throughput path in the multihop wireless network. However, the oscillatory network may not work correctly with a high traffic load; the probe packets may be lost or queued. This paper proposes a parameterized solution (data rate tuning and packet size adjustment) to minimize packet losses. Experimental results indicate that the network's performance has improved using ETX as a routing metric by tuning data rates and adjusting packet size. The results show that by keeping the Data rate under 200kbps and a Packet size of 256 bytes, the performance of the OLSR-ETX routing protocol has improved in the oscillatory network. Finally, we have evaluated the OLSR-ETX parameterized-based solution with OLSR-ETX in oscillation scenarios concerning packet loss ratio. The results show that a parameterized-based solution improves the functionality of the routing protocols in the oscillatory network.
  Keywords: ETX; OLSR-ETX; OLSR; oscillatory network; packet loss ratio.
  
  
• An efficient blockchain model for improving data transmission rate in ad hoc networks  
  by Lucky Narayana 
  Abstract: A Mobile Ad hoc Network (MANET) is an infrastructure-less network that can be established dynamically whenever and wherever required for establishing communication. The MANET is a series of nodes with capabilities in wireless communication and networking. A temporary network that is possible without an already-oriented network or centralised supervisor is linked by an ad hoc network to its mobile hosts as required. The topology of an ad hoc network is different for node mobility. The function of the ad hoc network needs its own solutions and should be different from the static networks to build applications. Radio nodes are immediately established to communicate with each other. With the help of intermediate nodes, nodes not within each other\'s radio range can be transmitted from source to destination. As ad hoc networks are dynamic in nature, they frequently undergo several attacks that reduces the data transmission rate. In the proposed work, an efficient blockchain model is used in ad hoc networks for improving the data transmission rate by analysing the cause for packet loss. In the proposed model, a Malicious Task Identification Head Node (MTIHN) is selected from the network that analyse the blocks generated after every transaction for checking the cause of packet drops. The blockchain is a modern data storage platform. In the various systems with different operating principles this does not operate in the same way. The proposed work explores network security using the blockchain framework to make it easier to send messages and information without loss that improves system performance. The proposed model is compared with the traditional methods and the results show that the proposed model exhibits better performance in improving Data Transmission Rate.
  Keywords: data transmission rate; malicious actions; blockchain; security; ad hoc networks; block generation.
  
  
• 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.
  
  
• A new time-frequency synchronisation algorithm based on preamble sequence in OFDM system  
  by Weimin Hou, Yan Wang, Yanli Hou 
  Abstract: Aiming at the problems of high computational complexity in the timing synchronization phase and poor frequency offset estimation performance of existing time-frequency synchronization algorithms, this paper proposed an improved time-frequency synchronization algorithm based on preamble sequence for OFDM systems. The preamble sequence is designed by using the property that the cross-correlation value of the Constant Amplitude Zero Auto Correlation (CAZAC) sequence with different root values is close to zero. Based on its features, a timing metric function and the frequency offset estimation function are designed. The frequency offset estimation function is used to obtain the coarse fractional frequency offset, and the fine fractional frequency offset is obtained by combining cyclic prefix (CP) and cyclic suffix (CS). Then the time domain sliding correlation between receiving sequence and the local preamble sequence is used to estimate the integer frequency offset. The results indicate that the proposed method has better synchronization capability than existing algorithms.
  Keywords: OFDM system; timing synchronization; frequency offset estimation; preamble sequence; CAZAC sequence.
  
  
• 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.
  
  
• An efficient spectrum shaping method for OFDM-based cognitive radio system  
  by Parmila Devi, Manoranjan Rai Bharti 
  Abstract: Orthogonal Frequency Division Multiplexing (OFDM) is widely used for transmitting digital data over wireless communication channels but suffers from high-side lobes causing energy leakage into adjacent frequency bands. This leakage can lead to interference between licensed and unlicensed OFDM-CR users. To minimise this interference, it is important to reduce the signal energy outside the OFDM frequency band. This can be achieved by shaping the spectrum of the OFDM signal. Therefore, in this paper, we have proposed a spectrum-shaping method for OFDM-based single-user and multi-user CR systems by using an orthogonal pre-coder in conjunction with raised cosine windowing to effectively reduce out-of-band radiation. The analytical and simulation results show that this method significantly decreases interference to licensed users while maintaining BER performance, though it slightly degrades the PAPR, which can be improved with a suitable reduction technique. Monte Carlo simulations validate the accuracy of our derived analytical expressions.
  Keywords: cognitive radio; OFDM; spectrum shaping; pre-coding; sidelobe suppression; raised cosine windowing; PAPR.
  DOI: 10.1504/IJWMC.2024.10068790
   
  
• Reinforcement learning framework based on hybrid honey badger-cat swarm optimisation for media access control protocol in WSN  
  by B. Ramesh, A. Rajani 
  Abstract: Adaptive models that modify a network's response over time are required for wireless networks. This paper establishes Parameter Optimized Based Reinforcement Learning Media Access Control (PORL-MAC), a new MAC protocol for Wireless Sensor Networks (WSN) using a hybrid optimization strategy. The latest protocols utilize adaptive duty cycles for later optimization of energy utilization. In this research, the nodes actively infer other node states by utilizing an optimized reinforcement learning-based controlling mechanism to maximize throughput for a large number of traffic scenarios. In reinforcement learning, the optimization of parameters in RL takes place by utilizing the hybrid algorithm named Hybrid Honey Badger-Cat Swarm Optimization (HHB-CSO). The experimental result indicates reduced computational complexity for practical applications in WSN. The throughput analysis is validated thus; it shows 12.5%, 15.8%, 3.7%, 7.07% and 3.29% better performance than DHOA-PORL-MAC, HHO-PORL-MAC, CSO-PORL-MAC, HBA-PORL-MAC, and DQN-RL.
  Keywords: wireless sensor network; media access control protocol; hybrid honey badge-cat swarm optimisation; parameter optimised based reinforcement learning media access control.
  DOI: 10.1504/IJWMC.2024.10068936
   
  
• Effects of obstacle BR on premixed hydrogen flame propagation behaviours in obstructed duct  
  by Guanbing Cheng, Yaolin Li, Wentao Zhen, Fangyu Liu 
  Abstract: Because of energy shortage and fossil fuel pollution issues, hydrogen is considered as a promising alternative fuel. Effects of three BR obstacles were examined on premixed hydrogen flame propagation characteristics in duct. Physical and calculated models of shock duct were established by Fluent. Turbulent and combustion models were provided. Flame velocity, pressure and shape changes were examined. Hydrogen flame undergoes slow, fast deflagration, DDT, and steady detonation process. High BR obstacle increases flame velocity and pressure, and also shortens flame characteristic parameters. The calculated detonation parameters agree with experimental ones. The sphere, finger and tooth flame are observed. Both flame surface area increase and interaction between flame and vortex accelerate the flame propagation in slow deflagration. Interaction between flame and shock wave is main physical mechanism in fast deflagration and DDT. The high BR obstacle raises oscillations of flame velocity and the wave pressure, but also produces more energy loss.
  Keywords: hydrogen; shock duct; flame propagation; obstacle BR; velocity and pressure; numerical schlieren.
  DOI: 10.1504/IJWMC.2024.10069185
   
  
• Video inpainting using hybrid DMN-EfficientNet with LGSR  
  by Manjunath R. Hudagi, Lingaraj A. Hadimani, Sachin A. Urabinahatti, Ramesh A. Medar, Abhinandan P. Shirahatti 
  Abstract: Video inpainting attempts to take off a specific area of a video or object and replace it in a visually consistent way. Identifying any interesting object or event has become a very time-consuming task because there is far more recorded video than the operators can watch. To overcome this, the video inpainting approach, Deep Maxout Network-EfficientNet (DMN-EfficientNet) is proposed in this paper. The input videos are subjected to video frame extraction. Then, residual frame extraction is performed using the Deep Residual Network (DRN). The extracted residual frames are then subjected to initial level restoration using Low-rankness guided Group Sparse Representation (LGSR). The output generated and the extracted residual frames are used as the input to the DMN-EfficientNet for video inpainting. The DMN-EfficientNet is devised using Deep Maxout Network (DMN) and EfficientNet. The performance of DMN-EfficientNet is estimated with metrics, like Second Derivative Measure of Enhancement (SDME), Structural Similarity (SSIM),
  Keywords: video inpainting; deep residual network; low-rankness guided group sparse representation; deep maxout network; EfficientNet.
  DOI: 10.1504/IJWMC.2024.10069545
   
  
• 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
   
  
• Next-Day Delivery timeliness and modelling and algorithm application of single-level warehouse network in supply chain  
  by Xiaoping Chen, Zixia Chen, Tingquan Gu 
  Abstract: In the context of rapid development of intelligent supply chain and e-commerce, enterprises are facing the constantly upgrading demand of consumers for fast delivery. This article innovatively explores the optimization of single-level warehouse network configuration, with a focus on improving the efficiency and reliability of "next day delivery" services. Through system modeling and the application of improved algorithms, this paper constructs a new mixed integer linear programming model that combines complex simulated annealing genetic algorithm. This research not only maximizes the timeliness of B2C electronic product delivery, achieving a delivery rate of over 70%, but also minimizes logistics costs. Through the verification of case data provided by the first "JD Logistics Cup" competition, the optimized layout of the supply chain single-level warehouse network obtained in this article significantly shortens the delivery time of orders and ensures the achievement of the goal of "reducing costs and increasing efficiency" for enterprises.
  Keywords: single-level warehouse network optimisation; RSC location selection; ‘next-day delivery’ service; simulated annealing algorithm.
  DOI: 10.1504/IJWMC.2024.10069593
   
  
• Prediction of the environmental quality of a central province based on GRA-BO-LSSVM  
  by Fang He, Shuliang Cheng, Tianyu Zhao, Jing Zhu 
  Abstract: With continuous improvements in living standards, environmental issues have gradually attracted increasing attention. The factors affecting ecological environment quality are complex. Exploring the key factors and analysing future development trends are conducive to improving the environmental governance system and providing a theoretical basis for the governance of environmental problems. An ecological environment quality prediction model based on the combination of grey relational analysis and Bayesian optimized least squares support vector machine was presented in this study. The key indicators affecting environmental quality were identified through grey relational analysis to reduce the input variables in the model. The Bayesian evidence framework was used to optimize least squares support vector machine algorithm for establishing an environmental quality prediction model. The results indicated that the key factors affecting water environmental quality were comprehensive sewage plant discharge compliance rate, harmless treatment rate of domestic waste, comprehensive compliance rate of wastewater discharge, total energy consumption, population, wastewater discharge, per capita annual disposable income of urban residents, and chemical oxygen demand. The grey relational degrees were 0.978, 0.958, 0.955, 0.931, 0.931, 0.917, 0.896, and 0.865, respectively.
  Keywords: environmental quality; grey relational analysis; Bayesian evidence framework; least square support vector machine.
  DOI: 10.1504/IJWMC.2025.10069594
   
  
• 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
   
  
• Research on dynamic task allocation model and method of multi-agent logistics AGV based on improved contract net  
  by Tongjuan Wu, Baokun Lv, Fangjie Fu, Zifang Tang 
  Abstract: In order to improve order picking efficiency, using logistics AGV instead of manual operation has become a main trend. This paper studies the dynamic task allocation problem of Multi-Agent Logistics AGV based on improved contract network algorithm. Aiming at the low efficiency and scalability of task allocation in the traditional contract network, and without considering the dynamic environment, this paper first describes the dynamic task in detail. Then, we propose an improved contract network task allocation model, which connects the trust with the task acquaintance database, sets the time limit for managers to wait for bids, the bidding threshold and the buffer pool, limits the number of bids, and reduces the waiting time. Finally, simulation experiments are carried out to verify the effectiveness and correctness of AGV dynamic task allocation method based on improved contract network.
  Keywords: improved contract net; dynamic task allocation; AGV; multi-agent system.
  DOI: 10.1504/IJWMC.2023.10070184
   
  
• Fundus Image Segmentation Method based on Improved DeepLabv3+ NetworkFundus image segmentation method based on improved DeepLabv3+ network  
  by Liguo Zheng, Yingying Ge, Meili Zhu, Huican Li, Yanlin He, Caixia Zheng 
  Abstract: Accurate optic disc (OD) and optic cup (OC) segmentation in fundus images is crucial for the automatic diagnosis and treatment of certain diseases. However, due to blurry boundaries and vessel occlusion in retinal fundus images, segmenting OD and OC remains a challenging task. This paper proposes an improved DeepLabv3+ network, which contains a novel sparse atrous spatial pyramid pooling (SparseASPP) module and an improved decoder architecture. Specifically, SparseASPP can enhance the ability of DeepLabv3+ to integrate multi-scale feature information, while the improved decoder architecture can effectively fuse low-resolution deep feature information to enhance the networks segmentation performance. The proposed method achieves significant improvements in segmentation accuracy on the RIGA+ dataset (including two training subsets and three test subsets) and the REFUGE dataset. These results demonstrate that our method outperforms DeepLabVv+ and other comparison methods.
  Keywords: DeepLabv3+ network; Sparse atrous spatial pyramid module; optic cup and optic disc segmentation; deep learning; fundus images.
  DOI: 10.1504/IJWMC.2023.10070250
   
  
• Insulator defect detection based on improved YOLOX  
  by Jinbao Meng, Yan Wang, Zhu Xu, Juanyan Fang 
  Abstract: Detecting insulator defects is a crucial stage in the process of inspecting a power grid. To increase the accuracy of insulator defect identification, this study proposes a detection technique based on an enhanced version of the YOLOX algorithm, which can improve the accuracy of insulator defect detection while maintaining the detection speed. The algorithm adds the Efficient Channel Attention (ECA) mechanism to the Spatial Pyramid Pooling (SPP) based on YOLOX-s, i.e., ECA-SPP, adds the ECA mechanism to the feature pyramid for cross-channel interaction, suppresses interference information and strengthens the models focus on target features according to the generated corresponding channel weight values, and then fuses the recalibrated feature maps with deeper features to improve the target feature expressiveness. The Complete Intersection over Union (CIoU) is utilised to calculate the loss, while the distance between the centroids of the two frames and the aspect ratio is factored into the penalty term, and the loss function is continuously changed and updated to accelerate the convergence speed of the model. The testing findings demonstrate that the revised mean of Average Precision (mAP) algorithm for YOLOX achieves 93.21% accuracy, which is 6.17% higher than the original algorithm.
  Keywords: insulator defect detection; YOLOX; efficient channel attention; CIoU; ECA-SPP.
  DOI: 10.1504/IJWMC.2023.10070251
   
  
• 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
   
  
• Applications of electronic nose in gas detection: a review  
  by Guosheng Mao, Yiyi Zhang, Pengfei Jia 
  Abstract: Electronic nose (E-nose) employs the sensor array and pattern recognition algorithm to simulate the mammalian olfaction. Widely used in gas detection, E-nose has outperformed the traditional gas detection methods in many aspects. E-nose is usually used in qualitative classification and quantitative regression analysis in the field of gas detection, that is, to perform the identification on the mixed gases. Currently, the most common used sensors for E-nose can be divided into conductivity sensor, piezoelectrical sensor, MOSFET sensor and optical sensor, the most widely used pattern recognition algorithms are classical machine learning, deep learning and hybrid models. This article aims to summarize the sensors and pattern recognition algorithms widely used in E-nose, and provides reference for the prospective specific application of E-nose.
  Keywords: E-nose; gas detection; sensor; machine learning; classification.
  DOI: 10.1504/IJWMC.2023.10070535
   
  
• 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
   
  
• Performance analysis of cognitive radio NOMA wireless communication systems  
  by Huu Q. Tran 
  Abstract: The rapid expansion of wireless communication necessitates innovative strategies to enhance spectrum efficiency. This paper explores two key technologies: Cognitive Radio (CR) and Non-Orthogonal Multiple Access (NOMA), which address the pressing need for improved spectrum utilization. CR enables unlicensed users to access spectrum dynamically, mitigating scarcity, while NOMA allows multiple users to share the same frequency resources, thereby boosting spectral efficiency. The integration of CR and NOMA (CR-NOMA) systems holds promise for maximizing spectrum efficiency and accommodating the growing number of connected devices. We present a thorough performance analysis of CR-NOMA systems, focusing on critical metrics such as outage probability, achievable data rates, and overall spectral efficiency. The study includes a detailed system model, analytical derivations for key performance indicators, and validation through extensive simulations. Our findings offer valuable insights for optimizing system design and operation, contributing to the practical implementation of CR-NOMA technologies. This research aims to inform the development of next-generation wireless networks capable of efficiently supporting diverse applications and services, particularly in environments with stringent spectrum demands and varying Quality of Service (QoS) requirements.
  Keywords: achievable data rate; cognitive radio; CR-NOMA; non-orthogonal multiple access; outage probability; quality of service; spectrum efficiency; system performance analysis.
  DOI: 10.1504/IJWMC.2025.10070721
   
  
• 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
   
  
• Study and analysis of various optimisation techniques for sentiment classification: a challenging overview  
  by Jyotsna Anthal, Bhavna Sharma, Jatinder Manhas 
  Abstract: A detailed survey is elaborated in this paper for classification of optimization algorithms utilized for sentiment classification. The reviews are gathered from 50 research papers and methodologies are classified depending on algorithms like Deep learning based techniques, language model based approaches, optimization and machine learning based algorithms. Moreover, the merits and demerits related to each research works are explained in this survey paper. The analysis is performed using the classification algorithms, evaluation metrics, tool, dataset used, and publication year. From analysis, it is proven that Deep learning based techniques is the category of algorithm is the widely used algorithm for sentiment classification. Similarly, Python is the most frequently used implementation tool in most of the research papers, and the evaluation metrics, like accuracy, precision, recall, and F1-measure are widely employed in classification algorithms. The research papers that are mostly taken for this survey was in the year of 2021.
  Keywords: sentiment classification; reviews; textual expressions; integrity; natural language processing.
  DOI: 10.1504/IJWMC.2024.10070894
   
  
• 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
   
  
• Research on task allocation model of logistics robot based on market coordination  
  by Tongjuan Wu, Baokun Lv, Fangjie Fu, Zifang Tang 
  Abstract: With the transformation and upgrading of intelligent warehousing systems, large-scale logistics robots have gradually replaced manual operations. Aiming at the problem of large-scale picking task allocation of logistics robots, this paper proposes a task allocation model based on market coordination. Specifically, according to the degree of task similarity, the total time of robot picking tasks in a wave is used as the objective function, which is established as a minimization model, and then a heuristic auction algorithm based on market coordination is designed to solve the model and obtain a task allocation strategy. In the simulation experiment, by comparing and analyzing the balance effect of the picking table and the saved task picking time, we verify the practicality and effectiveness of task allocation strategy and model in large-scale “goods to person” intelligent warehousing systems.
  Keywords: intelligent warehousing system; task allocation; market coordination; auction algorithm.
  DOI: 10.1504/IJWMC.2023.10071193
   
  
• Non-cooperative game-based overlapping community detection algorithm  
  by Jiqiang Yao, Deng Kun, Xingyan Liu 
  Abstract: Owing to the drawbacks of current non-cooperative game-based community detection algorithms, which include excessive iteration and high randomness in the results, this paper proposes a non-cooperative game-based overlapping community detection algorithm. Firstly, the algorithm defines the node influence coefficient to identify low-influence nodes. These nodes are then modeled as players in a community game, where the game ends when a player cannot improve their utility by changing strategies. Finally, experimental results using artificial benchmark networks and real networks demonstrate that this algorithm outperforms other comparative algorithms. The algorithm achieves at least a 10% improvement in NMI and a 15% improvement in EQ compared to the other algorithms.
  Keywords: complex networks; community detection; utility functions; game theory.
  DOI: 10.1504/IJWMC.2023.10071194
   
  
• 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