International Journal of Wireless and Mobile Computing (37 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.
  
  
• Two novel blind CFO estimation techniques for CP-OFDM  
  by Mohammadreza Janbazi Roudsari, Javad Kazemitabar, Hossein Miar-Naeimi 
  Abstract: In this paper, two new cyclic prefix (CP) based blind carrier frequency offset (CFO) estimation methods for orthogonal frequency division multiplexing (OFDM) transmission over multipath channels are proposed. In doing so, we first estimate the maximum delay of the fading channel. We borrow the concept of remodulation introduced in earlier works and use the repetitive structure of CP to calculate a maximum-likelihood based measure. In the first proposed method we use particle swarm optimisation aided search on all possible samples to find the optimal set. This technique provides performance improvement at the expense of more complexity. Then, in a second proposed method, we average over the optimal set of samples to estimate CFO. The second technique provides a major improvement over previous works while offering less complexity. Simulation results corroborate that both our proposed methods significantly decrease the mean square error.
  Keywords: orthogonal frequency division multiplexing; carrier frequency offset; cyclic prefix.
  
  
• 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.
  
  
• Optimisation of transportation vehicle scheduling based on FG-ABC algorithm in 5G scenarios  
  by Yuanyuan Hu, Yan Zhang 
  Abstract: This paper studies the vehicle scheduling problem of feeder bus, constructs a mixed integer nonlinear programming model to optimise the departure schedule of electric bus, and uses the improved artificial bee colony algorithm to solve the model. Then, based on the 5G communication system, an urban rail transit vehicle transportation scheduling management system was constructed to achieve transportation bus data collection, task scheduling management, vehicle management, station management, etc. The experimental results obtained by solving the model through parameter calibration show that the total cost of the optimised timetable bi-directional operation route is reduced by an average of about 22.83%, which verifies the good effect of the optimisation model constructed in the study. The experimental results show that the model can handle nonlinear objectives and constraints to reflect the nonlinear relationships in reality, and has good application effects for the management of transportation bus scheduling.
  Keywords: electric shuttle bus; artificial bee colony algorithm; 5G scene; scheduling optimisation; mixed integer non-linear programming model; FG-ABC.
  DOI: 10.1504/IJWMC.2024.10065565
   
  
• Energy and trust-aware hybrid optimisation for RPL routing in mobile internet of things  
  by C. Prabhavathi, S. Meera 
  Abstract: The RPL specification encrypts control messages, internal attackers and self-serving behaviour can still exploit RPL. To encounter the issues of the lack of robust security mechanisms in RPL, this paper concentrates on the modelling of energy and trust-aware RPL routing models in mobile IoT via an advanced hybrid optimization algorithm. This proposes the term Manta-Ray Updated White Shark Optimization (MRUWSO). The four stages are: DIO messages are broadcast by the root node, and every node that receives a DIO message updates its routing tables with the root. The optimal route establishment is done by a proposed MRUWSO algorithm. Thirdly, optimal routing will be done under the improved trust evaluation function that ensures the authentication. Lastly, if the preferred parent changes or a route is updated, every time, a DAO message needs to be sent. The proposed MRUWSO attained a minimal delay of 1.21
  Keywords: RPL routing; internet of things; optimisation; security; link quality.
  DOI: 10.1504/IJWMC.2024.10067190
   
  
• A high-resolution angle estimation method for distributed aperture arrays  
  by Xiang Sha, Guolong Cui 
  Abstract: To achieve accurate direction-of-arrival (DOA) estimation of far-field targets, distributed aperture arrays are employed in this work. Compared with traditional aperture array radars, distributed aperture arrays can expand the physical aperture of the array, leading to improved angle estimation performance. However, the spacing between subarrays in distributed aperture arrays is significantly greater than half a wavelength, violating the Nyquist sampling theorem. This results in multiple grating lobes in the echo directional diagram, causing ambiguity in angle estimation. A novel high-resolution angle estimation method based on subarray angle estimation for distributed aperture arrays is proposed. The method follows a two-step coarse-fine process and is applicable to arbitrarily configured distributed arrays and effectively addresses the issue of angle ambiguity. The estimation performance surpasses existing methods, achieving an estimation error of about 0.04
  Keywords: angle estimation; distributed aperture arrays; ambiguity; echo directional diagram.
  DOI: 10.1504/IJWMC.2024.10067560
   
  
• Performance analysis of naive vehicular named data networks  
  by R. Nithin Rao, Rinki Sharma 
  Abstract: Vehicular Named Data Networks (VNDN) is a content centric approach for vehicle networks. The fundamental principle of addressing the content rather than the host, suits vehicular environment. There are numerous challenges such as interest/data flooding, routing, naming schemes, channel constraints and security, among others. The proposed work aims to analyse the behaviour of the na
  Keywords: content forwarding; caching; naming; delay; data packets; timestamp.
  DOI: 10.1504/IJWMC.2024.10068030
   
  
• Improved model for identifying rice panicle disease based on MobileNetV2  
  by Le Yang, Huibin Long, Xiaoyun Yu, Huanhuan Zhang, Shuang Xu, Yingwen Zhu 
  Abstract: Rice plays a crucial role in agriculture, but a major issue is that various disasters and diseases of rice will greatly reduce rice production, especially affecting rice required for human consumption, and the rice seeds sown in the next year will also encounter problems.The learning nature of convolutional neural networks is used to identify rice ear diseases, and rice ear disease recognition rate is improved by modifying the network structure and integrating other network structures that can enhance deep learning for picture recognition. In this study, MobileNetV2 is used as the main network and trained on ImageNet using migration learning. The underlying convolutional layer weights are frozen to conserve resources. Then, the pretrained MobileNetV2 network is fused with BAM blocks to develop a new network. Experiments show that the efficiency and recognition rate of this method are improved, with an average recognition rate of 98.18%. The generalization ability of the model is then tested on the PlantVillage dataset, with an average recognition rate of 98.7%.
  Keywords: rice spike; migration learning; convolutional neural network; BAM block.
  DOI: 10.1504/IJWMC.2024.10068521
   
  
• Object detection method for improving the generalisation of deep reinforcement learning  
  by Junyu Sun, Fan He, Yong Liu, Menhua Zheng 
  Abstract: One of the key reasons for the wide gulf between the goals of deep reinforcement learning (DRL) and its practical applications is that trained intelligences tend to over fit to local features in the training data, and the models are trained with a mixture of perception and decision making in a single network, which is very sensitive to small changes in the environment, which restricts agent from learning real rules from experience. In this paper, we use a target detection model to recognize objects in a visual scene, learn perceptual level knowledge, and use the generalization ability of the target detection model to reduce the observation overfitting of the agent and improve the robustness of the agent in vision.
  Keywords: DRL; DQN; moving object detection; relation network; self-supervised reinforcement learning.
  DOI: 10.1504/IJWMC.2024.10068674
   
  
• Implementation of 16-QAM OFDM for TV white space spectrum efficiency gain in AWGN channel  
  by Khola Azhar, Muhammad Omar, Muhammad Usman, Saad Saleem Khan, Stephen Larkin 
  Abstract: Wireless communication has grown tremendously in recent years, impacting nearly every feature of our lives. The increased exigency for wireless broadband services leads to a huge demand for dynamic spectrum access, such as TV White Spaces (TVWS). The possibility of interference increases with the high density of users, reducing the Spectrum Efficiency (SE). SE improvement techniques have been utilised to resolve network congestion issues. This paper presents the Orthogonal Frequency Division Multiplexing (OFDM) wireless communication model with the Quadrature Amplitude Modulation (16-QAM) technique for TVWS SE gain. The performance analysis of the proposed model is based on the parameters such as Packet Loss, Bit Error Rate (BER), and Signal-to-Noise Ratio (SNR). The constellation diagrams and signal trajectories are used to evaluate the efficacy of the modulation technique. Zero packet loss is achieved by implementing the 16-QAM OFDM, demonstrating efficient usage of TVWS with extended coverage and almost-homogeneous data distributions.
  Keywords: TVWS; OFDM; 16-QAM; bit-error rate; packet loss; AWGN; spectral efficiency.
  DOI: 10.1504/IJWMC.2024.10068733
   
  
• 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
   
  
• A fault propagation model for complex systems based on community structure  
  by Tian Yuling 
  Abstract: The traditional fault localisation methods are always based on rough modelling of causal relationship between fault types and performance, ignoring the complex network dynamic characteristics of complex systems, especially the influence of community structure on fault propagation, which leads to the poor fault localisation result. In this paper, a new fault propagation model based on community structure is proposed, firstly the structure and characteristics of complex equipment system is transformed into a complex network, and then the community structure is detected, finally we realise time-continuous fault diagnosis, incorporate the dynamics of the immune networks to the fault propagation model. In the stage of detecting community structure, the improved SimRank method is adopted and the fault rate is set according to the calculated object, which effectively improves the defect that the traditional algorithm is easy to be interfered by independent objects. The experimental results show that the algorithm based on community structure can achieve accurate fault localisation.
  Keywords: fault propagation; community structure; SimRank; idiotye immune network; fault localisation.
  DOI: 10.1504/IJWMC.2024.10068808
   
  
• 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
   
  
• Poor and rich squirrel algorithm-based Deep Maxout network for credit card fraud detection  
  by Annu Paul, Varghese Paul 
  Abstract: This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation phase, transforming data using Yeo-Johnson (YJ) transformation. Then, the feature selection procedure is done by the Fisher score for creating the unique and significant features. Next, based on the selected textures, the data augmentation mechanism is done using the oversampling model. At last, the fraud detection is carried out by the Deep Maxout network, which is trained by the proposed PRSA optimisation algorithm, derived by integrating Poor and Rich Optimisation (PRO) and Squirrel Search Algorithm (SSA). The integration of parametric features of the PRSA algorithm effectively trained the classifier to update weights to generate the best solution by considering fitness measures. The proposed method achieved the best accuracy, sensitivity, and specificity measures of 0.96, 0.95 and 0.94, respectively.
  Keywords: credit card; deep learning; fraud detection; data augmentation; data transformation.
  DOI: 10.1504/IJWMC.2025.10068613
   
  
• Blockchain with drug traceability in medical supply chain with Henry gas solubility optimisation algorithm  
  by P. Yamini Devi, P. Sriramya 
  Abstract: The drug traceability model is used for ensuring drug quality and its safety for customers in the medical supply chain. The healthcare supply chain is a complex network, which is susceptible to failures and leakage of information because of cyber security attacks. Hence, this research aims to design a blockchain to perform traceability of drugs using Henry Gas Solubility Optimisation (HGSO). The entities include distributors, wholesalers, retailers, blockchain and patients. This model comprises initialisation, registration, key generation, authentication, drug distribution with traceability and key generation phases. Any exchange of medicines needs the approval of both sender and receiver. The drugs are registered in the blockchain, which then generates a secret key that is obtained using HGSO. The verification and authentication of drugs are performed after the authentication process. The implemented model outperformed with the response time, memory, normalised variance, and privacy of 32.678 sec, 84 MB, 0.958 and 0.958.
  Keywords: blockchain; drug traceability; medical supply chain; key generation; authentication.
  DOI: 10.1504/IJWMC.2024.10067239
   
  
• Design and analysis of a coupled-feed, reconfigurable antenna for wireless application  
  by Santimoy Mandal, Saindhab Chattaraj, Biswajit Mondal, Chandan Kumar Ghosh 
  Abstract: In this research, a planar reconfigurable antenna consists of multi-frequency broadband characteristics is designed for wireless communication systems application. It consists of inverted C-structured feeding strip and a shorting strip with three twigs for Long Term Evolution (LTE) band 46, 47 is proposed. The proposed antenna can be used for U-NII (licensed assisted access) and U-NII-4 (cellular vehicle to everything) with Time Division Duplexing (TDD) mode having frequency of 5.2 to 5.9 GHz. The simulated bandwidth (3:1 VSWR) of the projected antenna is (3.92 to 4.04 GHz) is 121 MHz at low band and 1236 MHz (5.264 to 6.5 GHz) at the high band. By bending the strip line and the use of coupled feed we achieved the miniaturisation and broadbandisation of the projected antenna. The total volume of the antenna is 12 mm × 50 mm × 1 mm, which is suitable for different wireless communication systems applications.
  Keywords: WWAN; wireless wide area network; LTE; long-term evolution; TDD; time division duplexing; VSWR; coupled feed; shorting strip.
  DOI: 10.1504/IJWMC.2024.10066947
   
  
• Research on optimisation of fresh product closed-loop supply chain based on DWOA algorithm in mobile environment  
  by Jiaming Shen 
  Abstract: In today's society, the timely supply of fresh agricultural products helps ensure the freshness and quality of food and reduce health risks such as food poisoning. Timely picking and transportation can reduce the risk of food spoilage. This study conducts research on robust fuzzy optimisation of green closed-loop fresh product supply chain network. With the goal of minimising costs and minimising carbon emissions, the research uses fuzzy program measurement methods to process existing models and introduces the concepts of mutation and crossover of differential algorithms to improve the whale optimisation algorithm. The results show that the fitness values of this method in the test functions F8 and F20 are less than -8000 and -3.2, respectively, and it has high convergence accuracy and speed.
  Keywords: improved differential whale algorithm; fresh products; closed-loop supply chain optimisation; fuzzy optimisation; robust optimisation.
  DOI: 10.1504/IJWMC.2024.10064920
   
  
• Application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network  
  by Promod Kumar Sahu, Hemanta Kumar Pati, Sateesh Kumar Pradhan 
  Abstract: Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases; if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems.
  Keywords: GSM; handoff call dropping; mobile cellular networks; new call blocking; optimal channel.
  DOI: 10.1504/IJWMC.2024.10066887
   
  
• Dynamic resources optimisation and interference management-based green communication protocol for 5G  
  by Dhanashree Shukla, Sudhir D. Sawarkar 
  Abstract: The increasing number of connected devices in 5G HetNets emphasises the need for energy-efficient communication. UE-to-UE communication, focusing on energy-efficient routing, handover, and optimal UE selection while maintaining throughput, is crucial. We introduce the Dynamic Resources Optimisation and Interference Management-based Green Communication Protocol (DROIM-GCP) using type-2 fuzzy logic. DROIM-GCP operates in two phases: resource optimisation and interference management. In resource optimisation, UEs periodically measure Bandwidth Utilisation Factor, Link Reliability Factor, Energy Utilisation Factor, and Velocity Normalisation Factor. These inputs are processed via fuzzy logic to select the optimal UE for transmission. In interference management, handover candidates are identified based on RSSI, Velocity Normalisation Factor, Energy Utilisation Factor, and Link Reliability Factor. Target femtocells are selected using Bandwidth Utilisation Factor and Channel Occupancy. DROIM-GCP aims to maximise throughput and packet data rate while minimising energy consumption and delay. Simulations show it outperforms existing methods, improving energy efficiency by 6.25-12.79%.
  Keywords: green communication; heterogeneous network; handover; UE-to-UE communication; resource optimisation; type-2 fuzzy logic.
  DOI: 10.1504/IJWMC.2024.10065549
   
  
• Data mining and learning behaviour analysis of French online education data-driven teaching based on generative adversarial network improvement Apriori algorithm  
  by Liqun Zhang 
  Abstract: With the rise of online education, French learning platforms are gaining popularity. Improving learning efficiency is a key challenge. This study uses the Apriori algorithm for data mining, enhances it with adversarial networks, and constructs a data-driven teaching system for French online education. The improved Apriori algorithm shows average accuracy, recall and F1-values of 90.1%, 0.92 and 0.93, respectively, making it ideal for mining French online education data. This system provides real-time, personalised feedback, helping optimise learning behaviour and significantly boosting learning outcomes. Analysis of behaviours like login times, browsing time and forum posts shows a positive correlation with learning success, allowing for targeted learning plans to enhance efficiency.
  Keywords: data mining; visualisation; French; online education; data-driven; Apriori algorithm.
  DOI: 10.1504/IJWMC.2024.10065304
   
  
• Construction of target detection teaching platform based on improved YOLOv5s algorithm  
  by Gongfa Li, Housheng Zhu, Xinjie Tang, Du Jiang, Chunlong Zou 
  Abstract: Measurement and control technology and instrumentation, as a high-tech intensive specialty integrating disciplines such as electrical engineering, optics, control theory, computer science and instrumentation science, has a strong interdisciplinary nature. This presents challenges to traditional teaching methods which often fail to fully cultivate students' practical and innovative abilities. To address this, a multi-level teaching model focused on the cultivation of practical and innovative skills has been developed, reforming the original experimental teaching mode and integrating the establishment of experimental teaching platforms. The proposed teaching model integrates theoretical knowledge with practical application, utilising improved experimental platforms such as YOLOv5s for hands-on teaching. The reformed teaching model has enhanced students' comprehensive abilities and achieved noticeable success in teaching practice.
  Keywords: cultivation program; cultivation mode; practice teaching; YOLOv5.
  DOI: 10.1504/IJWMC.2024.10066442