International Journal of Wireless and Mobile Computing (63 papers in press)

Regular Issues

• Multi-objective workflow scheduling in the cloud environment based on NSGA-II  
  by Tingting Dong, Chuangbai Xiao 
  Abstract: The emergence of cloud computing offers a novel perspective to solve large-scale computing problems. Workflow scheduling is a major problem in the cloud environment, and parallelism and dependency are two important characteristics of tasks in a workflow, which increases the complexity of problem. Workflow scheduling is also a multi-objective scheduling problem, and task execution time and cost are the two extremely significant goals for users and providers in the cloud environment. Existing heuristic algorithms are popular, but they lack of robustness and need to be revised when the problem statement changes. Evolutionary algorithms have a complete algorithm system, which is widely used in the multi-objective scheduling problem. In this paper, Nondominated Sorting Genetic Algorithm-II (NSGA-II) is utilized to solve the workflow scheduling problem aiming at minimizing the task execution time and cost. Some real-world workflows are used to make simulation expriments, and comparative simulations with genetic algorithm are given. Results show that NSGA-II is effective for the workflow scheduling.
  Keywords: cloud computing; workflow scheduling; non-dominated sorting genetic algorithm-II; multi-objective scheduling.
  
  
• Machine learning-based approach for the detection of phishing websites  
  by Yaqin Wang, Jingsha He, Nafei Zhu 
  Abstract: Compared with traditional forms of crime, cyber-attacks and cyber-crimes have removed the limitation on distance and speed. With very low cost, phishing is a very effective way of launching network attacks with the purpose of obtaining sensitive information about users, such as username, password and payment voucher, through counterfeiting regular websites so as to steal users private information and personal property using the obtained information. Both the trust that internet users have and the development of the internet itself can be affected by this kind of attack, making it imperative to detect this type of attack. Many methods have been proposed for the detection of phishing websites in the literature in recent years based on techniques ranging from conventional classifiers to complex hybrid classifiers. Meanwhile, although convolutional neural networks (CNNs) can achieve very high accuracy in classification tasks, not much research has been done on the use of CNNs for the detection of phishing websites. This paper proposes a CNN-based scheme for the detection of phishing websites in which four dimensions of the features of phishing websites are defined and CNN is used to extract local features. The proposed CNN-based scheme is compared with several machine learning-based methods on the effectiveness of detecting phishing websites, which shows that the proposed scheme can achieve the accuracy rate of 97.39% and is better than the other classification methods in terms of accuracy, recall and F1-score.
  Keywords: convolutional neural network; classification; machine learning; phishing website detection.
  
  
• Cold-start recommendation algorithm based on user preference estimation  
  by Biao Cai, Jiahui Xin, Xu Ou 
  Abstract: In order to improve the dilemma of collaborative filtering in the face of cold start and achieve a better balance between accuracy and diversity, this paper considers the influence of user characteristics on recommendation results and proposes a Preference Estimation Network (PEN) based on maximum likelihood. PEN uses the user's characteristic information to estimate the user's preference information, and represents the user's preference vector with the item's label system. On this basis, PEN-Rec, an improved version of the traditional recommendation algorithm based on preference vector estimation and particle swarm optimisation, is proposed. Finally, the PEN-Rec algorithm is compared with the benchmark algorithm on six public evaluation indicators using open datasets, and the experimental results show that the accuracy, diversity and novelty of the PEN-Rec algorithm are all improved.
  Keywords: recommendation; feature impact; preference estimation; label vector.
  
  
• Link prediction with Fusion of DeepWalk and node structural information  
  by Xinhui Xiang, Biao Cai, Yunfen Luo 
  Abstract: The existing link prediction algorithms are mainly based on structural information or network embedding, but minimal research has been conducted on the fusion of these algorithms. It is found that the structure-based algorithms have high accuracy, but the complexity is higher owing to the introduction of high-order structural information while the network embedding algorithms have low complexity, but because the structural information of the node is not fully used, the accuracy is not as good as some structure-based algorithms. Therefore, by combining the structural attributes of nodes and the degree of convergence between node pairs, this paper proposes two new improved similarity algorithms the similarity algorithm based on edge-degree DeepWalk cosine (EDDWC) and the similarity algorithm based on preferential attachment mechanism DeepWalk cosine (PADWC). Experiments show that the performances of the proposed algorithms are greatly improved over that of the DeepWalk algorithm, and they are also better than other link prediction algorithms.
  Keywords: link prediction; DeepWalk; edge-degree; preferential attachment mechanism; cosine similarity.
  
  
• 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 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.
  
  
• A novel trust-based approach for intrusion detection architecture in wireless sensor networks  
  by Mr. Jeelani, Kishan Pal Singh, Aasim Zafar 
  Abstract: Wireless sensor networks (WSNs) is a new technology that can be used to monitor the environment. Because sensor nodes in wireless sensor networks are installed in an open environment, they are more vulnerable to attacks. The sensor network lifetime improvement is dependent on minimum energy use. Protection is also a major concern when it comes to designing protocols for multi-hop secure routing. The results based on trust have proven to be more effective in addressing malicious node attacks. In this article, we propose a novel trust-based approach for intrusion detection architecture (IDA) in a wireless sensor network that is called the trust-based approach for varying nodes with energy (TBNE) model. TBNE finds the misbehaving nodes in the network. The structure is based on the trust model for secure communication in WSN and improves the performance of nodes. The simulation has been done with QualNet 5.0 simulator.
  Keywords: wireless sensor network; throughput; packet delivery ratio.
  
  
• Data sharing with privacy protection based on blockchain and federated learning in edge computing enabled IoT  
  by Shiqiang Zhang, Zhenhu Ning 
  Abstract: Data sharing of Internet of things devices is a powerful means and technology to break the data island in the era of big data. However, frequent privacy leaks indicate that privacy protection has become one of the most urgent problems in data sharing. The existing data sharing schemes usually provide data to the data demanders through access control authorisation through a third-party organization. This way can protect the privacy of data to a certain extent. But the biggest problem is that the data owner will lose control of the data, which increases the risk of privacy disclosure. In this paper, we proposed a new data sharing scheme based on blockchain and federated learning. The data sharing problem is transformed into a machine learning problem. The IoT devices train the model locally and use differential privacy technology to avoid privacy leakage, and ensures its security through the blockchain network aggregation model.
  Keywords: data sharing; blockchain; federated learning; differential privacy; edge computing; IoT.
  
  
• 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.
  
  
• Technology adoption of enablers of 5G networks for m-learning: an analysis with interpretive structural modelling and MICMAC  
  by L. Kala, Hameed T. A. Shahul, V.R. Pramod 
  Abstract: Mobile learning (m-learning) is one of the real-time applications of 5G technology with an impulsive future. COVID-19 pandemic enhanced the adoption of m-learning over wireless networks by facilitating continued formal education or work from home. This research aims to analyse enablers of 5G networks that enhance real-time m-learning by applying Interpretive Structural Modelling (ISM), a set-theory-based structural modelling method widely employed in many engineering and technology related research fields. Data was collected through questionnaire-based information gathering and from one-to-one discussions with experts. Modelling was performed to identify the correlations among system parameters through a hierarchically structured model. Further, the enablers were classified into different clusters based on their driving powers and dependency with MICMAC analysis, by which the results were validated. The study shows that enablers of 5G will undoubtedly support and uphold the system performance for future real-time scenarios of m-learning by eliminating all the inhibiting parameters of former 4G wireless networks.
  Keywords: 5G; wireless networks; enablers; mobile learning; ISM; MICMAC; driving power dependence.
  
  
• Two-phase approach for the detection and isolation of black hole attack in mobile ad hoc network  
  by Pankaj Khuresha, Sonal Sood, Mandeep Sandhu, Anurag Dixit 
  Abstract: A mobile ad hoc network (MANET) is an infrastructure-less network in which no central controller is present and nodes can communicate with each other independently. Owing to unique nature of the network, malicious nodes can enter the network which triggers various types of attack. The black hole is the attack in which the malicious node does not forward any packets and all the packets will be dropped in the network. In this research work, an approach is proposed for the detection and isolation of black hole attack in MANET. The proposed approach works in two phases: in the first phase the malicious node will be detected and in the second phase the malicious node will be isolated from the network. The proposed methodology is implemented in network simulator version 2 and results are analysed in terms of throughput, delay and packet loss.
  Keywords: MANET; black hole; malicious nodes; clustering; trust.
  
  
• 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.
  
  
• Signal strength and energy based efficient AODV routing algorithm in MANET  
  by Priyanks Pandey, Raghuraj Singh 
  Abstract: In recent years, Mobile Ad Hoc Network (MANET) has become one of the most popular research areas in the wireless networking domain. However, one of the major challenges remains to develop an efficient routing algorithm which depicts par excellence performance on all performance parameters even under highly dynamic network. Ad Hoc On-Demand Distance Vector (AODV) is a generalized routing protocol which establishes routes to destinations on demand in MANET environment and supports unicast as well as multicast routing. Many enhancements have also been proposed in AODV from time to time. These enhancements are based on various features which define a specific environment. But, these enhancements do not perform well on all considered performance metrics such as packet delivery ratio, delay, normalized routing load and throughput in highly dynamic network environment. In this paper, we have proposed an Enhanced version of AODV, namely (ENAODV) algorithm considering two important and additional stability parameters i.e. energy and signal strength along with hop count and sequence number in route selection process. Algorithm has been simulated using NS2 simulator and evaluated under different network conditions with varying maximum speed. Performance of the algorithm has been evaluated to be better on all parameters like throughput, normalized routing load, packet delivery ratio, control overhead and end to end delay than the AODV algorithm.
  Keywords: MANET; signal strength; RWP; AODV.
  
  
• QoS-based handover approach for 5G mobile communication system  
  by Amina Gharsallah, Nouri Omheni, Faouzi Zarai, Mahmoud Neji 
  Abstract: 5G mobile communication systems are in-depth fusions of multi-radio access technologies characterised by frequent handover between cells. Handover management is a particularly challenging issue for 5G networks development. In this article, a novel optimised handover framework is proposed to find the optimal network to connect with a good quality of service in accordance with the users preferences. This framework is based on an extension of IEEE 802.21 standard with new components and new service primitives for seamless handover. Moreover, the proposed vertical handover process is based on an adaptive heuristic model aimed at achieving an optimised network during the decision-making stage. Simulation results demonstrate that, compared to other existing works, the proposed framework is capable of selecting the best network candidate accurately based on the quality of service requirements of the application, network conditions, mobile terminal conditions and user preferences. It significantly reduces the handover delay, handover blocking probability and packet loss rate.
  Keywords: 5G mobile network; ultra-dense network; media independent handover; vertical handover optimisation; fast handover.
  
  
• 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.
  
  
• Towards energy-efficient 5G heterogeneous networks through dynamic small cell zoom and sleep control algorithm  
  by Janani Natarajan, Rebekka B 
  Abstract: The tremendously escalating mobile traffic and bandwidth hungry applications is challenging the network operators to provide guaranteed quality of service (QoS) over wider coverage and effective network resource usage. One of the effective solutions is heterogeneous network (HetNet) comprising an overlay of small cells (SCs) within a macrocell coverage. For enhancement in network energy efficiency (EE), we propose a joint small cell zoom and sleep strategy. The small cell zoom technique involves load-aware adaptive power control of the SCs for optimum network power consumption through lower SBS use together with appropriate user load balance. The small cell sleep method switches the SBSs with higher interference to sleep mode, thereby improving the network capacity as well as power saving. Simulation results show an EE improvement of the proposed sleep and zoom scheme by 25%, 26% and 28%, respectively, compared with three similar benchmark schemes in the literature.
  Keywords: heterogeneous networks; small cells; energy efficiency; small cell zoom; small cell sleep; adaptive power control.
  
  
• 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.
  
  
• Research on system parameter optimisation in electromagnetic tomography technology  
  by Liu Li, Yue Luo, Yao Huang, Lijuan Wu 
  Abstract: Electromagnetic tomography technology (EMT) based on the principle of electromagnetic induction is a multi-phase flow detection technology. It is reconstructed without contact and intervention. The development process of EMT is presented in this paper. The basic physical model is constructed. The internal sensitive field equation is given. The detection values are analysed by the numerical calculation method. It is mainly to establish the sensitivity model and the detection value matrix. By using the control variable method, the effects of the excitation current frequency, current strength on the detection values and phases are compared and analysed. Under the same parameter setting conditions, different imaging algorithms are used to reconstruct the images for models. In the inverse problem, Tikhonov regularization, LBP methods and conjugate gradient algorithm are introduced. The optimal parameters are determined by using parameters of IE (Image Error) and CC (Correlation Coefficient) to evaluate the reconstructed image.
  Keywords: electromagnetic tomography technology; image reconstruction; Ccnjugate gradient algorithm; inverse problem.
  
  
• 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.
  
  
• Energy-efficient dynamic load balanced clustering for MANET  
  by Naghma Khatoon, Vinay Singh, Prakash Kumar 
  Abstract: In mobile ad-hoc network (MANET), enhancing network lifetime is a challenging issue. Clustering is proved to be a suitable solution to increase scalability and MANET lifetime. In this paper, we present an energy-efficient dynamic load-balanced clustering for MANET. For cluster formation, nodes are divided into open set and restricted set. Depending upon the weight of Cluster Head (CH), node join them to form a cluster which make it load balanced. We use technique for self-adjustment of role of CHs dynamically based on fitness factor which is derived from remaining energy and weight of nodes to increase CH lifetime. The proposed method is experimented extensively and compared with related existing algorithms to demonstrate its ascendancy related to various performance metrics like packet delivery ratio, network lifetime, average number of clusters formed and re-clustering required. Also, we demonstrate that the work proposed accomplishes persistent messages and the time complexity is linear.
  Keywords: MANET; cluster head; fitness factor; remaining battery energy; packet delivery ratio.
  
  
• 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.
  
  
• Multi-task learning neural network for monitoring and diagnosis of smart meters in power IoT systems  
  by Ming Zhang, Yong Cui, Lei Wang, Shuang Ji 
  Abstract: With the continuous development of power Internet of Things technology, the scale and complexity of power systems are increasing, and the safe operation of power IoT systems is facing challenges In this paper, an intelligent monitoring and diagnosis model is proposed for smart meter, a key terminal in an IoT system The proposed model is developed using deep learning methods by proposing a novel multi-task learning deep neural network (DNN), which is designed for online perception, monitoring and diagnosis of the operation status of smart power electric meters The proposed method can simultaneously deal with both the regression task for meter pointer localization and the classification task for safety status identification, which support to realize multi-task-oriented deep data mining and intelligent decision-making The experimental results of this paper verify the accuracy and reliability of the proposed method for a real power IoT system, and validates the effectiveness of the method
  Keywords: deep neural network; multi-task learning; computer vision; smart meter; power IoT terminal.
  DOI: 10.1504/IJWMC.2024.10063330
   
  
• Research on three-dimensional perception and protection technology for power construction safety operations  
  by Yan Ke, Hongtao Chen, Zhiyuan Liu, Zhiyong Yang, Lin Song 
  Abstract: The traditional safety supervision of power transmission and transformation operation sites mainly relies on manual and video surveillance, resulting in a relatively low level of intelligent safety supervision in power construction operations This paper aims to conduct research on key technologies for three-dimensional perception and protection of power construction operations to enhance intelligent supervision in China's power construction operations Firstly, it reviews the research progress of power construction operations at home and abroad to provide references for intelligent supervision of power construction operations Secondly, based on the characteristics and safety requirements of power transmission and transformation operations, it conducts research on four key technologies, including three-dimensional protection based on lidar and image recognition, intelligent monitoring of environmental and hazardous gases based on various sensing devices, high-voltage near-electric detection based on digital filtering, and the development of a platform for three-dimensional protection safety warning monitoring of power construction These studies aim to improve the level of intelligent safety management on power construction. Lastly, this paper discusses further research directions including multi-sensor fusion, data analysis and processing, real-time warning and decision support, and virtual reality technology to achieve comprehensive monitoring and intelligent management of power construction, thereby improving safety and efficiency.
  Keywords: power construction operation; three-dimensional perception and protection; image recognition; near-electric detection; sensors; platform.
  DOI: 10.1504/IJWMC.2024.10063331
   
  
• Deep learning-based wall crack detection  
  by Zujia Zheng, Kui Yang 
  Abstract: Aiming at the problems of large weight and high complexity of YOLOv4 network, this paper proposed an improved wall crack detection method based on YOLOv4 network. Firstly, the backbone feature extraction network of YOLOv4 was replaced by Mobile Netv2, and then the common convolution in deep network was replaced by deep separable product, so as to make the whole network model lightweight. Then, SENet attention mechanism is incorporated to make up for the loss of detection accuracy caused by network lightweight. Finally, the data set was constructed and annotated by collecting crack images and data amplification. The experimental results show THAT this method can greatly reduce the network weight, the number of parameters and the computation, and shorten the detection time while maintaining a high detection level, which can meet the needs of various wall crack detection work.
  Keywords: YOLOv4; wall crack detection; target detection.
  DOI: 10.1504/IJWMC.2024.10063608
   
  
• Performance evaluation of centralised and distributed controllers in software defined networks  
  by Houda Hassen, Soumaya Meherzi 
  Abstract: Software-Defined Networking (SDN) is a new network paradigm that has been proposed to overcome the limitations of traditional networks in terms of complexity and management. According to SDN architecture, the control plane and the data plane are decoupled, and all the network’s intelligence is centralized in the controller. A myriad of open-source controllers have been proposed for both research and industrial use. Such controllers, which are designed with different characteristics, have been shown to behave differently with regard to some network QoS parameters. Due to the crucial role of the controller in SDN networks, we intend to provide in this paper a thorough performance analysis of the most commonly used controllers. As centralized controllers, we have considered POX and RYU, whereas OpenDayLight (ODL) and ONOS have been chosen as examples of distributed controllers. For each kind of controller, the performance behavior is analyzed under different topology types and various network scales using a wide range of QoS metrics.
  Keywords: SDN controller; POX; RYU; OpenDayLight; ONOS; performance parameters.
  DOI: 10.1504/IJWMC.2023.10063687
   
  
• DBSCAN behaviour analysis and N-Adaboost prediction model research for mathematics majors academic prediction  
  by Xiaoni Zhang 
  Abstract: From the perspective of distance optimisation, a density-based spatial clustering applied noise algorithm is proposed for clustering analysis and academic prediction of mathematical students' behaviour. This algorithm improves the clustering effect and accuracy by improving the selection of neighborhood radius. Secondly, to address the limited learning performance of a single classifier, an N-Adaboost model based on multiple classifiers is proposed. The experiment shows that when the number of clusters is 4, the network behavior description index reaches the optimal level, with a maximum contour coefficient of 0.667. The N-Adaboost prediction model has high accuracy, accuracy, and recall rate. When N = 3, the model has the best performance and can successfully predict and analyse data. In summary, the density based noisy clustering algorithm based on distance optimisation and the N-Adaboost prediction model based on multiple classifiers have broad application prospects in student behaviour clustering analysis and academic prediction problems.
  Keywords: mathematics major; academic prediction; DBSCAN; N-Adaboost; distance optimisation.
  DOI: 10.1504/IJWMC.2024.10063765
   
  
• SSD object detection algorithm based on knowledge map  
  by L. Huang, Xiaofeng Wang, Jianhua Lu, Wei Hu, Changrong Zhang 
  Abstract: With the application of artificial intelligence to all aspects of people's lives, talent is the first resource. Through the analysis of the current situation of talent training in colleges and universities, it is found that the major has problems such as scattered knowledge points, overlapping content, single practice path, and lack of effective evaluation. In view of the above problems, this paper proposes a multidisciplinary and comprehensive practical teaching mode based on knowledge graph by conducting research on the indoor object detection algorithm of service robot by SSD algorithm, and discusses multi-path practical teaching and evaluation methods from the aspects of teaching objectives, problem decomposition, resource integration, implementation methods and grade evaluation. Promote the all-round development of students by learning the knowledge of object detection.
  Keywords: knowledge map; integrated practice; SSD object detection algorithm.
  DOI: 10.1504/IJWMC.2024.10063828
   
  
• Low-complexity detector performance evaluation for cell-free massive MIMO systems  
  by Mitesh Solanki, Shilpi Gupta 
  Abstract: In 5G, massive MIMO combines beamforming and spatial multiplexing of users to enhance spectral efficiency and energy efficiency It employs a centralized solution to densify a network, resulting in intercellular interference because of its cell-centric design Massive MIMO systems enable user-centric transmission in cellular networks to overcome inter-cell interference limitations and provide macro-diversity Massive MIMO distinguishes itself from coordinated, distributed wireless systems with its scalability and cell-free design This article investigates the immense potential of our proposed detector, this promising technology, and practical deployment issues due to signal coprocessing's increased backhauling overhead We propose a conjugate gradient (CG)-based detector that enables faster convergence and smaller approximated errors for cell-free massive MIMO systems An analytical approach describes the asymptotic performance of a conjugate gradient-based likelihood ascent search detector with massive antennas Additionally, the joint channel estimation and data detection (JCD) method is used in real-world scenarios with imperfect channel state information
  Keywords: massive MIMO; conjugate gradients; cell-free massive MIMO; joint channel estimation and data detection; imperfect channel state information.
  DOI: 10.1504/IJWMC.2024.10063829
   
  
• Recommendation system based on improved graph neural networks  
  by Jiawen Chen, Chao Cai, Yong Cai, Fangbin Yan, Jiayi Li 
  Abstract: Recommendation systems are required in areas such as power distribution, product sales, and tourist attraction recommendations. The mainstream algorithms of graph convolutional networks in recommendation systems, NGCF and LGCN, only use the historical interaction information between users and products, and it is difficult to introduce the side information of users and products, which leads to some limitations in the effectiveness of recommendations. In this paper, we propose the Wide Graph Convolutional Neural Network Recommendation System (WideGCN), which considers the side information feature extraction of products and users, and constructs two fully connected neural networks for user features and products features respectively, which are used to learn user and products features, and aggregates them with the representation information obtained from LightGCN learning to generate the final user and products representations. In this paper, Popularity, BPR, NGCF and LGCN recommendation system algorithms are selected as baseline (Baseline) models and were made a comparison with WideGCN model. The results show that the WideGCN model consistently outperforms the latest and most widely used system algorithms in several recommendation system evaluation metrics.
  Keywords: graph neural networks; wide graph neural networks; recommendation system; user and products features.
  DOI: 10.1504/IJWMC.2024.10064332
   
  
• Application analysis of multi-target tracking algorithm based on BSIC-re ID and spatio-temporal constraints in basketball games  
  by Mingzhi Ye 
  Abstract: With the development of multi-target tracking technology, it has become an important part of ensuring the fairness of basketball games. The traditional multi-target tracking technology has disadvantages such as inaccurate data acquisition and low efficiency. In order to solve these problems, this study effectively combines human key point detection with spatio-temporal constraint recognition technology to design a multi-target tracking modeling algorithm suitable for basketball games. The results show that in the training set, the recognition rate of the modeling method for the basketball player's trajectory is 85.6%; meanwhile, the PR curve area of the modeling method is 0.86, and the selection accuracy is 93.61%, which are better than the comparison method. This indicates that the modeling algorithm is more suitable for multi-target tracking than the existing methods. It aims to provide a new research direction for basketball multi-target tracking technology.
  Keywords: basketball match; multi-target tracking; athletes; spatio-temporal constraints; camera.
  DOI: 10.1504/IJWMC.2024.10064365
   
  
• Construction of evaluation index system of architecture education based on vector analysis  
  by Kaifeng Chu, Hui Tan, Siyao Zhang 
  Abstract: In order to further improve the quality of architecture education, an architectural teaching evaluation model is constructed based on vector analysis and dynamic memory network. Among them, emotional analysis is carried out using students' comments on the teaching process as evaluation data, and the analysis results are used as the basis for teaching evaluation. The experimental results show that the designed aspect-level sentiment analysis model integrating prior knowledge (API-DMN), has a high accuracy in the sentiment classification and sentiment analysis, and its accuracy is stable at more than 80%. Therefore, the designed model can be applied to the evaluation of architecture education and teaching, and the credibility of the evaluation results is high.
  Keywords: architecture; educational evaluation; vector analysis; dynamic memory network.
  DOI: 10.1504/IJWMC.2024.10064369
   
  
• Research on reflective intensity-modulated fibre optical pressure sensor based on desensitisation of temperature compensation method  
  by Baili Zhang, Zhiguo Gao, Hongying Guo, Wei Qin 
  Abstract: Optical fibre sensors used in pressure detection have many incomparable advantages compared with conventional detection technology, such as strong environmental adaptability, high sensitivity, small body, light weight, flexibility, wide range of measurement objects, non-contact measurement and high comprehensive cost performance. This paper studies the RIM-FOPS (reflective intensity-modulated fibre-optic pressure sensor) technology based on temperature compensation method desensitisation. A new type of FBG (fibre Bragg grating) pressure sensor with temperature compensation based on a flat sheet structure is discussed. As a pressure sensitive thin plate of elastomer, its hard core pulls the pressure sensitive FBG. Through a rigid displacement transmission mechanism. Temperature desensitisation is solved by passive temperature compensation and real-time correction of residual temperature effect. Theoretical analysis and experiments show that the sensor system can effectively eliminate the influence of factors such as fluctuation of luminous power of light source, change of optical fibre loss and interference of ambient light.
  Keywords: temperature compensation; pressure sensor; fibre Bragg grating.
  DOI: 10.1504/IJWMC.2024.10064512
   
  
• Research on takeaway distribution path optimisation based on genetic algorithm combined with particle swarm optimised simulated annealing  
  by Chuanxu Cheng 
  Abstract: To help delivery personnel plan delivery paths more reasonably and improve the efficiency of order delivery, this paper constructs a takeaway distribution path planning model based on the real-time location of delivery personnel, delivery and pick-up order, order delivery time, and vehicle capacity. In addition, IGAPSO algorithm is introduced to solve the optimal path. The results reveal that when carrying out path planning, the delivery time required by the proposed algorithm is reduced by 95.3s, 35.16s and 10.55s compared with CA, SA and GWOA. In practical application, compared with the original path, the distribution time consumption of the optimized path is reduced by 15.57%, and the distribution efficiency is higher.
  Keywords: path optimisation; genetic algorithm; simulated annealing algorithm; big data; transportation network.
  DOI: 10.1504/IJWMC.2024.10064553
   
  
• Research on the feasibility analysis of old renovation project model construction of sponge city integrating BP neural network and MIKE model  
  by Mingqin Fu, Liqiang Xu, Chengcheng Wang 
  Abstract: In order to further improve the flood control level of the city in urban construction, the suitability of sponge city construction in the old renovation project is analyzed based on BP neural network and MIKE model. Among them, BP neural network evaluation model is constructed to evaluate the suitability of old renovation project of sponge city, and MIKE model is introduced to simulate rainfall and regional water accumulation. The experimental results show that the designed MIKE Flood water dynamic simulation model can simulate rainfall and water accumulation accurately. The designed BP neural network evaluation model is used to evaluate the suitability of regional sponge city construction with high accuracy, which can provide reliable data for the subsequent urban construction and transformation, and has high feasibility.
  Keywords: urban waterlogging; sponge city; BP neural network; MIKE Flood model.
  DOI: 10.1504/IJWMC.2024.10064599
   
  
• Emotion recognition method for multimedia teaching classroom based on convolutional neural network  
  by Xiaojuan Tang, Meilin Jin 
  Abstract: In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. Among them, VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.
  Keywords: multimedia teaching; emotion recognition; model fusion; convolutional neural network.
  DOI: 10.1504/IJWMC.2024.10064640
   
  
• Microstrip MIMO antenna design with self-improved optimisation strategy  
  by Shaktimayee Mishra, Asit Kumar Panda, Agarwal Arun 
  Abstract: To increase the channel's capacity and communication dependability, Multiple Input Multiple Output (MIMO) antennas are widely used on both the receiver and transmitter sides of interaction networks. One strategy is to place radiating parts far away, which will result in enormous antennas. These systems' channel capacity is influenced by the transmission bandwidth, S/N ratio, and the characteristics of the antennas being utilized, among other factors. In this work, MIMO Micro strip antenna design with optimization tactic is modeled. Here, Cartesian Distance Estimated Mayfly Optimization (CDE-MFO) is implemented to resolve the optimization issues by considering the constraints like Area (length, width), gain, beam width, antenna efficiency, envelope correlation coefficient (ECC), total active reflection coefficient (TARC) and directivity. Our findings demonstrate that channel capacity and performance for a wireless MIMO channel can be enhanced by increasing the number of sending and receiving antennas
  Keywords: MIMO; microstrip antenna; directivity; antenna efficiency; CDE-MFO model.
  DOI: 10.1504/IJWMC.2023.10064875
   
  
• 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
   
  
• Research and optimisation of low-cost cloud log storage system based on data pattern awareness  
  by Qixia Wang, Ning Lv, Junjun Du 
  Abstract: Logging services in public clouds have significantly enhanced the capabilities of research, operations, management, and security assurance. However, cloud logs are characterized by large data size, long retention time, fast writing, low useful information density, and high access latency. In order to reduce storage costs, the following requirements need to be met: 1) highly compressed save data to save space; 2) realise high-speed data writing and fast compression; 3) realise low-latency fast retrieval of compressed data. In this study, we summarize the typical data patterns in cloud logs and propose a low-cost cloud log storage system based on data pattern awareness to meet the goals of high compression rate, fast compression, and low latency retrieval. We comparatively test various low-cost cloud log storage systems in terms of compression rate, compression speed, and retrieval latency, and ultimately present experiences and outlooks for future research in related areas.
  Keywords: cloud log; data pattern; log storage; high-compression ratio; low-query latency.
  DOI: 10.1504/IJWMC.2024.10064929
   
  
• Enhancing energy efficiency and QoS in 5G networks with dynamic resource optimisation green communication protocol  
  by Dhanashree Shukla, S.D. Sawarkar 
  Abstract: The rapid expansion of 5G networks and increasing user equipment (UEs) necessitate innovative approaches for improved energy efficiency. UE-to-UE communication is a promising solution, leveraging nearby UEs for efficient data transfer and reducing reliance on macro cells. We present the dynamic resource optimisation green communication protocol (DRO-GCP) for 5G, using fuzzy logic (FL) to optimise energy-efficient UE-to-UE routing. DRO-GCP includes phases of network deployment, route optimisation, and D2D transmission. It calculates parameters such as UE speed, energy consumption, bandwidth occupancy, and connection quality using type-2 FL to identify over-utilised UEs. During transmission, the transmitting UE requests relay UEs, evaluates their responses, and selects optimal relays, ensuring reliable data transfer while minimising network overhead and CO2 emissions. Simulations show DRO-GCP improves energy efficiency by 2.310.6% over existing methods, enhancing throughput, delay, energy consumption, and packet data ratio. This protocol offers a promising solution for energy-efficient 5G D2D communication.
  Keywords: fuzzy logic ; green communication; HetNets; UE-to-UE communication.
  DOI: 10.1504/IJWMC.2024.10064966
   
  
• An evaluation model for college students mental health based on machine learning algorithm  
  by Qiaoying Ming 
  Abstract: Owing to traditional beliefs, people tend to be hesitant and reserved in expressing themselves. To achieve accurate assessment of college students' mental health problems, a CNN-BiLSTM mental health assessment algorithm based on metaphorical attention mechanism was proposed. CNN-BiLSTM text processing module and metaphorical attention mechanism are used to improve the evaluation effect. The results show that compared with Text-CNN, BiLSTM+multi-layer RNN and BiLSTM+Attention, the recall rate and F1 value of the proposed algorithm are increased by 6.52% and 4.04%, respectively, and the prediction effect is best. After the elimination of RNN_MIP, metaphorical attention mechanism and BiLSTM, F1 value decreases by 2.33%, 8.72% and 5.7%, respectively, and the decrease is obvious.
  Keywords: machine learning; mental health; social platform; evaluation and prediction; metaphorical feature.
  DOI: 10.1504/IJWMC.2024.10065027
   
  
• Application of integrated image processing technology based on PCNN in online music symbol recognition training  
  by Ting Zhang 
  Abstract: To improve the effectiveness of online training for music education, it was investigated how to improve the pulse-coupled neural network in image processing for spectral image segmentation. The study proposes a two-scale descent method to achieve oblique spectral correction. Subsequently, a convolutional neural network was optimized using a two-channel feature fusion recognition network for music theory notation recognition. The results showed that this image segmentation method had the highest accuracy, close to 98%, and the accuracy of spectral tilt correction was also as high as 98.4%, which provided good image preprocessing results. When combined with the improved convolutional neural network, the average accuracy of music theory symbol recognition was about 97%, and the highest score of music majors was improved by 16 points. This shows that the method can effectively improve the teaching effect of online training in music education and has certain practical value.
  Keywords: image processing; simplified symbolic music theory; symbol recognition; online training; skew correction; CNN.
  DOI: 10.1504/IJWMC.2024.10065057
   
  
• Design of traffic signal automatic control system based on deep reinforcement learning  
  by Haoyu Wang 
  Abstract: Aiming at the problem of aggravation of traffic congestion caused by unstable signal control of traffic signal control system, the multi-agent deep deterministic policy gradient-based traffic cyclic signal (MADDPG-TCS) control algorithm is used to control the time and data dimensions of the signal control scheme. The results show that the maximum vehicle delay time and vehicle queue length of the proposed algorithm are 11.33s and 27.18m, which are lower than those of the traditional control methods. Therefore, this method can effectively reduce the delay of traffic signal control and improve the stability of signal control.
  Keywords: traffic signal; automatic control; deep reinforcement learning; MADDPG-TCS; multi-agent.
  DOI: 10.1504/IJWMC.2024.10065072
   
  
• Enhancing artificial bee colony algorithm with depth-first search and direction information  
  by Xinyu Zhou, Hao Tang, Shuixiu Wu, Mingwen Wang 
  Abstract: In recent years, Artificial Bee Colony (ABC) algorithm has been criticised for its solution search equation, which makes the search capability bias to exploration at the expense of sacrificing exploitation. To solve the defect, many improved ABC variants have been proposed aiming to utilise the elite individuals. Although these related works have been shown to be effective, they rarely take the factor of search direction into account. In fact, the search direction has an important role in determining the performance of ABC. Thus, in this work, we are motivated to investigate how to combine the idea of utilising the elite individuals with the search direction, and a new ABC variant, called DDABC, is designed. In the DDABC, the Depth-First Search (DFS) mechanism and Direction Information Learning (DIL) mechanism are introduced, and the former mechanism is to allocate more computation resources to the elite individuals, while the latter mechanism aims to adapt the search to the promising directions. To verify the effectiveness of the DDABC, experiments are carried out on 22 classic test functions and three relative ABC variants are included as the competitors. The comparison results show the competitive performance of our approach.
  Keywords: artificial bee colony; exploration and exploitation; depth-first search; direction information learning.
  DOI: 10.1504/IJWMC.2024.10064643
   
  
• A study on dual-sense broadband circularly polarised monopole antenna for UWB applications  
  by Umesh Singh, Kalyan Mondal, Rajesh Mishra 
  Abstract: The proposed work is designed with the embedded of stubs and Parasitic Strips (PSs) under the radiator. An FR4 substrate is used to design the antenna (ε_r = 4.4, h = 1.6 mm). The overall size of the antenna is 0.8 λ₀ × 0.75 λ₀ × 0.04 λ₀, where λ₀ is the free space wavelength at f₀ = 7.5 GHz. Two orthogonal modes are generated with the same magnitude and 90° phase difference over the frequency band. The stubs and strips are used to maintain the E_X and E_Y ratio for better Axial Ratio Bandwidth (ARBW). The measured impedance bandwidth of 7.74 GHz (3.9-11.64 GHz), 99.6%, two ARBW of 4.80 GHz (5.12-9.92 GHz), 63.83% and 0.75 GHz (11.1-11.85 GHz), 6.53% are achieved. The proposed antenna is preferred for the applications of 5.8 GHz ISM band, WiMAX (5.7-5.85 GHz), WLAN (5.2-5.8 GHz), Satellite (7.25-7.75 GHz, 7.9-8.4 GHz) and X-band (8-12 GHz).
  Keywords: monopole; circular polarisation; dual-sense; stubs; ARBW; satellite.
  DOI: 10.1504/IJWMC.2024.10064644
   
  
• RPL-OFs analysis and dynamic of selection for QoS optimisation of RPL protocol  
  by Sharwari S. Solapure, Harish H. Kenchannavar, Umakant P. Kulkarni 
  Abstract: Quality-of-Service (QoS) requirements differ for various IoT applications such as for smart health, reliability is the need, for industry, delay is essential, etc. The Routing-Protocol-for-Low-Power-Lossy-Network (RPL) with Objective Function (OF) is used for routing in IoT-applications. Default RPL-OF is deficient to fulfil the QoS requirements of different IoT applications. Hence, several OF designs were proposed as per the QoS need in the earlier research. The work presented in this paper is the extension of previous research work. The analysis of these OF designs is carried out with the parameters such as number of nodes, simulation-time, data-rate, Media Access Control (MAC) protocols, communications ranges and different topologies. This analysis resulted in a data-set which addresses most of the QoS-requirements and is used to optimise the RPL protocol QoS performance. Decision tree algorithm is used to predict a suitable RPL-OF design. The accuracy achieved using Gini and Entropy method of decision tree is 87.14%, 88.57%, respectively. Thus, the contribution of this research is to prepare the data-set using comprehensive analysis and use the same for predicting suitable RPL-OF design according to QoS-requirements of IoT applications. The proposed methodology is useful in IoT-applications where dynamic-OF selection as per QoS-requirements is needed.
  Keywords: IoT; RPL; OF; LLN; QoS; quality-of-service.
  DOI: 10.1504/IJWMC.2023.10064454
   
  
• A sound and complete axiomatic system for compound accessible relations  
  by Sujie Guan 
  Abstract: Approximation spaces are deemed as frames for the modal logic. Given two equivalence relations R₁ and R₂. R₁ and R₂ can be mapped to the modalities ͸₁ and ͸₂, respectively. For any formula A, ͸A ≡ ͸₁A ˅ ͸₂A or ͸A ≡ ͸₁A ˄ ͸₂A, where ͸ corresponds to an accessible relation R. Obviously, R may not be an equivalence relation and it is difficult to give an axiomatic system for ͸. So a sound and complete axiomatic system is given for the propositional modal logic, where the accessibility relation R for modality ͸ is the join of the accessibility relations R₁, R₂. Obviously, this paper presents a generalised modal logic in which the modality ͸ corresponds to two accessible relations, and has stronger knowledge representation ability and stronger formal ability.
  Keywords: accessibility relation; axiomatic system; soundness and completeness.
  DOI: 10.1504/IJWMC.2023.10064121
   
  
• Research on facial expression recognition based on multimodal data fusion and neural network  
  by Yi Han, Xubin Wang, Zhengyu Lu 
  Abstract: Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed.
  Keywords: multimodal data; deep learning; neural network; facial expression recognition; data fusion.
  DOI: 10.1504/IJWMC.2024.10064645
   
  
• A hybrid malicious node detection approach based on fuzzy trust model and Bayesian belief in wireless sensor networks  
  by Wuchao Shi 
  Abstract: With the wide range of Wireless Sensor Network (WSN) applications including environmental monitoring and healthcare, the sensor nodes in WSN are susceptible to security threats including dishonest recommendation attacks from malicious nodes, which could disrupt communication's integrity. Thus, malicious node detection in WSN is essential. In recent years, several malicious node detection approaches based on trust management were proposed to protect the WSN against dishonest recommendation attacks. However, the existing approaches ignore data consistency and re-evaluation of participating nodes in trust evaluation, which seriously undermine their effectiveness. To address these limitations, we propose a hybrid malicious node detection technique for WSN based on the Fuzzy Trust Model (FTM) algorithm and the Bayesian Belief Estimation (BBE) approach. The key idea in the proposed approach is to determine direct trust values through the FTM algorithm using the correlation of data collected over time, and to ascertain the trustworthiness of indirect trust values from recommendation nodes via the BBE approach. The results of simulations conducted to evaluate the effectiveness of our approach show that our model can effectively detect malicious nodes in WSN better than the previous approaches.
  Keywords: WSN; wireless sensor network; dishonest recommendation attacks; fuzzy trust model; Bayesian belief.
  DOI: 10.1504/IJWMC.2024.10064646
   
  
• Task scheduling in multi-cloud environment via improved optimisation theory  
  by Prashant Balkrishna Jawade, S. Ramachandram 
  Abstract: As one of the most popular technologies nowadays, cloud computing has a big demand in the distributed software space. It is highly difficult for CSPs to work together in a multi-cloud context, and contemporary literature does not adequately address this issue. In this work, a protected TS paradigm in a multi-cloud environment is introduced. The suggested scheme mainly focuses on the optimal scheduling of tasks by considering a modified Deep Neural Network (DNN) as a task scheduler. Accordingly, the task is allotted based upon 'makespan, execution time, security constraints (risk assessment), utilisation cost, maximal Service Level Agreement (SLA) adherence and Power Usage Effectiveness (PUE)'. Moreover, the weights of DNN are tuned optimally by Self-Improved Aquila Optimisation (SI-AO) technique. The developed model has obtained a lower MAE value = 0.052581 which is 46.67%, 90.85%, 89.29% and 86.43% better than DNN, NN, RNN and LSTM, respectively.
  Keywords: task scheduling; execution time; modified DNN; risk assessment; SI-AO model.
  DOI: 10.1504/IJWMC.2024.10064647
   
  
• Feedback-based trust module for IoT networks using machine learning  
  by Sania Iqbal, Shaima Qureshi 
  Abstract: The increasing emergence of IoT in healthcare, industrial automation, manufacturing, infrastructure, business and the home undoubtedly provides more conveniences in different aspects of human life. Any IoT security and trust solution must consider the heterogeneity and mobility of IoT devices, and the features of other devices connected to the network. Traditional security measures face computation, storage and communication challenges in securing IoT, and the increasing reliance on IoT services and applications has exacerbated this issue. It is crucial to identify a security management model to handle these constraints. Trust as a security regime in the IoT has far-reaching implications. This paper proposes a novel trust-based security mechanism by combining machine learning and traditional summations to determine a node's trustworthiness in an IoT environment in milliseconds. Based on this, a computational trust management model is designed by aggregating direct and feedback-based trust information. The model parameters like the trust breach window and trust re-initiation point are selected based on extensive experimentation to dynamically calculate each node's trust value to reach the proposed model's peak performance. The model's effectiveness is verified through simulation. In 500 iterations, the proposed model achieved 97% accuracy with a run time of 0.37 seconds.
  Keywords: internet of things; machine learning; decision trees; trust management; network security.
  DOI: 10.1504/IJWMC.2023.10064368
   
  
• A method of selecting characteristics based on P_KPCA for new power system operation mode  
  by Xiaoli Guo, Qingyu Shan, Zhenming Zhang, Hao Jiang 
  Abstract: With the increasing role of renewable energy in the power system, the characteristic variables of the new power system operation mode are made to develop towards high dimensionality and diversification. As a result, the applicability and accuracy of the traditional feature variable screening methods are insufficient. For this reason, a method of selecting characteristics based on P_KPCA for a new power system operation mode is proposed. Firstly, to quantify the correlation between the various operating variables, a correlation quantification method based on the Pearson coefficient of the characteristic variables of the operating mode is designed. Then, the feature vector dimension is reduced to reduce the impact on the extraction accuracy of the operating mode, a screening model for strongly correlated characteristic variables of the operation mode based on P_KPCA is constructed. Finally, based on more than 8000 power grid operation sections, experimental verification is carried out to verify the accuracy and rationality of the method in this paper.
  Keywords: power operation data; new power system; feature dimension reduction; kernel principal component analysis; dimension reduction.
  DOI: 10.1504/IJWMC.2023.10063373