Forthcoming and Online First Articles

International Journal of Wireless and Mobile Computing

International Journal of Wireless and Mobile Computing (IJWMC)

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International Journal of Wireless and Mobile Computing (46 papers in press)

Regular Issues

  • Multi-objective workflow scheduling in the cloud environment based on NSGA-II   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • A feature fusion pedestrian detection algorithm   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • Research on optimisation of fresh product closed-loop supply chain based on DWOA algorithm in mobile environment   Order a copy of this article
    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
     
  • Enhancing energy efficiency and QoS in 5G networks with dynamic resource optimisation green communication protocol   Order a copy of this article
    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   Order a copy of this article
    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
     
  • Multiscale modelling and control of inductively coupled plasma etching based on recurrent neural networks   Order a copy of this article
    by Lihong Zhu, Qingchuan Chen, Junwei Nie, Kewei Liu 
    Abstract: In order to deepen the understanding of the process flow and improve the process, the plasma etching simulation is an essential means, but the calculation cost of the traditional simulation system is too high. Therefore, a study proposes a multi-scale model optimisation algorithm and model prediction and control system based on recurrent neural network. The multi-scale model optimisation algorithm adopts a recursive neural network model to achieve simulation acceleration of the multi-scale model. While the model prediction science system uses model reduction and recurrent neural network models to achieve full state prediction of macroscopic plasma models. The experimental results indicate that the simulation model can achieve the expected simulation results within 7.3 seconds. Moreover, the average etching depth of the model predictive control system shall not exceed 4.6%. Consequently, the proposed simulation model optimisation algorithm and model prediction and control system can effectively optimise and control the plasma etching.
    Keywords: multi-scale model; plasma etching; recurrent neural network; model reduction.
    DOI: 10.1504/IJWMC.2024.10065185
     
  • Data mining and learning behaviour analysis of French online education data-driven teaching based on generative adversarial network improvement Apriori algorithm   Order a copy of this article
    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, personalized feedback, helping optimize learning behavior and significantly boosting learning outcomes. Analysis of behaviors 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
     
  • Improved multilevel scaled cognitive diagnostic model as an aid in mental health education   Order a copy of this article
    by Bo Wang, Qian Ren 
    Abstract: In mental health education, there are differences in the level of knowledge and skill acquisition among students that cannot be assessed by a simple two-level scale. To this end, this study propose an improved multi-level scoring cognitive diagnosis model based on deterministic input noise and gate model, and apply it to the personalized exercise recommendation of mental health education. In order to improve the evaluation of students' skill mastery level, the practice skill matrix is improved. In addition, considering the different requirements of skill levels for different problems, weight functions are introduced to ensure more accurate measurement feedback. The experimental results indicated that the proposed model is more reliable, with recommendation accuracy rates of 52.43%, 54.45%, and 53.78%, respectively. This was significantly higher than other models and had a significant positive effect on student achievement. The model provided a new cognitive diagnostic tool for mental health education.
    Keywords: cognitive diagnostic model; mental health education; exercise recommendation; performance prediction.
    DOI: 10.1504/IJWMC.2024.10065305
     
  • Energy harvesting-based trust-aware medical data transmission model with multi-objective function in cognitive radio network   Order a copy of this article
    by Bancha Naresh Kumar, Jai Sukh Paul Singh 
    Abstract: Background of the study: Cognitive Radio Network (CRN) is a fundamental and emerging technology for sharing the spectrum over the Secondary Users (SU) that cannot be used by the Primary Users (PU). The CRN considers the reactive security policy for improving energy monitoring when using the primary channels of the CRN. Data communication becomes a challenging process when utilizing the moderate energy level for transmission in security monitoring of CRN because the intruders are mostly aimed to make network attacks to minimize the PU or SU's energy level. Aim: In this research work, the security-aware data transmission model in CRN with energy harvesting is developed especially for supporting Medical Data Transmission (MDT). Methods: A novel Self-Adaptive Ladybug Beetle Optimization Algorithm (SA-LBOA) is developed to identify the optimal routing path between the nodes of source and destination with the optimal energy harvesting model, which helps to expand the lifetime of the network. In the medical domain, the optimal path is selected to provide reliable, energy-efficient data transmission.
    Keywords: cognitive radio network; MDT; energy harvesting; self-adaptive ladybug beetle optimisation algorithm; multi-objective function; trust computation.
    DOI: 10.1504/IJWMC.2024.10065543
     
  • Dynamic resources optimisation and interference management-based green communication protocol (DROIM-GCP) for 5G   Order a copy of this article
    by Dhanashree Shukla, S.D. Sawarkar 
    Abstract: The increasing number of connected devices and Heterogeneous Networks (HetNets) highlight the importance of Green Communication. A promising aspect of 5G HetNets is UE-to-UE communication, focusing on energy-efficient routing, handover, and optimal User Equipment (UE) selection. We introduce the Dynamic Resources Optimization and Interference Management-based Green Communication Protocol (DROIM-GCP) using type-2 fuzzy logic. DROIM-GCP operates in two phases. In the resource optimization phase, each UE periodically measures variables like Bandwidth Utilization, Link Reliability, Energy Utilization, and Velocity Normalization, using fuzzy logic to select the best UE for transmission. In the interference management phase, UEs for handover are identified based on RSSI, Velocity Normalization, Energy Utilization, and Link Reliability, with target femtocells chosen based on Bandwidth Utilization and Channel Occupancy. This method aims to maximize throughput and data rate while minimizing energy consumption and delay. Simulations show DROIM-GCP outperforms existing methods, improving energy consumption by 6.25% to 12.79%.
    Keywords: green communication; heterogeneous network; handover; UE-to-UE communication; resource optimisation; type-2 fuzzy logic.
    DOI: 10.1504/IJWMC.2024.10065549
     
  • Optimisation of transportation vehicle scheduling based on FG-ABC algorithm in 5G scenarios   Order a copy of this article
    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
     
  • Hybrid ECD model for firewall tuning and attack detection   Order a copy of this article
    by C. Thyagarajan, S. Vijay Bhanu, S. Suthir 
    Abstract: The Internet of Things (IoT) increases efficiency due to the vast number of sensors and processors, which preserve the data and dynamically adjust the operation of each component. The firewall acts as one of the most efficient techniques for enhancing computer network security. Furthermore, software-defined networking (SDN) has greatly improved the network management. In this research, a hybrid Deep Learning (DL)-based firewall is designed. The request log is sent to the primary firewall, which tracks the network traffic and restricts the vulnerabilities and undesirable traffic. The EfficientNet-B3-Attn-2 fused Cascade Neuro-Fuzzy Network (ECD) is developed for network security whenever the primary firewall fails to regulate the network traffic. Furthermore, the devised framework is evaluated in terms of accuracy, sensitivity, and specificity metrics that yield values like 0.885, 0.946, and 0.915.
    Keywords: deep learning; software-defined networking; internet of things; neuro-fuzzy network; firewall.
    DOI: 10.1504/IJWMC.2024.10066225
     
  • Construction of target detection teaching platform based on improved YOLOv5s algorithm   Order a copy of this article
    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, utilizing 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
     
  • AGOA: Adam gazelle optimisation algorithm for collaborative e-learning application in cloud computing with load balancing   Order a copy of this article
    by N. Venkatesh Naik, K. Madhavi 
    Abstract: A novel strategy for cloud employing collaborative E-learning application with course recommendation named Adam Gazelle Optimization Algorithm (AGOA) is designed. Here, an effective structure of cloud is analyzed for collaborative E-learning application hosting. Afterwards, the course recommendation approach is designed for E-learning application. According to user access, a VM migration algorithm is established with effectual load balancing in public subnet of cloud, wherein the following steps are utilized in E-learning application. Here, tasks are allocated to VM in round robin manner and predict load of PM, which is carried out by Deep Recurrent Neural Network (DRNN). When predicted load is greater than threshold, VM migration is conducted by migrating VM in overloaded PM to underloaded PM by regarding objectives namely, CPU utilization, energy consumption, makespan, and migration cost, employing proposed AGOA. The performance measure employed for AGOA attained 0.139, 0.158 J, and 10.1.
    Keywords: collaborative e-learning; cloud computing; load balancing; Adam optimisation; Adam; GOA; gazelle optimisation algorithm.
    DOI: 10.1504/IJWMC.2024.10066452
     
  • Application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network   Order a copy of this article
    by Promod Kumar Sahu, Hemanta Kumar Pati, Sateesh Kumar Pradhan 
    Abstract: In this paper, we propose a mathematical model that estimates optimal number of guard channel (GC)s for handoff call (HC)s by considering target handoff call dropping probability (HCDP) and keeping new call blocking probability (NCBP) minimum.The mathematical model proposed assume static traffic parameters. However, in real the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance further 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 that 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
     
  • Design and analysis of a coupled-feed, reconfigurable antenna for wireless application   Order a copy of this article
    by Santimoy Mandal, Saindhab Chattaraj, Biswajit Mondal, Chandan Kumar Ghosh 
    Abstract: In this research, a planer 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 LTE (Long term evoluation) band 46, 47 is proposed The proposed antenna can be suitable used for U-NII (licensed assisted access) and U-NII-4 (cellular vehicle to everything) with TDD (Time division Duplexing) mode 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
    Keywords: wireless wide area network; long term evolution; time division duplexing; VSWR; coupled feed; shorting strip.
    DOI: 10.1504/IJWMC.2024.10066947