Forthcoming and Online First Articles

International Journal of Vehicle Information and Communication Systems

International Journal of Vehicle Information and Communication Systems (IJVICS)

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International Journal of Vehicle Information and Communication Systems (12 papers in press)

Regular Issues

  • Efficient clustering for wireless sensor networks using modified bacterial foraging algorithm   Order a copy of this article
    by Dharmraj Biradar, Dharmpal D. Doye, Kulbhushan A. Choure 
    Abstract: The energy efficiency and clustering are directly related to each other in Wireless Sensor Networks (WSNs). A significant number of methods have been introduced for energy-efficient clustering in the last couple of decades. To limit energy use and improve network throughput, various methods for the clustering algorithm were introduced using an optimisation algorithm, fuzzy logic, and thresholding techniques. The optimisation algorithms such as Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Ant Colony Optimisation (ACO) and their variants were presented, but the challenge of selecting the efficient Cluster Head (CH) and cluster formation around it with minimum overhead and energy consumption is unresolved. In this paper, energy proficient and lightweight clustering algorithm for WSNs is proposed using the Modified Bacterial Foraging optimisation Algorithm (MBFA). The aim of designing the MBFA is to limit energy use, control overhead, and improve network throughput in this paper. The process of CH selection using MBFA is performed via a novel fitness function. The wellness capacity is planned using key parameters, for example, remaining energy, node degree, and geographical distance between sensors to base station. The MBFA selects the sensor node as CH using the fitness value. The proposed clustering protocol is simulated and evaluated with state-of-art protocols to justify efficiency.
    Keywords: bacterial foraging optimisation; clustering; cluster head selection; energy efficiency; particle swarm optimisation.

  • Enhanced video-based traffic management application with virtual multi-loop crate   Order a copy of this article
    by Manipriya Sankaranarayanan, Mala Chelliah, Samson Mathew 
    Abstract: The growth in urban population leads to gridlock of vehicles in city roads. The quality of transportation is improved by the latest technologies of Intelligent Transportation Systems (ITS) applications. Any ITS application relies heavily on sensors for data collection for efficient management, control and planning of transportation. In this paper, the video-based traffic data collection systems and their techniques are improved by using the proposed Virtual Multi-Loop Crate (VMLC) framework. VMLC uses the all the spatial colour information for image processing without losing information. The results of the proposed framework are used to estimate traffic statistics and parameters that are essential for ITS applications. The parameter values obtained from VMLC are analysed for accuracy and efficiency using Congestion Level (CoLe) estimation application. The results show that the VMLC framework improves the quality of data collection for any video-based ITS applications.
    Keywords: vehicle detection; traffic statistics and parameters; spatial colour information; image processing data management; video-based traffic data.

  • Efficient resource allocation scheme using PSO-based scheme of D2D communications for overlay networks   Order a copy of this article
    by Yogesh Kumar Sharma, Bharat Ghanta, Pavan Mishra, Shailesh Tiwari 
    Abstract: Device-to-Device communication (D2D) is an essential technology in cellular networks which enables direct communication between devices and supports the high data rate compared with cellular communication. To improve the system capacity, multiple D2D uses are allowed to share the same resource block. With the limited number of available resource blocks, it is very challenging to assign a resource block for newly formed D2D pairs. Furthermore, to solve the aforementioned problem, an effective resource allocation scheme is proposed that gives the minimum number of required resource blocks for a given link. The proposed scheme is based on particle swarm optimisation (PSO). The proposed scheme reduces the number of resource blocks for a given D2D link and improves the network throughput. Moreover, compared with greedy and LIFA schemes, the proposed scheme could set aside to 26.69% resource blocks around and enhance the throughput per resource block by up to 34.4%.
    Keywords: device-to-device communication; interference; overlay network; PSO; resource block.

  • Electric vehicle battery consumption estimation model based on simulated environments   Order a copy of this article
    by Iñaki Cejudo, Iker Arandia, Itziar Urbieta, Eider Irigoyen, Harbil Arregui, Estíbaliz Loyo 
    Abstract: Governmental policies are promoting using electric vehicles (EVs) to reduce carbon emissions and make transportation more energy efficient. Car manufacturers are putting much effort into making reliable EVs. However, consumers still have to deal with the lack of enough infrastructure and an immature technology readiness level. In order to have an accurate battery range prediction and lessen these issues, this research proposes an energy consumption estimation model based on factors related to battery consumption during a trip. As part of the process, Simulation of Urban Mobility (SUMO), a well-known traffic simulation tool, has been used to run many simulations, produce a heterogeneous dataset, and train the model with a neural network. The results show an accurate battery range forecast, with a coefficient of determination of 0.91. This model can determine trip consumption considering conditions that vehicle manufacturers' reference consumption values do not.
    Keywords: eletric vehicle; energy consumption; simulation; battery estimation; dataset; deep learning.
    DOI: 10.1504/IJVICS.2024.10062692
  • Research on load state prediction model of electric vehicle lithium battery based on Kalman filter algorithm.   Order a copy of this article
    by Xiu Zheng, Zhen Nie, Jie Yang, Qiqi Li, Fenglin Li 
    Abstract: The monitoring and protection of the power core lithium battery of electric vehicles is closely related to whether the lithium battery can output energy efficiently. In order to predict the charge state of lithium battery, an online identification method of model parameters, FRLS, was determined on the basis of selecting the equivalent circuit model of lithium battery, Thevenin. The overall prediction error of SVD-UKF is lower than that of AEKF and IEKF, which are about 20% and 30% respectively. FRLS and SVD-UKF both have low prediction error of lithium battery load state under the same working condition and temperature, and the prediction error of lithium battery load state under the same working condition shows a gradually increasing trend with the increase of temperature. The FRLS&SVD-UKF joint prediction model can accurately predict the load state of lithium battery in electric vehicles in real time, and can improve the recycling performance of lithium battery.
    Keywords: ternary lithium battery; load state; SVD-UKF; electric vehicle.
    DOI: 10.1504/IJVICS.2023.10062693

Special Issue on: AIST2019 Empowering Intelligent Transportation Using Artificial Intelligence Technologies

  • Network traffic analysis using machine learning techniques in IoT network   Order a copy of this article
    by Shailendra Mishra 
    Abstract: End-node internet-of-things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. They have their IP address, and once the hacker traces the IP, it becomes easy to get into the network and exploit the other devices. Cyber threats can become potentially harmful and lead to infection of machines, disruption of network topologies, and denial of services to their legitimate users. Artificial intelligence-driven methods and advanced machine learning-based network investigation protect the network from malicious traffic. The support vector machine learning technique is used to classify normal and abnormal traffic. Network traffic analysis has been done to detect and protect the network from malicious traffic. Static and dynamic analysis of malware has been done. Mininet emulator is selected for network design, VMware fusion is used for creating a virtual environment, the hosting OS is Ubuntu Linux, and the network topology is a tree topology. Wireshark was used to open an existing packet capture file that contains network traffic. Signature-based and heuristic detection techniques were used to analyse the signature of the record, which is found using a hex editor, and proposed rules are applied for searching for and detecting these files that have this signature. The support vector machine classifier demonstrated the best performance with 99% accuracy
    Keywords: network traffic analysis; IoT; cyber threats; cyber attacks; machine learning.
    DOI: 10.1504/IJVICS.2022.10047575
  • A novel framework for efficient information dissemination for V2X   Order a copy of this article
    by Ravi Tomar, Hanumat G. Sastry, Manish Prateek 
    Abstract: This paper is focused on presenting a robust framework for information dissemination in vehicular networks using both Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication modes. The framework is designed to first prioritise the generated information and then, based on the priority, the message is disseminated over the network using one of the techniques for V2V or V2I. The paper first discusses the need for information dissemination and further proposes the novel framework for efficient information dissemination. The framework comprises two techniques for disseminating the information through V2V or V2I. The two techniques are presented and supported by the experimental, simulation and statistical analysis results. The results obtained are compared with existing mechanisms for information dissemination and are found to be performing better than standard information dissemination mechanisms.
    Keywords: information dissemination; V2V; V2I; priority based.

  • Automated storyboard generation with parameters dependencies for regression test cases   Order a copy of this article
    by Nishant Gupta, Vibhash Yadav, Mayank Singh 
    Abstract: In recent trends and advancement of agile technology, the industry demand is for an effective and useful specification from the customer to reduce the effort, time and cost of software development. The storyboard is an effective tool to cater for the customer's requirements in an efficient manner. Our proposed framework and tool STORB will provide the platform where customer and business analyst may use the tool to generate a storyboard based on provided functionalities and parameters. The tool will provide detailed information about the customers requirements and generate the storyboard. Further, test data can also be generated for testing test cases. The tool has been used for three functionalities and their parameters on login functionalities of web application. The tool also defines the dependencies among parameters so that regression test cases can be generated. The result shows a useful significance of the tool in the software industry for the current trend of agile development.
    Keywords: agile testing; regression testing; storyboard;test cases; functionalities.

  • Machine learning techniques applied to call admission control in 5G mobile networks   Order a copy of this article
    by Charu Awasthi, Prashant Kumar Mishra 
    Abstract: Highly reliable applications with low latency are key feature in 5G networks. In the prevailing scenario of efficient mobile network systems, the Quality of Service (QoS) depends on the regulation of traffic volume in wireless communications, known as the Call Admission Control (CAC). 5G networks are also very important for Intelligent Transportation Systems (ITS) as they can be used for quick detection and controlling of traffic, hence can be informative, sustainable, and more effective. Machine learning is the concept of providing the power to learn and develop mechanically, by practising. It also provides the power to attain learning and development in the absence of classical methods such as programming. It also permits wireless networks such as 5G to be increasingly dynamic and predictive. With this feature, the formulation of the 5G vision seems possible. With the use of machine learning and neural networks, this paper proposes various CAC methods deployed for 5G multimedia mobile networks. This can be achieved by delivering the best from all the attributes of soft computing that are deployed in the current mobile networks for ensuring recovery of efficiency of the prevailing CAC methods.
    Keywords: artificial intelligence; machine learning; neural networks; 5G mobile networks; wireless networks; intelligent transportation system.

  • PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm   Order a copy of this article
    by Righa Tandon, P.K. Gupta 
    Abstract: Vehicle-to-Vehicle communication is one of the new paradigms of networking which should be secure, fast and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using matrix array symmetric key (MASK), digital signature algorithm (DSA) and intelligent water drop (IWD). The proposed security scheme also ensures handling of many security attacks such as key-guessing, non-repudiation, replay and modification. In the proposed scheme, to preserve the vehicles identity, we have provided different pseudonyms to each vehicle in the network, which ensures secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of time-delay during vehicle to vehicle communication. In this algorithm, we have used Dijkstras algorithm for finding the optimal shortest path during vehicular communication. Obtained results show that the proposed scheme is effective and efficient as it reduces the time-delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes.
    Keywords: pseudonyms; vehicle-to-vehicle communication; security; time-delay.

Special Issue on: Artificial Intelligence Driven Vehicular Communication Networks in Beyond 5G Era

  • Real-time separation optimisation algorithm for hybrid blind speech signal in vehicular communication networks in beyond the 5G era   Order a copy of this article
    by Guoan Hu 
    Abstract: In this paper, we conduct an in-depth analysis on the real-time separation optimisation algorithm for the hybrid blind signal in wireless communication networks. In the process of the real-time separation of blind signals in network communication, firstly, the hybrid signal in the network communication is whitened to remove the core correlation between the blind signals. In this foundation determined in the network correspondence the mixed signal matrix of linear transformation, it uses transformation matrix to separate in the network correspondence in the blind signal independent signal component, from this the network correspondence area south of core signal is restored. The data analytic framework is suggested to optimise the framework of the proposed tool. Then, the designed model is applied to the vehicular communication networks in beyond the 5G era, and the simulation gives the overall performance estimation to test the robustness.
    Keywords: real-time separation; optimisation algorithm; blind signal; wireless communication; vehicular communication; 5G technology.

  • Hybrid deep learning-based intrusion detection system for wireless sensor networks   Order a copy of this article
    by V. Gowdhaman, R. Dhanapal 
    Abstract: Wireless sensor networks (WSNs) play an important role in the modern era and security has become an important research area. Intrusion detection system (IDS) improve network security by monitoring the network state so that threats and attacks can be detected and rectified. With decades of development, IDS still lags in performance in terms of detection accuracy, false alarm rate, and unknown attack detection. To overcome this performance issue researchers implemented numerous machine learning techniques to detect the attacks. Conventional machine learning models identify the essential features through specific feature extraction techniques which increases the computation complexity of the system. For detailed attack details and their sub-categories, deep learning technique is used in the proposed work. The detection model incorporates ResNet based on Inception with a support vector machine to detect WSN intrusions. Proposed algorithm is applied to Standard NSL-KDD dataset and performance metrics like recall, precision, accuracy, and f1-score are considered for analysis. The comparative analysis demonstrates the proposed model performance of 99.46% accuracy is better than traditional approaches like random forest, decision tree, deep neural network and convolutional neural network.
    Keywords: deep learning; intrusion detection system; wireless sensor network; deep neural network; convolutional neural network.