International Journal of Vehicle Information and Communication Systems (10 papers in press)

Regular Issues

• Efficient clustering for wireless sensor networks using modified bacterial foraging algorithm  
  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  
  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  
  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.
  
  
• Automatic number plate recognition via convolutional neural network for residential gate access control  
  by Shannise Tan Jing Yi, Sarah Atifah Saruchi, Fahri Helta, Nor Aziyatul Izni 
  Abstract: Traditional guardhouse visitor management system at residential gate that practices manual checking of residents and visitors passes can result in traffic congestion during peak hours. To address this issue, this study proposes an Automatic Number Plate Recognition (ANPR) system using a customised Convolutional Neural Network (CNN) to automate residential and visitor verification process by recognising the number plate, thereby reducing traffic congestion. In addition to the existing CNN-based ANPR system, this study investigated the performance of the combination of computer vision techniques with a custom CNN image classification model. Comparison analysis was carried out between YOLOv3 and computer vision methods, and between MobileNetV2 and a custom CNN model to identify the most effective techniques for number plate localisation and character recognition. The models performance was evaluated using 150 images, where the custom CNN model outperformed MobileNetV2 with an accuracy of 0.997. Image augmentation was introduced to diversify the training set, where the custom CNN model with augmented data achieved an accuracy of 0.998 and an F1 score of 0.999. The results suggest that the proposed CNN-based ANPR system has the potential to automate the residential verification process and reduce traffic congestion.
  Keywords: ANPR; automatic number plate recognition; CNN; convolutional neural network; CV; computer vision; DL; deep learning; MobileNetV2; YOLOv3; TensorFlow.
  DOI: 10.1504/IJVICS.2023.10069978
   
  
• Performance analysis of dual-hop DF multi-relay communication applying TAS/MRC and JTRAS system over Fisher-Snedecor F fading channels  
  by Hubha Saikia, Rajkishur Mudoi 
  Abstract: In this paper, the outage probability (OP), bit-error-rate (BER) and ergodic capacity of dual-hop and decode-and-forward (DF) type multi-relay aided communication system subject to Fisher-Snedecor F fading channel are analyzed. The multiple-input-multiple-output (MIMO) technique is utilized in the communication system. The MIMO system can upgrade the system performance, but such a method uses plenty of antennas, thereby increasing the cost and hardware ramifications of the system. In this article, to prevail over these problems, at first the transmit antenna selection (TAS) is utilized for the transmission of signals along with the maximal ratio combining (MRC) diversity receiver. In the second method, a joint transmit and receive antenna selection (JTRAS) diversity scheme is applied for communication. The expressions of OP, BER and ergodic capacity are obtained concerning TAS/MRC as well as JTRAS type MIMO systems.
  Keywords: BER; cooperative communication; dual-hop; decode-and-forward; joint transmit and receive antenna; maximal ratio combining.
  DOI: 10.1504/IJVICS.2025.10070719
   
  
• EdgeDriver: optimising autonomous driving assistance with multi-LLM framework in cloud-edge computing environments  
  by Yitian Zhu 
  Abstract: The deployment of LLMs is challenged by their computationally intensive nature, making di-rect implementation on mobile devices impractical. A viable solution to this issue has been the deployment of LLMs in cloud-edge computing environments, where the computational load can be distributed more efficiently. We introduce EdgeDriver, an innovative autonomous driving service framework that harnesses the power of multiple LLMs to offer enhanced driv-ing assistance. This paper presents a comprehensive evaluation of EdgeDriver's service quality across various input lengths and model parameters. Through meticulous experimentation, we have discovered that a model size of 14B parameters, when processing input texts of 150 words, exhibits optimal performance, striking a balance between accuracy and computational efficiency. By addressing the challenges of deploying LLMs and demonstrating the feasibil-ity of a multi-LLM-based framework, this study contributes valuable insights to the field of smart transportation.
  Keywords: autonomous driving; large language models; mobile edge network; AIGC.
  DOI: 10.1504/IJVICS.2024.10070784
   
  
• A review of different strategies for vehicle collision avoidance  
  by Saloni Valecha, Umesh Dutta, Sudesh Pahal 
  Abstract: Vehicular Networks (V2X) technologies play important role in improving road safety, traffic efficiency and communication among vehicles. The paper commences by explaining the pivotal role of trajectory prediction in V2X systems, emphasizing its significance in anticipating the future positions and behaviours of vehicles. Analytical approaches and data-driven approaches, such as machine learning and deep learning techniques, are analysed for their potential to process data sets for enhanced prediction accuracy. Furthermore, the paper investigates early warning collision system, which leverage trajectory prediction and risk assessment to provide timely alerts to drivers and autonomous vehicles. Various methodologies, including sensor-based systems, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols are analysed for their effectiveness in preventing collisions and enhancing overall road safety. By exploring the intricate interplay between trajectory prediction, risk assessment and early warning collision systems, this review paper offers insight into the state-of-art methods and technologies shaping the future of vehicular networks.
  Keywords: collision avoidance; vehicle trajectory prediction; risk assessment; early warning systems; vehicular communication; machine learning; neural networks; deep learning and V2X.
  DOI: 10.1504/IJVICS.2025.10070922
   
  

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

• A novel framework for efficient information dissemination for V2X  
  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  
  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  
  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.