International Journal of Vehicle Information and Communication Systems (11 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.
  
  
• UAV-assisted computing offloading and resource allocation based on Stackelberg game in vehicular edge computing  
  by Yuhang Jiang, Zhiyong Wu, Xiuwei Hu, Yilong Sun, Yunhui Zheng 
  Abstract: Due to the computational density and complexity of vehicle applications, unique vehicle mobility, and limited edge server resources, vehicle edge computing (VEC) faces significant challenges. Unmanned aerial vehicles (UAVs) are considered an important integrated part of extending the reach of edge computing and providing computing resources. In order to achieve efficient and reliable service and resource utilization, we investigate UAV-assisted vehicle edge computational offloading and resource allocation mechanisms. The computing offload, migration, and security models are analyzed. A Stackelberg game model is constructed, iteratively analyzing the interaction among the VEC servers, UAVs, and VUs (VUs) to obtain an appropriate computational offloading and resource allocation scheme. Based on this, we propose an improved Stackelberg game algorithm (ISGA) to optimize computational offloading and resource allocation. The simulation results show that the proposed algorithm is feasible and performs competitively compared to other methods.
  Keywords: resource allocation; vehicle edge computing; UAV; Stackelberg game.
  DOI: 10.1504/IJVICS.2024.10066011
   
  
• Performance analysis of AODV-based VANETs in Guwahati city: a comparative study of propagation models  
  by Rajkumar Joydev Borah, D. Ganga 
  Abstract: As a part of technological advancement, vehicular communication plays a vital role in modern day of urbanisation. The performance of Vehicular Ad-hoc Network (VANET) mainly depends on routing protocols and propagation models. This study looks into how different propagation models affect VANET performance in real time environment. As vehicle environments are dynamic and demanding, selecting an accurate propagation model is essential to create reliable communication. In this work, performance of widely used propagation models, such as Free Space Propagation (FRIIS), ITU-R P.1411 Loss Model and Nakagami fading are assessed and contrasted in Adhoc On-Demand Vector (AODV) routing-based VANET, considering various real-time traffic scenarios of Guwahati city using SUMO and NS3 platform. The results show that ITU-R P.1411 propagation model gives better Average Throughput, End-to-End Delay, End-to-End Jitter Delay and MAC/PHY-Overhead, whereas FRIIS routing protocol gives better Packet Delivery Ratio of Basic Safety Messages, Average Routing Goodput and Packet Delivery Ratio.
  Keywords: VANET; AODV; propagation model; FRIIS; ITU-R P.1411; Nakagami.
  DOI: 10.1504/IJVICS.2024.10066105
   
  
• The application of BIM-based architectural space and omnidirectional vision in smart parking systems  
  by Wanhua Li, Lin Chen 
  Abstract: With the increasing number of cars, the problem of vehicle parking has become a more complex traffic problem. Therefore, this study focuses on the parking efficiency and safety of cars, and builds an intelligent parking system based on building information modeling technology and omnidirectional vision concept. The new system uses improved HybridA* algorithm and building information modeling technology to plan the vehicle's path, and then tracks the vehicle's trajectory through omnidirectional vision and predictive control algorithms to improve parking efficiency. The research results indicated that the error variation of path tracking was 0.1492 on the X-axis, 0.1318 on the Y-axis, and 4.1220 on the yaw angle. The braking and yaw angle changes throughout the entire parking process were relatively small. The new intelligent parking system can improve parking efficiency and enhance parking safety.
  Keywords: BIM technology; omnidirectional vision; predictive control algorithm; HybridA* algorithm; parking.
  DOI: 10.1504/IJVICS.2024.10066441
   
  
• Exploring the impact of e-transportation system on social, economic, and environmental development: moderation of information and communication technologies  
  by Guang Shao 
  Abstract: E-transportation systems (e-TS) offer crucial solutions for reducing carbon emissions, alleviating traffic congestion, and enhancing energy efficiency. E-transportation systems play a pivotal role in social, economic, and environmental development. By fostering sustainable mobility, this system contributes toward healthier communities, economic growth, and the preservation of natural resources. However, information and communication technologies (ICTs) are vital for e-TS that eventually facilitate real-time data exchange among vehicles, infrastructure, and users. This study particularly investigates the nexus among e-transportation systems and sustainable development (i.e., social, economic, and environmental development) along with moderation of evolving technologies in e-TS (e.g., vehicle-to-vehicle, vehicle-to-infrastructure, global positioning system, intelligent transportation systems, smart grid technologies, and IoT).
  Keywords: e-transportation system; information and communication technologies; social development; economic development; environmental development; structural equation modelling.
  DOI: 10.1504/IJVICS.2024.10066520
   
  

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

• Network traffic analysis using machine learning techniques in IoT network  
  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  
  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.