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  
  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.
  
  
• 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.
  
  
• 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
   
  
• Advancing cooperative adaptive cruise control: a comprehensive review of control methodologies and traffic impacts  
  by Ala Alobeidyeen 
  Abstract: Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) are transforming transportation by enhancing traffic flow, safety, and efficiency. ACC uses onboard sensors to maintain safe spacing, while CACC integrates vehicle-to-vehicle (V2V) communication for coordinated platooning. Despite their potential, challenges persist in mixed-traffic environments where human-driven vehicles introduce unpredictability. This study evaluates ACC/CACC impacts on traffic capacity, stability, congestion, and energy consumption. Results indicate improved efficiency in controlled settings, but real-world deployment is hindered by low penetration rates, communication infrastructure gaps, and integration with non-autonomous vehicles. Future advancements require adaptive algorithms, hybrid control strategies, and large-scale validation. Machine learning, robust V2V/vehicle-to-infrastructure (V2I) systems, and human behavior analysis are critical to optimizing these technologies. By addressing these challenges, ACC/CACC systems can advance toward scalable, sustainable transportation networks. This work provides actionable insights for refining CACC frameworks and guiding research priorities in intelligent transportation.
  Keywords: adaptive cruise control; cooperative adaptive cruise control; traffic capacity; string stability; congestion reduction; energy consumption.
  DOI: 10.1504/IJVICS.2025.10072045
   
  
• Energy storage capacity configuration and optimisation of electric/thermal integrated energy systems in the context of electric vehicle clusters  
  by Xiaoyan Zhang, Lei Ji 
  Abstract: In order to improve the energy efficiency and economy of the system, a double layer optimisation model was proposed to optimise the allocation of electric/thermal energy storage capacity in a dual optimisation integrated energy system. The innovation of the research lies in the dual optimisation of energy system operation strategies, combined with energy scheduling and energy storage capacity decision making. The experiment showed the total operating cost of the electric energy storage system and the disorderly charging Plan B for electric vehicles is 2210 yuan, and the rate of wind and light abandonment was reduced by 5.72%. The total operating cost of Plan C for the electric energy storage system, electric vehicle disorderly charging and thermal storage system is 2021 yuan. The total operating cost of Plan D, which considers the impact of Vehicle-to-Grid as a power storage system on energy configuration and scheduling, is the lowest at 1700 yuan. The average charging cost and daily average investment cost of the energy storage system were both reduced compared to Plan C. Electric vehicle V2G not only reduces its own electrical load, but also plays a positive role in system operation and vehicle owner charging economy.
  Keywords: electric vehicles; electric heating integrated energy; double layer optimisation; capacity configuration; energy storage.
  DOI: 10.1504/IJVICS.2024.10072319
   
  
• Optimisation of freight vehicle routes leveraging cloud computing and edge endpoints  
  by Hongxia Jin 
  Abstract: In the current freight industry, how to improve the economic benefits of freight transportation and solve the problem of insufficient information timeliness under cloud computing is a persistent challenge. In response to the problems of delayed information in cloud computing path planning and insufficient economic benefits of planning results, this study proposes a freight vehicle path optimization model supported by cloud computing and edge end. This model introduces edge computing into cloud platforms and constructs a genetic algorithm-based truck path optimization algorithm. According to test outcomes, the path planning time of the proposed model was at most 160 ms lower than that of a regular cloud computing environment. In simulation testing, the optimized path planning model saved a total cost of 16.8% compared to the pre optimized output. The proposed model effectively optimizes the economic benefits of freight transportation and solves the problem of insufficient information timeliness.
  Keywords: cloud computing; edge end; path optimisation; iteration; logistics.
  DOI: 10.1504/IJVICS.2024.10072356
   
  
• Data storage and sharing for the internet of vehicles and GPBFT consensus algorithm  
  by Jiyan Zhou, Jinfeng Liu 
  Abstract: The current centralized storage mode of the Internet of Vehicles system is prone to single point attacks, which cannot guarantee the safety and privacy of vehicle users Therefore, this study constructed a secure and efficient data storage and sharing architecture for the Internet of Vehicles, and proposed a practical Byzantine fault-tolerant consensus algorithm using reputation to improve the shortcomings of roadside unit equipment in the architecture, while also validating it The experimental results show that in the performance verification of reinforced data storage, the storage proportion of the research scheme is always lower than that of the comparison scheme In the simulated 5000 transactions, it is always lower than 40MB, while the comparison scheme reaches a maximum of 72MB The efficiency of querying registration information and vehicle data has increased by 38 2% and 49 1%, respectively In the shared performance verification, the percentage of fully loaded roadside unit devices
  Keywords: Internet of vehicles; GPBFT; Roadside unit equipment; Throughput; Malicious nodes.
  DOI: 10.1504/IJVICS.2024.10072357
   
  
• Green traffic management strategy for hybrid electric vehicles based on monocular deep velocity estimation algorithm  
  by Donggen Yang, Jiang Qiu 
  Abstract: Energy management strategies can control the energy flow between hybrid vehicle fuel tanks and electrical energy storage by addressing energy allocation issues, but they are influenced by factors such as driving conditions, electric drive system structure, and load characteristics. Based on this, a monocular depth and velocity estimation algorithm was proposed, which was analysed, extracted and fused from the perspective of network features combined with a learning framework, and the strategy function was set and the vehicle power results were analysed. The results show that the average extraction accuracy of the vehicle feature information management strategy of this method exceeds 85%, and the error under different working conditions is less than 5%. The vehicle energy management strategy based on environmental information integration can greatly improve the fuel economy and power performance of hybrid vehicles, providing new ideas and tools for green traffic management and design of vehicles.
  Keywords: monocular deep velocity estimation algorithm; hybrid electric vehicles; deep learning; energy management strategy; energy conservation.
  DOI: 10.1504/IJVICS.2024.10072590
   
  
• Addressing rushing attacks in VANETs: a comprehensive two-tier QoS-based approach  
  by Sadanand R. Inamdar, Jayashree I. Kallibaddi 
  Abstract: Vehicular Adhoc Networks (VANETs) enable real-time safety communication among vehicles but face security threats like Rushing attacks. This study proposes a 2-Tier approach leveraging Quality of Service (QoS) to detect assaults. QoS factors divide vehicles into white and black categories based on distance, energy, and bandwidth. Cluster Head (CH) nodes are selected based on trust and delay parameters. The Self-Adaptive Poor Rich Optimization (SA-PRO) algorithm detects Rushing attacks and other threats. NS2 simulator implementation evaluates energy usage, drop, fairness index, and latency. Results show promising performance, with a 98% reduction in energy consumption. Additionally, the proposed approach ensures effective detection and mitigation of Sybil and DoS attacks, as well as GPS Spoofing, enhancing the overall security of VANETs. The findings suggest that the 2-Tier approach with QoS and SA-PRO algorithm significantly improves the resilience and efficiency of VANET systems.
  Keywords: VANET; vehicular adhoc network; rushing attack; QoS; quality of service; two-tier model; distance; energy; bandwidth.
  DOI: 10.1504/IJVICS.2024.10072634
   
  
• Innovative approach to detect wormhole attack from vehicular ad-hoc network by using variable control chart  
  by Shahjahan Ali, Parma Nand, Shailesh Tiwari, Kumari Hemlata 
  Abstract: Wireless nature of vehicular ad-hoc network (VANET), have made it more sensitive towards various types of attacks. Wormhole is one of the attack by which the security of VANET’s routing may be disturbed. In this research paper innovative approach based on variable control chart is proposed to detect the wormhole attack from VANET. Variable control chart is used in industry to judge the quality of predefined process. This approach can detect misbehaving nodes in real time by applying the monitoring system at every receiving node within the network. Here, SUMO 0.32.0 and NS-2.35 simulators are used. The results signify that the proposed approach is capable to detect the wormhole attack from VANET. The novelty of this research work is that, here a new approach based on variable control chart is used to make the VANET more secure by detecting the wormhole attack from VANET.
  Keywords: VANET; vehicular ad-hoc network; routing; wormhole; network; SUMO 0.32.0; NS-2.35; attack.
  DOI: 10.1504/IJVICS.2025.10072857
   
  

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

• 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.