International Journal of Vehicle Information and Communication Systems (9 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.
  
  
• Bayesian optimised route and SOH estimation effect for Li-ion battery management system of electric vehicles based on LSTM  
  by Zhijun Xiao 
  Abstract: Lithium-ion batteries are widely used in electric vehicles, and accurate state of health (SOH) estimation is crucial for driving safety. This study applies a long short-term memory (LSTM) algorithm to model SOH based on health features correlated with standardised capacity. Since manual parameter tuning is inefficient and training is time-consuming with large datasets, a domain space design inspired by manual adjustment is combined with Bayesian optimisation for hyperparameter configuration. Experimental results show that the optimised LSTM improves estimation accuracy by 0.0235%. Compared with grid and random search, Bayesian optimisation reduces relative error by 50.63% on average and requires the least time, demonstrating both higher optimisation efficiency and near-optimal parameter selection.
  Keywords: LSTM; lithium battery management system; Bayesian optimisation algorithm; SOH estimation; battery health characteristics.
  DOI: 10.1504/IJVICS.2025.10073695
   
  
• Estimating road profiles using functional observer: a half-vehicle model approach  
  by Mohamed Saber, Mohamed Ouahi, Ghali Naami, El Akchioui Nabil 
  Abstract: To guarantee both driver and vehicle safety when driving and to avoid different kinds of accidents, particularly rollover accidents, which may culminate in catastrophic consequences, it becomes essential that drivers have as precise comprehension of the variables that influence vehicle as possible. The nonmeasured variables in the system state are estimated using a half-vehicle model that includes an unknown input functional observer, allowing one to precisely estimate the unknown inputs such the road profile characteristic. With the assistance of Lyapunov-Krasovskii stability theory, convergence conditions, and a solution of linear matrix inequalities (LMIs), the suggested functional observer design constraints are developed in order to accomplish the goal of minimizing the estimation error convergent to zero, which ultimately results in a more accurate determination of the observer parameters. Furthermore, a simulation that accurately evaluates vehicles variable’s estimation and changing road conditions prediction has been implemented. By developing advanced vehicle control and monitoring systems as a whole this research makes a substantial contribution to the field of road safety.
  Keywords: vehicle dynamics; functional observer; state estimation; road profile estimation; half-vehicle model.
  DOI: 10.1504/IJVICS.2025.10074228
   
  
• Application of improved BFO algorithm for urban rail transit scheduling  
  by Yuting Li 
  Abstract: To improve the operational efficiency and quality of trains, this study improved the bacterial foraging optimisation algorithm and applied it to the optimisation of urban rail transit train scheduling. By introducing ant foraging behaviour and bacterial foraging behaviour, a hybrid optimisation algorithm with stronger global search ability is constructed and the concentration of pheromones is dynamically updated during the iteration process to avoid the influence of local optimal solutions on global optimal solutions. Meanwhile, a new model for driving demand has been established. Under 50 dimensional multimodal testing, compared with ant colony optimisation, traditional bacterial foraging optimisation and adaptive bacterial foraging optimisation, the improved bacterial foraging optimisation improved accuracy by about 19%, 30% and 8%. In the 15th and 20th speed limit events, with and without initial delay, the objective function value was 0, and the final delay time was also 0. Therefore, the improved bacterial foraging optimisation can remarkably improve the operational efficiency of trains, reduce operating costs, enhance the overall operational level of urban rail transit and further promote the sustainable growing of urban rail transit.
  Keywords: rail transit; optimise scheduling; ACO; BFO; operation diagram; C3 system; delay time.
  DOI: 10.1504/IJVICS.2025.10074656
   
  
• Vehicle assisted driving system based on FNN-PID controller  
  by Yunpeng Li 
  Abstract: The rapid development of urban transportation has increased the number of motor vehicles, causing serious traffic congestion and frequent accidents, which challenge the efficiency of transportation systems. To improve autonomous driving performance and relieve traffic pressure, this study proposes a vehicle-assisted driving system combining a fuzzy neural network with a proportional integral derivative controller. The longitudinal control structure is analysed, and controllers at different levels are designed. Experimental results indicate that the maximum overshoot is reduced by 11.4% compared with the traditional proportional integral derivative controller. The rise time and adjustment time decrease by 0.3 s and 0.9 s, and the response stabilises at about 0.5 s with almost no overshoot. In practical road tests, the controller reaches the desired speed within 10 s and maintains a stable following distance, demonstrating strong robustness and effective control performance.
  Keywords: fuzzy neural network; assisted driving; fuzzy control; vertical control system; intelligent vehicles.
  DOI: 10.1504/IJVICS.2025.10074859
   
  
• Computation of delay at traffic signals using an Mt/Mt/1 queueing model  
  by Kuruvila Reya, Narayanan Viswanath 
  Abstract: Several formulae/methods exist in the literature to compute delay at traffic signals. Among these, the Incremental Queue Accumulation (IQA) model can be considered a universal model in that the vehicle arrival and departure rates are the only parameters required for its computation. In the IQA model, the vehicle queue length computation uses a deterministic model. This paper presents a variant of the IQA model, in which the queue length is computed using the transient probability distribution of an Mt/Mt/1 queueing model. The average and total delay are defined similarly to the IQA model. The performance of the new delay model was compared to that of five existing delay formulae, including the IQA delay, based on simulated traffic data. This study shows that the new model outperforms the existing delay models when the overflow queue sizes are high.
  Keywords: delay at traffic signals; queueing model; time-dependent rates; cycle time.
  DOI: 10.1504/IJVICS.2025.10074878
   
  
• Blockchain-based secure authentication protocol for vehicular ad-hoc networks  
  by Ram Baksh, Samiulla Itoo, Musheer Ahmad 
  Abstract: Vehicular Ad Hoc Networks (VANETs) facilitate communication among vehicles and roadside units (RSUs) to enhance road safety and traffic efficiency. However, ensuring the security and privacy of communications in VANETs remains a significant challenge. In this paper, we propose a Blockchain-based Conditional Privacy-Preserving Authentication (BCPPA) protocol for VANETs to address these challenges. The protocol leverages Blockchain technology for secure data storage and employs smart contracts for authentication and revocation processes. Furthermore, we introduce a key derivation algorithm to alleviate the burden of key pre- storing in vehicle On-Board Units (OBUs). Our protocol utilizes modified Elliptic Curve Digital Signature Algorithm (ECDSA) with batch verification to enhance verification efficiency in VANETs. We provide a detailed description of the protocol, along with security and performance analysis, demonstrating its effectiveness in ensuring secure and privacy-preserving communications in VANETs.
  Keywords: blockchain; ROR model; elliptic curve cryptography; smart contract; vehicular ad-hoc network.
  DOI: 10.1504/IJVICS.2025.10074924
   
  
• Vehicle traffic information management based on big data technology and DETR algorithm  
  by Qizheng Yang, Meng Gao, Kai Sun 
  Abstract: A vehicle tracking algorithm using Detection Transformer (DETR) for small data annotation is proposed to enhance vehicle recognition and tracking accuracy in traffic management. Principal component analysis is introduced to handle complex vehicle feature data. Combining these methods, a vehicle traffic information management system based on big data technology and DETR is constructed. Experiments show that the DETR-based algorithm achieves an average accuracy of 0.96 and a loss value of 0.93, outperforming other algorithms. The system has a recognition accuracy of 95.2% and processes keyframe information at 65 frames per second, significantly better than other models. Results indicate that the proposed algorithm and system effectively improve vehicle information management accuracy.
  Keywords: DETR; PCA; big data technology; vehicle traffic; information management.
  DOI: 10.1504/IJVICS.2025.10075114