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.
  
  
• 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.
  
  
• Ensuring safety of vehicular cyber physical systems using machine learning and MQTT  
  by Neha Bagga, Sheetal Kalra, Parminder Kaur 
  Abstract: One of the pressing concerns for emerging nations is the maintenance of roads, including the identification and repair of pavement distress, such as potholes and curvy roads. This not only helps drivers avoid accidents and fatalities on the road, but also assists civic bodies in identifying and maintaining roads. Previous research has focused on pothole detection and lane identification, with the distress details being shared with drivers via a database or an Android application. However, this approach is battery-intensive for sensors in smart vehicles and requires a regular internet connection. To address these issues, we have proposed a model trained using Python and TensorFlow to identify road distress and steep curves with an accuracy of 85.2% and 83.1% respectively. The simulation uses Geocoder to capture the geographical coordinates of the distress, and the collected data is transferred to other CPS devices in cars using MQTT. The data is shared via an MQTT broker (cloud), which outperforms databases and Android applications in terms of efficiency, sensor load, and internet connectivity.
  Keywords: VCPS; MQTT; safety; autonomous vehicle; sensors.
  DOI: 10.1504/IJVICS.2024.10068858
   
  
• Robust detection and revocation of mischievous nodes using non-voting-based mechanism  
  by E.Jayanthi Kamalasekaran, Tintu Vijayan, Rohini Aravindan, Prachi Amol Gadhikar, Sapna R, Sreelatha P. K 
  Abstract: The study focuses on addressing authentication challenges in Mobile Ad hoc Networks (MANETs), particularly the disruption caused by malicious nodes. The proposed Improved Non-Voting System (INVBS) efficiently detects and manages such nodes using packet loss. INVBS involves blacklisting accused nodes and whitelisting those reporting malicious behavior, enhancing network security. It considers factors like link failures, mobility issues, and traffic load intensity for accurate identification. Simulations using NS-2 reveal INVBS's effectiveness, surpassing the Cluster-Based Certificate Revocation approach. INVBS improves Packet Delivery Ratio by 44.80%, throughput by 10.49%, and demonstrates enhanced node speed vs. accuracy, making it a robust solution for MANET security and resilience against malicious nodes.
  Keywords: CA; certificate authentication; CRL; certificate revocation list; MANET; mobile ad hoc networks; cluster; digital certificates; malicious; packet loss; black list; NS2.
  DOI: 10.1504/IJVICS.2024.10069052
   
  
• Harnessing blockchain for resilient emergency message dissemination in vehicular ad hoc networks  
  by Iraq Reshi, Adil Mudasir Malla, Sahil Shola, Asif Ali Banka 
  Abstract: Vehicles exchanging information in real-time to enhance road safety and optimise traffic flow is a burgeoning field of study in Vehicular Ad hoc Networks (VANETs). Efficient transmission of emergency warning messages in VANETs is crucial to mitigate collision risks, minimise delays, and reduce message redundancy. In this study, we analyse existing protocols and propose the ”Rate Decreasing Algorithm” to address these challenges. The algorithm adaptively adjusts the transmission rate of emergency warning messages based on vehicle conditions, striking a balance between the risk of flooding the network and premature loss of messages. We integrate blockchain technology into the algorithm to enhance security and accountability, providing vehicle registration, message source verification, integrity maintenance, and transaction log capabilities. Our experimental results demonstrate that the proposed algorithm significantly reduces collisions and delays compared to existing protocols, even in scenarios with varying vehicular densities. Blockchain-based mechanisms enhance the algorithm's performance, ensuring trust, integrity, and accountability in vehicle-to-vehicle (V2V) communication.
  Keywords: VANET; blockchain; RD algorithm; smart contracts.
  DOI: 10.1504/IJVICS.2024.10069078
   
  
• Freight vehicle path planning method based on Logistics 4.0 technology and customer dynamic needs  
  by Xinchun Wang 
  Abstract: In the process of logistics transportation, customer demand is constantly changing, but the current vehicle path planning method considering customer dynamic demand has the problem of low quality. For enhancing the quality of logistics services and decrease logistics costs, this study constructed a freight vehicle path planning model using Logistics 4.0 technology and customer dynamic needs. Firstly, the mathematical model of path planning under fixed demand is constructed from the aspects of vehicle driving cost, time window constraint and vehicle loading constraint. The mathematical model of fixed demand is solved by simulated annealing algorithm. Then, we use the master-slave parallel thought and Tabu search algorithm to develop the path planning strategy under dynamic demand. The experimental results show that the time utilisation rate and vehicle loading utilisation rate reach 100% and 94.34%, respectively after considering the actual dynamic demand of users. With the increase of customer size, the solution deviation of the research design model is less than 2%. This indicates that the model constructed by the research institute can effectively improve the efficiency of logistics transportation and customer satisfaction. And it can meet the dynamic needs of customers and enhance the quality and efficiency of logistics transportation.
  Keywords: Taboo search algorithm; Logistics 4.0; path planning; dynamic demand; transportation efficiency.
  DOI: 10.1504/IJVICS.2024.10069101
   
  
• 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
   
  

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.