International Journal of Vehicle Information and Communication Systems (21 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.
  
  
• Performance analysis of a fog-computing-based vehicular communication  
  by Manoj Kumar, Pratik Gupta, Ankur Kumar 
  Abstract: Vehicular networking is an emerging research area. It enables vehicles equipped with sensing capacities to establish communication vehicle-to-vehicle or with available roadside infrastructure. It has significant applications in terms of information and traffic management and road safety. To attain effective communication in vehicular networking, a hybrid technological solution is proposed in terms of blockchain in vehicular communication using vehicular resources, cloud or fog computing environment. Blockchain-based fog computation is an alternative solution to get both security and privacy, especially in vehicular communication. As security and privacy are the key challenges in cloud-assisted vehicular communications owing to the adoption of the internet of things environment, a secure and efficient communication framework is required. This paper presents a model based on blockchain technology i.e. blockchain in fog nodes. This framework for vehicular communication ensures security and privacy. The presented frameworks are efficient being based on new blockchain design rather than the general cloud and fog-computing framework. This scheme has capabilities to establish an authenticated connection among the user, the fog node and the cloud server, where cloud services can respond to users treating fog node as a semi-trusted party. The effect of the proposed scheme was investigated and the obtained results are explained in the form of tables and graphs.
  Keywords: blockchain; vehicular communication; elliptic curve cryptography, authentication key agreement.
  
  
• 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.
  
  
• Driving style recognition of highway-driving semi-trailer at different altitudes  
  by Ke Liang, Huasheng Chen, Yu Ye, Song Zhang, Mingzhang Pan 
  Abstract: Driving style provides information about driving behaviour and the driving environment, which reflects the driver's operation while driving. High altitudes can significantly influence the human body, thereby affecting driving ability. Consequently, accurately recognizing driving styles at different altitudes has significant implications for driving safety, road design, and fuel economy. This paper proposed a method that incorporates data processing, feature selection, a Bi-LSTM autoencoder, and spectral clustering to address this issue. Based on the analysis of real-driving data experiments, three driving styles were identified as calm, moderate, and aggressive. These styles accounted for 46%, 19%, 36% in plateau driving and 33%, 29%, 38% in plain driving. The results demonstrate how the proposed method can effectively recognize driving styles at different altitudes with fewer features. Additionally, driving styles remained relatively consistent for the same driver driving at various altitudes, despite changes in vehicle performance.
  Keywords: driving style recognition; whale optimisation algorithm; feature selection; Bi-LSTM; autoencoder; spectral clustering.
  DOI: 10.1504/IJVICS.2023.10058625
   
  
• Performance analysis of pentagonal MIMO antenna with elliptical slots for 5G V2V communication  
  by A. Amsaveni, L. Harish, S. Rashmi, A. Kaiser 
  Abstract: The purpose of this research is to develop a slotted pentagonal MIMO antenna specifically for 5G vehicle-to-vehicle (V2V) communication. The proposed antenna takes the shape of a pentagon with elliptical slots, and the feed is positioned slightly away from the centre to optimise gain. V2V communication relies on antennas that can effectively capture signals even while in motion within a vehicle. The proposed antenna supports dual operating bands, enabling it to operate in the required frequency ranges. In the 5G frequency spectrum, the antenna achieves a maximum gain of 7.34 dB, which can significantly enhance V2V communication capabilities. The presence of elliptical slots enables the antenna to focus the transmitted and received signals in specific directions, improving signal strength, directivity, coverage, and overall system performance. These characteristics make it well-suited for enabling reliable and efficient V2V communication in the context of 5G networks.
  Keywords: V2V; 5G frequency bands; MIMO; antenna; communication.
  DOI: 10.1504/IJVICS.2023.10059209
   
  
• Experimental study of the weather effects on LoRa-based vehicular communications  
  by Jetendra Joshi, Ajay K. Singh 
  Abstract: With the advancement LoRa nodes, which works in the sub-GHz frequency bands, are used in IoT applications and in vehicular environments Deployment of these low power long range sensor nodes require better analysis of the link quality and weather and environmental conditions In literature very little information is available about the impact of weather conditions on the performance of LoRa based applications The impact of the height and the shaded region will also impact the link quality The sub urban environment is considered in the given paper to evaluate the performance of the several link qualities under the weather conditions such as fog, humidity, temperature, shaded region, and the impact of height Regular pattern of RSSI is analyzed and PDR values are presented to understand the impact of the weather conditions and other effects on the link quality Real outdoor test bed experimentation was performed to validate the results.
  Keywords: humidity; temperature; weather condition; long range technology; PHY settings.
  DOI: 10.1504/IJVICS.2023.10060432
   
  
• Real-time voice-controlled human machine interface system for wheelchairs implementation using Raspberry Pi  
  by Aymen Mnassri, Sihem Nasri, Mohamed Boussif, Adnane Cherif 
  Abstract: The article describes the development of a wheelchair prototype designed to facilitate the mobility of people with disabilities. The proposed system is based on voice commands to ensure communication between humans and machines. The system consists of two modules. The first module involves the detection, processing, and classification of actual voice signals acquired from a mobile phone. This module incorporates a robust and excellent speech recognition strategy. Indeed, the combination of Mel Frequency Cepstral Coefficients (MFCC) and the discrete wavelet transform (DWT) in signal processing and feature extraction allows for better performance, achieving an effective recognition rate of 100% for an SNR of 5 db. The second module presents the mechanical design and development of the actual prototype, which enables real-time simulation of the first module. This module is based on a Raspberry Pi 3 board with a Linux operating kernel. Finally, tests carried out on the designed wheelchair prototype have demonstrated its efficiency, robustness, and excellent response to critical situations, particularly obstacles.
  Keywords: wheelchair; speech recognition; human-machine-interface; real-time; Raspberry Pi; mobile phone.
  DOI: 10.1504/IJVICS.2023.10060591
   
  
• Detection and prevention of various routing attacks in RPL for a smart vehicle environment using an enhanced privacy secure-RPL routing protocol  
  by P. Deepavathi, C. Mala 
  Abstract: The recent advancements in Internet of Things (IoT) play a significant important role in day-to-day activities of human beings to connect things-to-things; it reduces the efforts of an individual as well as increases the safety and smartness of people through smart phones. With the increase in population, there is a tremendous increase in the number of vehicles, which leads to more possibilities of road accidents. In a Smart Vehicle Environment, various sensors are used to collect vehicle-related information from different locations and alert the driver about road traffic conditions to avoid unexpected accidents. This paper proposes an Enhanced Privacy Secure-Routing Protocols for Low Power and Lossy Networks (EnPS-RPL) routing protocol to detect various attacks and prevent a vehicle from unexpected accidents. The proposed work is simulated in Cooja Platform Simulator which is in Contiki Operating System. The simulation results show that the proposed algorithm improves networks' lifetime, throughput and decreases the packet loss ratio and end-to-end delay compared to the existing RPL-based protocols.
  Keywords: EnPS-RPL; smart vehicle environment; road traffic; packet loss; RPL-based protocols.
  DOI: 10.1504/IJVICS.2023.10060771
   
  
• Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks  
  by Cong-Xiang Wang, Chih-Min Chao 
  Abstract: How to design an efficient route for a mobile charger is an important issue in Rechargeable Sensor Networks (WRSNs). Mobile Chargers are costly and may consume a lot of time to reach and charge sensor nodes. Existing route planning algorithms either fail to minimise the number of mobile chargers or do not consider the actual traffic conditions. In this paper, we propose a Hybrid-Clustering-Minimum-Mobile-charger route planning algorithm (HCMM) for WRSNs. The charging route selected by the HCMM protocol prolongs the network lifetime by using a minimum number of mobile chargers. Simulation results verify that the charging efficiency achieved by HCMM is better than that of the m-MTS protocol which is currently the best charging route planning protocol for dispatching the least number of mobile chargers.
  Keywords: wireless charging; mobile charger; wireless rechargeable sensor networks; route planning.
  DOI: 10.1504/IJVICS.2023.10060772
   
  
• A proactive load balanced handoff scheme for vehicular ad-hoc networks  
  by Rajeshwari Madli, G. Varaprasad 
  Abstract: The embracement of the wireless technology by the automobile industry has led to novel research interests in the field of Vehicular Ad-hoc Networks (VANETs). In addition to ad-hoc mode, a VANET supports infrastructure mode of communication, which provides access to internet services such as Hyper Text Transfer Protocol (HTTP), Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP), through access points known as Road Side Units (RSU). Vehicles can benefit from these services when associated with an RSU. However, because of uneven distribution and high mobility of vehicles, some RSUs are overloaded, resulting in network congestion and affecting the packet delivery ratio and throughput. This paper proposes a proactive load-balanced handoff scheme, in which vehicles evade overloaded RSUs and choose an RSU based on signal strength, network load and service reliability. Simulation results indicate that vehicles using the load-balanced handoff method, experience a higher packet delivery ratio and throughput.
  Keywords: VANET; handoff; load balance; RSU reliability; packet delivery ratio.
  DOI: 10.1504/IJVICS.2023.10060773
   
  
• Compromised-resilient counter-measures against black-hole and worm-hole attacks in edge-based internet of vehicles  
  by Oladayo Olufemi Olakanmi, Kehinde Oluwasesan Odeyemi 
  Abstract: The Internet of Vehicles (IoV) is a distributed network that allows vehicles, pedestrians and several road units to exchange traffic and road-related information in real-time to increase users' safety and convenience. However, its inter-connectivity exposes the vehicles and the people to different cyber-attacks. In this paper, we identify the black-hole and worm-hole attacks as the major security threats to the IoV. We then propose periodic-time slicing and trust degree approaches to detect and discourage black-hole attacks, and a cryptography procedure to detect and prevent worm-hole attacks in the IoV.
  Keywords: car connectivity; cyber-security; cloud and fog-assisted; internet of things; autonomous vehicles.
  DOI: 10.1504/IJVICS.2023.10060774
   
  
• Modified stable weighted clustering scheme for secured V2V communications  
  by Siman Emmanuel, Ismail Fauzi Bin Isnin, Mohd. Murtadha Bin Mohamad 
  Abstract: Designing a Medium Access Control (MAC) protocol for vehicular ad hoc networks is challenging due to high node speeds, topology changes, infrastructure limitations, and varying quality-of-service requirements. To address these, we propose the Enhanced Cluster Multiple Access (ECMA) protocol, supporting multiple access methods and using a modified weighted clustering algorithm for Cluster Head (CH) selection, ensuring even vehicle distribution. A Chain Markov model manages smooth transitions between access mechanisms based on node thresholds, maintaining network stability. Evaluation with four weighting factors, including network throughput, end-to-end delay, and Packet Delivery Ratio, showcases the protocol's robustness. It outperforms Stable-Based (SB) and Threshold-Based (TB) techniques in various traffic scenarios with fewer modifications. Surprisingly, cluster stability improves by 52% to 61% with more vehicles and extended transmission range, and compared to EWCA and VeMAC protocols, our method excels in data PDR and end-to-end delay enhancement.
  Keywords: cluster-based; IEEE 802.11p; time division multiple access; threshold; stable weighted clustering; vehicular ad hoc network.
  DOI: 10.1504/IJVICS.2023.10060776
   
  
• An intelligent hybrid model approach for predictive maintenance of tool wear using machine learning techniques  
  by Soorya Prabha Mohan, S. Jaya Nirmala 
  Abstract: Machine uptime is highly important as the repairing time takes longer which affects the production and the manufacturing industry focus on new ways of being competitive. Manufacturing and assembly parts of the machine are the key component for ensuring machine uptime. Maintenance of these components plays a major role in ensuring the key component health and is an ongoing process. For this, predictive maintenance is the commonly used approach, based on machine running conditions and tool information in production environment. For the proposed methodology, data has been collected from a well-reputed machinery manufacturer. This paper presents the hybrid model, which predicts the machine failure based on multiple ensembling techniques followed by the stacking approach, which performs the k-fold cross-validation and leads to results that provide good accuracy and less false alarms.
  Keywords: tool wear; heat dispatch failure; stacking; ensembling.
  DOI: 10.1504/IJVICS.2023.10057196
   
  

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.
  
  
• PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm  
  by Righa Tandon, P.K. Gupta 
  Abstract: Vehicle-to-Vehicle communication is one of the new paradigms of networking which should be secure, fast and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using matrix array symmetric key (MASK), digital signature algorithm (DSA) and intelligent water drop (IWD). The proposed security scheme also ensures handling of many security attacks such as key-guessing, non-repudiation, replay and modification. In the proposed scheme, to preserve the vehicles identity, we have provided different pseudonyms to each vehicle in the network, which ensures secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of time-delay during vehicle to vehicle communication. In this algorithm, we have used Dijkstras algorithm for finding the optimal shortest path during vehicular communication. Obtained results show that the proposed scheme is effective and efficient as it reduces the time-delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes.
  Keywords: pseudonyms; vehicle-to-vehicle communication; security; time-delay.
  
  

Special Issue on: Artificial Intelligence Driven Vehicular Communication Networks in Beyond 5G Era

• Real-time separation optimisation algorithm for hybrid blind speech signal in vehicular communication networks in beyond the 5G era  
  by Guoan Hu 
  Abstract: In this paper, we conduct an in-depth analysis on the real-time separation optimisation algorithm for the hybrid blind signal in wireless communication networks. In the process of the real-time separation of blind signals in network communication, firstly, the hybrid signal in the network communication is whitened to remove the core correlation between the blind signals. In this foundation determined in the network correspondence the mixed signal matrix of linear transformation, it uses transformation matrix to separate in the network correspondence in the blind signal independent signal component, from this the network correspondence area south of core signal is restored. The data analytic framework is suggested to optimise the framework of the proposed tool. Then, the designed model is applied to the vehicular communication networks in beyond the 5G era, and the simulation gives the overall performance estimation to test the robustness.
  Keywords: real-time separation; optimisation algorithm; blind signal; wireless communication; vehicular communication; 5G technology.
  
  
• Hybrid deep learning-based intrusion detection system for wireless sensor networks  
  by V. Gowdhaman, R. Dhanapal 
  Abstract: Wireless sensor networks (WSNs) play an important role in the modern era and security has become an important research area. Intrusion detection system (IDS) improve network security by monitoring the network state so that threats and attacks can be detected and rectified. With decades of development, IDS still lags in performance in terms of detection accuracy, false alarm rate, and unknown attack detection. To overcome this performance issue researchers implemented numerous machine learning techniques to detect the attacks. Conventional machine learning models identify the essential features through specific feature extraction techniques which increases the computation complexity of the system. For detailed attack details and their sub-categories, deep learning technique is used in the proposed work. The detection model incorporates ResNet based on Inception with a support vector machine to detect WSN intrusions. Proposed algorithm is applied to Standard NSL-KDD dataset and performance metrics like recall, precision, accuracy, and f1-score are considered for analysis. The comparative analysis demonstrates the proposed model performance of 99.46% accuracy is better than traditional approaches like random forest, decision tree, deep neural network and convolutional neural network.
  Keywords: deep learning; intrusion detection system; wireless sensor network; deep neural network; convolutional neural network.