International Journal of Vehicle Autonomous Systems (4 papers in press)

Regular Issues

• Energy-efficient cluster-based routing in VANET assisted by hybrid jellyfish and beluga optimisation and fault tolerance  
  by R.K. Mahesh, Shivkumar S. Jawaligi 
  Abstract: VANET clustering solves scalability issues while fortifying the network and extending its lifespan. The following phases are included in the developed work on Energy-Efficient Cluster-based Routing (EECR) and Fuzzy-Topsis (FT) in VANET: Cluster head selection, routing and fault tolerance are the first three. Initially, the Hybrid Shuffled Shepherd Namib Beetle Optimisation Algorithm (HSS-NBO) method, which is centred on the prior work, is used to pick the cluster heads. Then, using the created Combined Jellyfish Beluga Whale Optimisation (CJ-BWO) model, routing is done as efficiently as possible while taking into account limitations such as security, trust, delay, quality of service, energy and connection quality (RSSI). Then, as a prerequisite for energy-efficient processing, the intermediate node forwarding mechanism is applied for void handling. In case of occurring faults, it is tolerated by initially detecting the faults and then proposing a recovery phase with a proxy agent Road Side Unit (RSU) deployed.
  Keywords: VANET; quality of service; fault tolerance; recovery phase; CJ-BWO algorithm.
  DOI: 10.1504/IJVAS.2024.10065454
   
  
• A review of traffic-aware content caching vehicular social networks in the internet of vehicles  
  by Vedha Vinodha Doraiswamy, Malathy Subramanium 
  Abstract: The Internet of Vehicles (IoV) is predicted to significantly enhance the safety and effectiveness of conveyance systems by supplying effectual data distribution among vehicles and people, vehicles and roads, vehicles and networks, vehicles and vehicles, etc. In this survey, 50 research papers on traffic-aware content caching are considered and reviewed. The numerous methods of traffic-aware content caching in vehicular social networks are categorised based on their computational mechanism, like Deep Learning, Optimisation-based techniques, Federated Learning and other techniques. Initially, for a reliable evaluation, research papers based on traffic-aware content caching in vehicular social networks from 2018 to 2023 are gathered. Thereafter, an extensive outline of traffic-aware content caching in vehicular social networks is depicted with its fundamental features. Then, the issues involved in the classified algorithms are enlightened. At last, the assessment is performed based on performance metrics, category analysis, publication year, data set and tools. Based on the estimation, it is concluded that many of the papers gathered depend on deep learning methods and SUMO is a widely used tool for execution.
  Keywords: deep learning; optimisation-based techniques; federated learning; internet of vehicles; content caching.
  DOI: 10.1504/IJVAS.2024.10066489
   
  
• Reviewing on wire arc additive manufacturing techniques with conventional approaches over literature review, algorithms, features and complications  
  by Jyothi Padmaja Koduru, T. Vijaya Kumar, Kedar Mallik Mantrala 
  Abstract: Wire Arc Additive Manufacturing (WAAM) attained more attention from professionals because of their advancements in producing huge components with medium geometric complications by Additive Manufacturing (AM). On account of all the factors mentioned above, this survey work explores the literature review of WAAM with past and present approaches of the last 10 years' publications. Initially, the introduction and basic process are discussed regarding WAAM. It is then followed by the literature survey of the WAAM model using distinct methodologies. Further, the chronological analyses are given to analyse the year of publication of WAAM. Subsequently, algorithm deployments for WAAM are given elaborately. The execution platforms, merits and demerits of the survey paper are also included. Then, the research gaps and challenges of former implemented methods are addressed, which is helpful for future improvement.
  Keywords: wire arc additive manufacturing; additive manufacturing; deep learning; gas metal arc welding; plasma arc welding; gas tungsten arc welding.
  DOI: 10.1504/IJVAS.2024.10066276
   
  
• An artificial intelligence-based approach for avoiding traffic congestion in connected autonomous vehicles  
  by Djamel Bektache, Nassira Ghoualmi-Zine 
  Abstract: The Internet of Vehicles (IoV) has led to the emergence of sustainable smart roads. Recent advancements in this field have focused on improving traffic flow and reducing congestion using intelligent systems. In this paper, we propose a novel approach called the 'Traffic Congestion Avoidance Approach (TCAA)'. Our approach leverages IoV technologies and deep learning algorithms to create a more responsive and efficient traffic management system. The IoV model facilitates communication between autonomous vehicles, allowing them to coordinate movements and optimise traffic flow seamlessly. Additionally, deep learning algorithms analyse real-time data, to predict and mitigate congestion dynamically. The performance evaluation of TCAA demonstrates the potential of intelligent traffic regulation systems. The union of IoV and deep learning technologies provides a robust solution to contemporary traffic challenges, paving the way for smarter, more sustainable urban mobility. This research underscores the transformative potential of AI-powered IoV systems in creating the smart roads, ultimately enhancing the quality of life in smart cities.
  Keywords: internet of vehicles; artificial intelligence; traffic regulation; avoidance congestion; smart roads.
  DOI: 10.1504/IJVAS.2024.10066104