A review on VANET routing protocols and proposing a parallelized genetic algorithm based heuristic modification to mobicast routing for real time message passing
Issue title: Soft Computing and Intelligent Systems: Techniques and Applications
Guest editors: Sabu M. Thampi and El-Sayed M. El-Alfy
Affiliations:
Department of Information and Technology, School of Computing and Information Technology Manipal University Jaipur, Dehmi Kalan, Off Jaipur-Ajmer Expressway, Jaipur, (Raj.), Rajasthan, India
Correspondence:
[*]
Corresponding author. Rahul Saxena and Monika Jain, Department of Information and Technology, School of Computing and Information
Technology Manipal University Jaipur, Dehmi Kalan, Off Jaipur-Ajmer Expressway, Jaipur, (Raj.). Rajasthan 303007,
India. E-mails: [email protected] and [email protected].
Abstract: VANET has been an area of great interest and exploration for researchers to solve several challenging issues regarding communication, topology, security etc. in the last few years. Currently, a lot of work has been done and is looked after to establish effective communication and message passing among the vehicles (V2V and V2I routing) with a number of algorithmic models developed. The paper presents a survey of the routing algorithms proposed to have communication inside a VANET among the nodes. Since V2V and V2I interactions is a complex combinatorial problem which falls under the class of NP-Complete set of problems. The paper here presents a modified mobicast routing version using genetic algorithm with certain considerations for mutation and crossover operator for the algorithm in order to achieve more accuracy for the results. The method shows a great enhancement in the execution timing for a considerable number of vehicles where the traditional algorithms may hang up to produce a route. But still the serial version stucks up for heavy density vehicle scenarios for message passing in real time. So, the efficiency of the proposed method is enhanced using parallel processing power of multi-core and many-core processors using OpenMP and Computationally Unified Device Architecture (CUDA) API. The enhanced results show a great improvement in the performance in terms of execution time when compared with the serial algorithm, especially for the cases where the solutions cannot be obtained in real time. The results over GPU based architecture suggests that the proposed method has a huge potential to scale up with the vehicles on the road, thus, reducing the road side units for providing a larger range of coverage.
Keywords: VANET, Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), genetic algorithm, OpenMP, CUDA
