Swarm Intelligence Based Algorithm for Efficient Routing in VANET
Gagan Deep Singh1, Manish Prateek2, Hanumat Sastry G3

1Gagan Deep Singh*, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
2Dr. Manish Prateek, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
3Dr. Hanumat Sastry G, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 1134-1126 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2857039520/2020©BEIESP | DOI: 10.35940/ijitee.E2857.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Many recent researchers are working to optimize solutions in the field of Vehicular Adhoc Network. However, none of them has yet claimed that it will fulfill all the challenges of such a dynamic region. VANET in itself is a complete area of study, research and improvements. Most of the researchers and industry consortiums has given their hypothesis and solution that depends on their predefined scenarios but no complete solution has designed until yet. Through this research work, the authors concluded that bioinspired solutions can be used to integrate along with VANET for a much accurate and optimized solution. The performance of VANET depends on various scenarios and due to the unpredictable behavior of the vehicle movement, no concrete solution can be claimed as of now. We incorporated Swarm Intelligence in VANET through the Ant Colony Optimization algorithm and found that the performance of VANET has enhanced by avoiding the entire congested path as it senses the pheromone trail. We have implemented and tested the results using open source software like Instant Veins, Simulation of Urban MObility (SUMO) and MObility model generator for VEhicular networks (MOVE). SUMO has used for testing the traffic simulation and MOVE is used to design model. Python for the script. The OSM used to take a map of Dehradun city. When we performed the experimental setup and found that the result confirms in reducing the traveling time of the nodes, which makes nodes faster and managed even it helps in saving the hydrocarbon fuels. During our approach, we have devised our own algorithm that has improvised the present Ant Colony Optimization algorithm and has concluded that the average traveling time of the nodes minimized through our approach. 
Keywords: Ant Colony Optimization, Dehradun, OSM, Swarm Intelligence, VANET.
Scope of the Article: Swarm intelligence