Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET
|Deepak Rewadkar1 , Dharmpal Doye2|
1 Department Information Technology, Government Polytechnic Awasari, Pune, India.
2 Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India.
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Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 40-49, Mar-2018
Online published on Mar 30, 2018
Copyright © Deepak Rewadkar, Dharmpal Doye . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Deepak Rewadkar, Dharmpal Doye, “Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.40-49, 2018.
MLA Style Citation: Deepak Rewadkar, Dharmpal Doye "Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET." International Journal of Computer Sciences and Engineering 6.3 (2018): 40-49.
APA Style Citation: Deepak Rewadkar, Dharmpal Doye, (2018). Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET. International Journal of Computer Sciences and Engineering, 6(3), 40-49.
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|A traffic-aware routing in VANET is a prime step in transmitting the long data for applications. Researchers’ address that the traditionally used routing protocols employed in Mobile Ad Hoc Networks are not suitable for routing in VANET, as VANETs differ from MANETs in the mobility model and environment. The demand to develop a traffic-aware protocol in VANET initiated to propose a routing protocol, termed as Adaptive Autoregressive Whale Optimization algorithm (Adaptive-ARW). The main goal of the proposed algorithm is to select the optimal path for performing routing in VANETs, for which the traffic required to be predicted. For predicting the traffic in the road segment, Exponential Weighed Moving Average (EWMA) is employed that predicts the traffic based on the average vehicle speed and the average traffic density. The minimum values of average speed and vehicles average traffic density to the less traffic density. Using the predicted traffic, the routing paths are generated, and the optimal paths are selected using the proposed algorithm that exhibits adaptive property. The analysis of the proposed algorithm provides the End-to-End delay, distance, average traffic density, and throughput of 2.938, 2.08, 0.0095, and 0.1354, respectively.|
|Key-Words / Index Term :|
|Exponential Weighed Moving Average (EWMA), End-to-End Delay (EED), Whale Optimization algorithm (WOA), Autoregressive Model, Adaptive property|
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