A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks

Manoj Sindhwani, Shippu Sachdeva, Akhil Gupta, Sudeep Tanwar (), Fayez Alqahtani, Amr Tolba and Maria Simona Raboaca ()
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Manoj Sindhwani: School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144001, India
Shippu Sachdeva: School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144001, India
Akhil Gupta: School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144001, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Fayez Alqahtani: Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
Amr Tolba: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Maria Simona Raboaca: Doctoral School, University Politehnica of Bucharest, Splaiul Independentei Street, No. 313, 060042 Bucharest, Romania

Mathematics, 2023, vol. 11, issue 3, 1-15

Abstract: The Vehicular Ad-hoc Network (VANET) is an innovative technology that allows vehicles to connect with neighboring roadside structures to deliver intelligent transportation applications. To deliver safe communication among vehicles, a reliable routing approach is required. Due to the excessive mobility and frequent variation in network topology, establishing a reliable routing for VANETs takes a lot of work. In VANETs, transmission links are extremely susceptible to interruption; as a result, the routing efficiency of these constantly evolving networks requires special attention. To promote reliable routing in VANETs, we propose a novel context-aware reliable routing protocol that integrates k-means clustering and support vector machine (SVM) in this paper. The k-means clustering divides the routes into two clusters named GOOD and BAD. The cluster with high mean square error (MSE) is labelled as BAD, and the cluster with low MSE is labelled as GOOD. After training the routing data with SVM, the performance of each route from source to target is improved in terms of Packet Delivery Ratio (PDR), throughput, and End to End Delay (E2E). The proposed protocol will achieve improved routing efficiency with these changes.

Keywords: vehicular ad-hoc networks; mean square error; k-means clustering; support vector machine; packet delivery ratio (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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