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PRAVN: perspective on road safety adopted routing protocol for hybrid VANET-WSN communication using balanced clustering and optimal neighborhood selection

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Abstract

Recently, the transportation technologies and driving become essential part in everyone's life. International organization of motor vehicle manufacturers submitted manufactured vehicle list that reaches 97 million vehicles in a year. However, million people are died in road accidents each year and this number also increases in parallel. Vehicular ad hoc networks (VANET) should be used to gather and distribute safety information to reduce the accident growths. Recently, several protocols have been proposed to address these issues, but the trade-off between energy and delay is major issues. In this paper, we propose a perspective on road safety adopted routing protocol for hybrid VANET-WSN communication (PRAVN) based on threefold contributions. The first contribution of PRAVN protocol is to perform clustering using improved water wave optimization (IWWO) algorithm. The second contribution is to compute the cluster head (CH) node of SN among multiple nodes based on multi-constraint features that are gathered from the SNs. Third contribution is to introduce the rider optimization (RO) algorithm for neighborhood selection, which provides the network lifetime and lossless connection. Finally, the PRAVN protocol used to forward correct information from source to destination that enhances the road safety. PRAVN protocol is implemented on network simulator (NS2) tool with the simulation of urban mobility (SUMO) for traffic generation. The simulation results prove that the proposed PRAVN protocol can outperform other conventional routing protocols in terms of packet delivery ratio, number of drops, throughput, routing overhead, energy consumption, end-to-to delay and network lifetime.

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Correspondence to Muthupandi Raja.

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Communicated by V. Loia.

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Raja, M. PRAVN: perspective on road safety adopted routing protocol for hybrid VANET-WSN communication using balanced clustering and optimal neighborhood selection. Soft Comput 25, 4053–4072 (2021). https://doi.org/10.1007/s00500-020-05432-3

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