journal article·2022Open access

A Robust Emergency Messages Routing Scheme for Urban VANETs

Mumtaz Ali Shah, Farrukh Zeeshan Khan, Ghulam Abbas

Computers, Materials & Continua· Volume 72· Issue 2

Abstract

Vehicular ad-hoc networks (VANETs) play an essential role in enhancing transport infrastructure by making vehicles intelligent and proficient in preventing traffic fatalities. Direction-based greedy protocols pick the next route vehicle for transmitting emergency messages (EMs) depending upon the present location of adjacent vehicles towards sink vehicles by using an optimal uni-directional road traffic approach. Nevertheless, such protocols suffer performance degradation by ignoring the moving directions of vehicles in bi-directional road traffic where topological changes happen continuously. Due to the high number of vehicles, it is essential to broadcast EMs to all vehicles to prevent traffic delays and collisions. A cluster-based EM transmitting technique is proposed in this paper. For urban VANETs, this paper pioneers the clustering of bi-directional road traffic for robust and efficient routing of EMs. In this regard, this paper introduces a routing protocol, namely, the bi-directional urban routing protocol (BURP). In addition to the paths and relative locations of vehicles, BURP takes account of the distance parameter by using the Hamming distance function to determine the direction of motion of vehicles and communicates EMs through the cluster head (CH). A modified k-medoids algorithm is presented for the clustering of bi-directional road traffic. A median method is presented for selecting CH to ensure the long-running of a cluster. Simulation results show that BURP provides enhanced throughput, a maximized packet delivery ratio, low energy consumption, and network delay relative to eminent routing protocols.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Vehicular Ad Hoc Networks (VANETs) · Mobile Ad Hoc Networks · Opportunistic and Delay-Tolerant Networks

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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