Journal of Ambient Intelligence and Humanized Computing | 2021

Link risk degree aided routing protocol based on weight gradient for health monitoring applications in vehicular ad-hoc networks

 
 
 

Abstract


Recently Vehicular Ad-hoc Network (VANET) has gained great exposure for its emerging applications and becoming an essential part of human daily life. VANET can broadcast safety and control messages to neighbors and intended destinations on time. But due to the frequent topology change and high mobility of vehicles imposes a major challenge in VANET communications. Designing an efficient routing algorithm for maintaining adequate network performance in health monitoring applications is the necessity of today’s life. Wireless technology and positioning system make geographic routing protocol a more suitable and effective solution for VANET applications. High-quality medical facilities and respond promptly needed to the patient in an emergency or disaster situation. Therefore, using efficient routing, a patient’s health can be monitored through communication between vehicles and the patient. Greedy Perimeter Stateless Routing protocol can be a suitable routing protocol that can help in best route selection in an emergency. The work presented in this paper analyzes the advantages and shortcomings of GPSR and proposes an improved link risk degree aided Greedy Perimeter Stateless Routing protocol based on Weight Gradient (GPSR-WG) protocol. GPSR-WG improves the greedy forwarding by considering multiple routing criteria such as direction, distance degree, link risk degree, and normalized speed factor. GPSR-WG implements the weight gradient on these criteria for selecting the next-hop node within the communication range to maximize the routing performance. We have evaluated the work mathematically and simulated it using the NS-3 network simulator. Simulation results have proved that the GPSR-WG protocol performs better when compared with GPSR, GPSR-M, and MM-GPSR in an urban vehicular environment.

Volume None
Pages 1-23
DOI 10.1007/S12652-021-03264-Z
Language English
Journal Journal of Ambient Intelligence and Humanized Computing

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