An adaptive node density estimation in vanets using received signal power

Abstract

Accurately estimating node density in Vehicular Ad hoc Networks (VANETs) is a challenging and crucial task. Various approaches exist yet none are able to deliver an accurate estimation in all situations especially in presence of collision in highly saturated networks. This paper describes an adaptive framework that allows individual nodes to estimate the real-time node density of their surrounding network. Our solution performs well in all situations even in chaotic channel conditions by retrieving information from collisions. This provides every vehicle on the road with two equations indicating the relation between 1) received signal strength versus simultaneously transmitting nodes, and 2) the relation between simultaneously transmitting nodes versus node density. These equations and frequently sampled received power levels enable individual nodes to estimate their real-time surrounding node density. Our proposal adapts to different environments and can address fast changes in configuration of the network and property of the channel in VANETs. Our experimental results indicate that the method yields reliable estimation in an acceptable time frame.

Multi-hop broadcast message dissemination in vehicular ad hoc networks: A security perspective review

“…There are several vehicle density estimation techniques proposed in the literature which can be used in VANETs scenarios. For instance, in Khomami et al 74 the relationship between the number of nodes present in a scenario and the number of nodes transmitting simultaneously was studied. Each individual node monitors the wireless channel and records the RSS values of several consecutive packets.…”

“…The scenario modeling approach could be used to validate information received from potential relay vehicles, in particular, statistical methods for vehicle density estimation (e.g. Khomami et al 74 and Shirani et al 86 ). These methods use parameters such as the mobility pattern of vehicles or signal propagation modeling to validate if the received data are reliable.…”