Designing statistical multi-hop wireless broadcast protocols using confidence levels from stochastic models of reachability

Abstract

Multi-hop wireless broadcast is a critical component in ad-hoc wireless networks. Vehicular Ad-hoc Networks (VANET) in particular utilize broadcast as a primary communication mechanism in many applications. Because of their simplicity, statistical broadcasting methods have been adopted in several VANET broadcasting schemes. However, it is difficult to develop analytical models of these protocols, therefore no results are known proving reachability characteristics for them like there are for topological protocols such as Multi-Point Relaying (MPR).Therefore, we describe a technique to design statistical broadcasting protocols with threshold curves that give a target level of reachability with a desired confidence level. We model the distribution of reachability in a statistical protocol using the Pareto distribution which is then used to estimate the confidence with which a given threshold value will provide reachability exceeding the application-specific target.We apply this technique to develop a protocol based on the distance-to-mean method that meets a target reachability of 99% with 95% confidence. Evaluations using JiST/SWANS verify the protocol's performance.

Efficient multi‐hop wireless broadcast protocol in vehicular networks using automated threshold function design

“…Analysis in [33] shows a rough approximation of this phenomenon for the case of the distanceto-mean method ( Figure 1). Reachability here is defined as the fraction of nodes in the network that receive the broadcast message.…”

“…WiBDAT, a fast highlevel multi-hop broadcast simulation tool, was developed specifically for this purpose [34]. For reachability, we use the Pareto model described in [33] to calculate the probability that the threshold function will achieve a reachability level greater than 95%, called L. We set our application-required value for L also at 95% (meaning the application requires at least 95% of nodes receive broadcasts in 95% of trials). This makes the simulator fast and scalable, which is important when designing threshold functions because many simulation runs are required.…”

“…Applications requiring these performance characteristics include the broad class of general information dissemination, such as traffic flows, public advisories, weather, and commercial adver- Figure 1. Reachability versus threshold value in distance-to-mean method [33]. tisement.…”

“…When the evaluation is complete, we then measure reachability and transmit density and calculate an overall fitness. For reachability, we use the Pareto model described in [33] to calculate the probability that the threshold function will achieve a reachability level greater than 95%, called L. We set our application-required value for L also at 95% (meaning the application requires at least 95% of nodes receive broadcasts in 95% of trials). The transmit density, tx , is defined as the number of transmitting neighbors averaged across all nodes that receive the broadcast message.…”

Performance Improvement of Channel Quality Adaptive Protocol for Effective Routing in VANET

“…If the value is set too low then many nodes will be prevented from rebroadcasting. The value of D c should be decided in such a way that it should be adjustable to the varying node densities [3]. At low node densities, D c must be lower to allow the message to get propagated to the entire network.…”