Exploiting Context Severity to Achieve Opportunistic Service Differentiation in Vehicular Ad hoc Networks

Abstract

Wireless Access in Vehicular Environment (WAVE) dictates primitives for intelligent transportation system (ITS) applications and services. The medium access control (MAC) layer in WAVE facilitates service differentiation. This offers quality of service (QoS) by prioritizing traffic through different access categories (ACs), which are based on application-requested priorities. However, it is oblivious to network load, to delay requirements for ITS safety applications, and to the “severity” of vehicles. In this paper, we propose a novel opportunistic service differentiation (OSD) scheme as an enhancement to WAVE. We define a fuzzy inference system (FIS) to deduce a context severity metric (CSM) that relates the driving behavior of a vehicle to its environment. Our OSD traffic distribution heuristic prioritizes vehicle traffic, with respect to CSM, and accounts for network load and link layer bounds. Furthermore, the OSD scheme guarantees that ACs do not exceed maximum allowable delay for ITS applications. Our methodology entails defining and designing a linear programming (LP) model for verification and validation of the OSD scheme. We perform a comparative study of OSD-enhanced WAVE and classical WAVE analytically and in a vehicular ad hoc network (VANET) simulator. We show that both simulation and analytical results substantiate our claim of improvement in performance of OSD-enhanced WAVE over classical WAVE.