Performance Evolution of Vehicular Ad-hoc Network concerning data latency

Abstract

Vehicular Ad Hoc Networks (VANETs) are ad hoc networks established among vehicles that have communication facilities. Vehicles compromise network nodes by acting as source, destination and router. VANET improves road safety through propagation of warning messages about potential obstacles among vehicles in the network and so improves safety. By turning cars into routers or nodes, allows cars approximately 100 to 300 meters of each other to connect and, in turn, creates a network with a wide range. VANET protocols maintains route by dropping out of range cars in term of signal strength and add ones that have good signal. VANET can be viewed as component of the Intelligent Transportation Systems (ITS). VANET has mobile nodes On Board Units (OBUs) and static nodes Road Side Units (RSUs). The former resembles mobile network module and the later is central processing unit for on-board sensors and warning devices. QoS parameter in vehicular ad-hoc network is difficult due to network topology changes with high mobility and the available state information for routing is inherently imprecise. QoS Security is provided by authentication, encryption etc. Wirelesses access in Vehicular Environment (WAVE), defined in IEEE 1609.x family of standards. Transmission technology for (ITS) is classified into two categories, Vehicle-to-Infrastructure communications (V2I) and Vehicle-to-Vehicle communications (V2V). V2V are achieved by using effective routing protocol that considers the specific characteristic of the road information, relative car movements and application restriction. Simulation and implementation iv phase of the research with the aid of MATLAB shows Latency big deal as dominant criteria in measuring quality of service in networks performance as well it considered as safety guard. Latency depends of many types of delays; however three of them are considered here, namely propagation delay, Transmission delay and queuing delay. Transmission, propagation, and queuing delay put the average latency in a network onto distinct behaviors according to packet size. Due to computational and time constraints, were not able to fully explore all communication density metric. We used high performance computing to achieve a complex network model, using MATLAB GUI for simulation and in approximately realistic timescale. Behaviors found may not be fully present in urban environments, which intend to study in future work. Furthermore, even in in highway environments the vehicle-to-vehicle channel may show a dependence on vehicular density which is not incorporated in the two ray propagation model used.