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.