Convex Hull and Vornoi Diagram Based Routing Protocol for Adhoc Network

Abstract

The MANET faces major challenges in design of routing protocol due to their highly dynamic topology, poses by mobility. The position based routing algorithm that does not depend on topological information is more suitable than other routing protocol for MANET. In previous position based routing protocol does not consider the traffic patterns of higher layer. In fact, the result of these algorithms and test of connectivity in the network layer level and the used to analyze position based algorithm that do not reflect the true behavior of MANET. In our proposed position based routing protocol, we have made modifications in LAR1 algorithm by changing the request zone region computations. The results of this algorithm are compared with the existing LAR1. The performance of the CHMFR protocols is measured in terms of throughput, success rate and power consumption for varying network size which is efficient as compared to LAR1. We have simulated CHMFR protocol using GloMoSim simulator.

Performance Analysis of Radio Propagation Models in VANET Application

“…This RPM induced oscillation by the constructive and destructive combination of the two rays which causes it not providing a good result for a short distance [17]. It gives more accurate prediction at a long distance but not for a short distance.…”

“…This RPM is considering both the direct path and ground reflection path. The two-ray ground reflection principle is generally a multipath radio propagation model that predicts path losses between a transmitting antenna and a receiving antenna when they are in line of sight (LOS) [1,17]. As a result, nodes are positioned on a plane as illustrated in Figure 5.…”

“…This model is also used to classify the statistics of signals transmitted over the multipath fading channel since it can flexibly model various fading conditions [13].The aim is to enable the wireless communication channel to be more closely represented, which could define various parameter values. Nakagami is considered as a stochastic [13] model which is a method for estimating probability distributions of possible results by allowing one or more inputs to differ randomly over time [13,17]. Nakagami distribution is applied to the power level and defined by the function of probability density as in Equation (3) with 'ⅿ' indicates the fading depth parameter and 'ω' as the average received power [13].…”