# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 5 Discussion  
In this section, the most important features of the proposed protocol G-OLSR are discussed and compared to the classic version of the OLSR protocol. First, some works related to evaluate and compare the routing protocols are exposed. Secondly, the four studied routing protocols are presented. Next, the system used to evaluate the protocols is described. Then, simulation results are analyzed and discussed. Based on the comparative study, we classify the protocols according to their transmission type, metrics, advantages, and disadvantages. This analysis allows us to choose OLSR, because it outperforms other protocols according to its End-to-End Delay and Packet Delivery Ratio, especially in the case of high mobility. In addition, we note that the Greedy forwarding mechanism (GF) is more adapted to the VANET environment and gives a success rate nearly close to wired networks in the case of low mobility, and we note that GF is not efficient in the case of the stationary node. If the GF selects as a relay the node where the speed is less than other candidate node, then it can be suitable to reach the destination as quickly as possible. Therefore, we propose the Advanced Greedy Forwarding, which is an amelioration of the GF in order to overcome the problems previously cited, especially in the case of high mobility. The GFA mechanism is used to collect the information of topology for the current segment (delimited by the source and destination intersections). This information is used to compute the score that can help overcome the problem of end-to-end delay in the case of high mobility. Therefore, we choose the right segment to reach the destination as fast as possible. The classic greedy forwarding does not take into account if the vehicle is moving or not. Knowing that the furthest node will not necessarily be the fastest and best for the transfer, to accelerate the communication, we add the speed of the vehicle as a parameter to compute the score in our amelioration. In order to validate GFA, we compare it with GF in several possible cases. We consider a simple network where we have just four nodes, the source S, the destination D, and two candidates nodes A and B, in the range of the source. We vary the distances between source and candidate nodes (xa and xb) and then we vary the speed of candidate nodes (Va and Vb). In all simulation cases, the GFA mechanism keeps the advantages of GF and solves the problem of end-to-end delay divergence noted in the GF mechanism. This divergence is caused by the stationary vehicle, because GFA considers the speed of each vehicle and prioritizes the mobile vehicles instead of stationary ones. We also note that, when the distance between candidate nodes exceeds a threshold, the speed of candidate nodes has a significant influence and GFA can choose the closer node instead of the farthest because it can be the fastest to reach destination according to the score \(t_{p}\) calculated based on the speeds and positions of candidates nodes. Therefore, we note that GFA reacts better than GF because of the score \(t_{p}\), especially when the score of the closer vehicle is less than that of the further one. GFA and GF have the same result, if the difference between the speeds of candidate nodes does not influence the score \(t_{p}\) in such a way that the score of the furthest node is lower than that of another closer node. In G-OLSR, we suppose that the road delimited by two junctions, source and candidate, is divided into several zones defined by the range of vehicles that is equal to 250 m. In each zone, we adopt the GFA method previously proposed to collect information such as density, direction, and all information that is used in the OLSR score. Therefore, we compute the cost of the segment and we apply the Dijkstra algorithm to reach the destination. The simulation results show that our proposal gives the best performance in terms of packet delivery ratio. The PDR of G-OLSR increases more and more as the traffic increases.