# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 4 Optimization-Based Multiple Objective
### Configuration Optimization Architecture  
The proposed framework model is comprised of three stages: The network simulation with different urban scenarios, communication cost function, and PSO optimization algorithm. These procedures generate optimal configurations of OLSR protocol for a given VANET scenario. In each urban scenario, all mobile nodes are equipped with IEEE 802.11p network interface card which give each node the ability needed to send and receive messages according to Wi-Fi constraints. This ability is conditional by several factors such as limitation of distance, bandwidth availability, link life time, directions, and the varying speed of vehicles. This is because of the routing protocol discovers the path through the vehicles based on the network topology, which build it by periodically broadcasting control messages. These messages are used to update and maintain the network topology information within specific period time. In such VANET environments, this time is not compatible with routing configuration parameter values defined in (Table 1. OLSR RFC 3626 Configurations), which lead to frequent link breakage and flooding more packets to detect the network topology, and the network topology information is often outdated. Thus, computing an optimal configuration for the parameters of this protocol is crucial before developing any routing protocol for VANET. To build route successfully, OLSR uses eight different configuration parameters. These parameters represent the time for gathering neighbors information, processing, calculating and select routes. OLSR selects routes based on the principle of the information collection about the global state of the network which represent the critical issues in the VANETs. To ensure more reliability, and stability in the selected paths, relaying messages between nodes in the network and selecting shortest path should be smoother. For this reason, this work is seeking to tune the routing parameters of OLSR protocol efficiently. We evaluate and analyze the global simulation result of different urban scenarios by the performance index model, which provide the information of global communication. These information are used to obtain the communication cost for each performance metric. The proposed model index aggregates the global network information of the routing connectivity in terms of the amount of information successfully received in a time unit (Throughput), the number of packets received by destination nodes to the number of packets sent by the source node (PDR), the number of forwarded packets divided by number of data packets received (NRL), and the ratio of time difference between every packet sent and received to the total time difference over the total number of packets received (E2 ED). Figure 2 describes the main components of the framework model. In order to save network bandwidth, and improve the transmission ability of the network, the proposed equation is made to reflect correctly the deviating values of response metrics from its respective performance metric bound. The performance metrics values are placed in the equation in the form of numerator and denominator to maximize and minimize the performance metric. Additionally, the deviating values obtained from the equation reflects the balance of the trade-off between the QoS requirement and the efficiency of routing, these values plays an important role in balancing the weight of communication costs. The PSO algorithm calculates the optimal fitness value using the communication cost obtained from cost function. The communication cost is associated with each particle which affects the behavior of particles to find the global fitness value of a single parameter at a time. These values represent the new value of BOLSR-PSO configuration parameters. This process continues to happen once a new communication cost is calculated until an optimal global fitness value is obtained for each parameter. The new values rising from this process are the configuration parameters of BOLSR-PSO (Table 2).