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Top1. Introduction
Vehicle Ad-hoc Network one of the emerging technologies in the field of ITS (Intelligent Transport System). There are two classifications of this network: MANET and VANET. VANET plays an essential role in the area of ITS. A further category of VANET is V2V (Vehicle to Vehicle), V2I (Vehicle to infrastructure) and Hybrid V2I and V2V both. This research takes advantage of this communication. The main aim of VANET is to avoid the collision, share the traffic information and efficiently manage the available resource. VANET vehicles communicate with each other to share important information available. The vehicle is sharing information for communication with each other for solving the purpose of an intelligent transport system. Figure 1 shows the VANET architecture. It shows obvious how VANET communication takes place with the help of RSU, the Internet and OBU, how these devices communicate with each other.
Figure 1.
VANET Architecture (Kumar et al.2018)
Top2. Clustering
Clustering is a method of grouping the vehicle based on some predefined metrics such as velocity, density, and direction. Clustering is one of the control mechanisms in VANET. VANET is a MANET subclass, and many of the clustering technique is derived from MANET for VANET. Clustering in VANET has a highly dynamic topology that why most of the clustering algorithm is consider velocity and direction as an essential parameter for clustering. Clustering is a technique where each Cluster Head (CH) has a Cluster member (CM) and CG (cluster gateway). In clustering, each cluster member can become a cluster head (CH), but one can become a CH due to some condition in the algorithm.
2.1 PSO (Particle Swarm Optimization)
PSO is a heuristic algorithm for optimization. IT was expected to work with a nonlinear persistent optimization issue. It is a technique based upon swarm movement and intelligence enrage from the social behavior of birds gathering. Flocking can acquire the optimal result in this algorithm ever particle searches to scan the optimal solution with its speed. Every moving particle has an N-dimensional space that appropriately modifies its flying (Kumar et al., 2018).
The PSO algorithm can understand deeply by described following step below
• Randomly, instate the swarm with the level of the Earth heading confidential and independent location vectors.
• Providing a sensible vector of velocity to every practical available in network.
• Tap and make record of the fitness of each people.
• Verify the optimal implementation of molecules in the social market.
• Update velocity and position vectors as shown in (6) and (7) for each particle.
• Discrete the vector of the location.
• If any available iota flies beyond the potential arrangement space, return the molecule to its best possible and available solution before the time has come.
• Repeat step 1 − 7 until the minute the most unmistakable number of cycles has hit (Limbasiya et al., 2018).
3. Literature Review
After an initial overview of related work, we examine specific prior work related to the PSO algorithm used in VANET. There are many types of research works based on PSO. Some of them are reviewed in the literature.