A Novel Clustering Algorithm Based on Agent Technology for VANET

Abstract

Vehicular Ad-hoc Network (VANET) is a sub-family of Mobile Ad-hoc Network (MANET).The means goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road, varying node density and locations of vehicles existing in this road. That's why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs. But most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another (such as traffic congestion, looking for free parking space, etc.). In this paper, we propose a novel clustering algorithm based on agent technology to solve the problems mentioned above and improve routing in VANET. Experimental part show promising results regarding the adoption of the proposed approach.www.macrothink.org/npa 1 Network Protocols and Algorithms

Destination Based Stable Clustering Algorithm and Routing for VANET

“…5) List of interests. Each vehicle has its own interests ( Figure 2) depending on the interests and wishes of the drivers of the transport system and passengers [1]. Interests may differ as interests about the availability of nearby places like gas stations, parking spaces, cafes and restaurants.…”

“…I use a vector to represent the interests of the vehicle and "K" the vector of interests of each vehicle [1] [9] in the form:…”

“…1) The first step, each vehicle from its on-board devices processes the data of B. Azat, T. Hong Journal of Computer and Communications the clustering parameter (Figure 3). Here LI-data from the list of interests presented in the form of a vector as [1,1,0], which can indicate certain (separate for each) interests of each transport. For example: this vehicle is interested in information about traffic accidents and traffic jams, but is not interested in information about the presence of specific places such as a gas station, restaurants and shops etc.…”

“…In the paper, presented effective solutions for stable efficient communication between vehicles in VANET: 1) A stable clustering algorithm based on the destination, which contextually takes into consideration the important parameters, for formation of the cluster, such as: "location" and "destination" of the vehicle, the "direction" of the vehicle and the "speed" of the vehicle, in addition a possible list of interests of each vehicle [1]. Based on these key parameters necessary for clustering, we know that means the list of interests of each individual vehicles will unit (group) them in a cluster with similar interests, and this means that each member of the clusters will have supposedly similar common interests within the cluster, such as: interest in some information, about the presence of parking lots, accident notifications, information about traffic jams, etc.…”

HSDN‐GRA: A hybrid software‐defined networking‐based geographic routing protocol with multi‐agent approach

“…The agent of MA‐DSDV has a knowledge base and four components: an agent identifier, a program agent to act autonomously, a memory agent that contains all the variables, and a routing table agent. They recently proposed in Harrabi, Jaffar et al 23 to combine the multi‐agent system approach and the Particle Swarm Optimization (PSO) algorithm named PSO‐C‐MA‐DSDV for a selective destination routing protocol by clustering the nodes according to the interest or context information of the vehicles as described in Harrabi et al 32 . They applied the PSO algorithm with a set of positions and velocities as particles.…”