Abstract
Vehicular Ad hoc Network (VANET) is a sub-family of Mobile Ad hoc Network (MANET). The principal 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 and varying node density 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. In this paper, we propose a novel clustering algorithm based on agent technology to improve routing in VANET.
Highlights
Despite that Vehicular Ad hoc Network (VANET) is considered as a subclass of Mobile Ad hoc Network (MANET), it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road and varying node density of vehicles existing in this road
Despite the fact that VANET is a specific application of MANET, several studies have demonstrated that routing protocols designed for MANET are inadequate and have poor performance if they are used without any modification or improvement [6]
VANETs possess exceptional network features that distinguish them from MANETs such as highly dynamic topology
Summary
With the wireless technology becoming pervasive and cheap, the majority of vehicles have information concerning the outside environment constraints such as the map of the regions to be visited and the density of traffic on the road, etc. All this precious information can be exploited by the vehicles to get an adaptive decision about the choice of a suitable path. To ensure uninterrupted route, the vehicles must be intelligent to be suitable for network environment and take autonomously the best decision when unexpected problem occurs such as topology changing, accident, traffic jam, varying in density of nodes based on the available information.
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