Cluster Based Semantic Data Aggregation in VANETs

Abstract

Recently, we are witnessing increased interest in the research of Vehicular Ad-hoc Networks (VANETs). Due to the peculiar characteristics of VANETs, such as high speed, the unstable communication link, and network partitioning, information transfer becomes inevitably challenging. The main communication challenges in vehicle to vehicle communication is scalability, predictability and reliability. With increasing number of vehicles in highway congestion scenarios, the congestion application need to disseminate large amount of information over multiple hops to the control center. This challenge can be solved by reducing the data load through clustering and data aggregation. In this paper, we propose cluster based semantic data aggregation (CBSDA) protocol that divide the road into different segments based on the cluster-ID and aggregate the data in each cluster. The aggregation scheme is a lossy aggregation with maximum precision. CBSDA scheme stores the data using a data structure that consists of super cluster, cluster and cluster member (CM) nodes. CBSDA is proposed to adaptively adjust the number of super cluster nodes. Moreover, the CBSDA scheme consists of weighted deviation scheme that decides which data to be fused for aggregation. Additionally, the aggregation level is controlled based on the density of vehicles and channel busy ratio (CBR). Simulation results show that the CBSDA using weighted deviation decision scheme is able to quickly reduce the channel congestion and improve the data precision even in congested traffic scenarios.