Using clustering for target tracking in vehicular ad hoc networks

Abstract

Vehicular Ad-hoc Networks (VANETs) have recently drawn the attention of academic and industry researchers due to their potential applications in enabling various Intelligent Transportation Systems (ITS) applications for safety, entertainment, emergency response, and content sharing. Another potential application for VANETs lies in vehicle tracking, where a tracking system is used to visually track a specific vehicle or to monitor a particular area. For such applications, a large volume of information is required to be transferred between certain vehicles and a command and control centre, which can easily congest the wireless network in a VANET if not designed properly. The development of low-delay, low-overhead, and precise tracking systems in VANET is a major challenge requiring novel techniques to guarantee performance and to reduce network congestion. Among the several proposed data dissemination and management methods implemented in VANETs, clustering has been used to reduce data propagation traffic and to facilitate network management. However, clustering for target tracking in VANETs is still a challenge due to the dynamic nature of such networks. We have proposed two cluster-based algorithms for target tracking in VANETs in our previous works [1,2]. These algorithms provide a reliable and stable platform for tracking a vehicle based on its visual features. In this paper, we have demonstrated the performance evaluation and testing results of both of our algorithms in the context of vehicular tracking under various scenarios. We have also compared the performance of both our algorithms to assess the performance of distributed algorithms as compared to centralized cluster-based target tracking algorithms. Besides, we have tested two data dissemination techniques for information delivery. Performance evaluation results demonstrate clearly that the proposed clustering schemes provide better performance for target tracking applications as compared to other cluster-based algorithms.