Cluster-based target tracking in vehicular ad hoc networks
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Recently Vehicular Ad-hoc Networks (VANETs) have drawn the attention of academic and industry researchers due to their potential applications in enabling Intelligent Transportation System (ITS), including safe driving, entertainment, emergency response, and content sharing. Another potential application for VANET lies in vehicle tracking, where a tracking system is used to visually track a specific vehicle or to monitor a particular area. In this case, and in similar applications such as multimedia content sharing, a large volume of information is required to be transferred between vehicles, which can easily congest the wireless network in a VANET if not designed properly. The development of low-delay, low-overhead, and precise tracking system in VANET is a major challenge requiring novel techniques to guarantee performance and 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. In this thesis, we propose two clustering algorithms for vehicle tracking in VANETs. These algorithms provide a reliable and stable platform for tracking specific vehicles based on their visual features under various conditions. These algorithms have also been tested and evaluated in the context of vehicular tracking under various scenarios. Performance evaluation results demonstrate that the proposed schemes provide a more stable clustering structure with reduced overhead.