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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

February 11, 2015

Speaker: Sanaz Khakpour, UOIT CS Graduate Student

Title: Cluster-Based Target Tracking in Vehicular Ad Hoc Networks

Abstract: 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.