Information propagation in traffic monitoring sensor networks

• Main Author: Skordylis, Antonios
• Other Authors: Trigoni, Niki
• Published: University of Oxford 2009
• Subjects: 388.31
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558548

This work investigates the problem of efficiently monitoring and disseminating road traffic information in urban settings using fixed and mobile sensor networks. A key challenge in outdoor urban environments is that bandwidth is a scarce resource. It is thus vital to reduce the communication cost of forwarding traffic data from source sensor nodes through the wireless network to the traffic monitoring center. This thesis proposes two distinct approaches to reducing the communication cost of traffic monitoring: 1) in-network data reduction in the context of fixed sensor networks, and 2) efficient data acquisition and routing in the context of mobile sensor networks. In fixed sensor networks, nodes are deployed in fixed locations and are capable of monitoring local traffic at regular intervals. When users can tolerate long delays in traffic updates, we propose Fourier-based compression techniques that exploit spatio-temporal correlations in traffic data and reduce the cost of data delivery. When users require real-time traffic updates, we investigate the use of model-based approaches, in which sensor nodes use a model to predict traffic data, and only report data that deviates from the predicted values. Our evaluation of in-network reduction techniques for fixed sensor networks is based on a real traffic dataset derived from traffic monitoring sensors in the city of Cambridge, UK. In mobile sensor networks, we utilize traveling vehicles as nodes that can sense local traffic and forward it to the monitoring center. The key challenge in vehicular networks is to minimize the communication cost of traffic monitoring by jointly optimizing the processes of data acquisition and routing. Given user requirements for data freshness, we devise a traffic data acquisition scheme, and propose two routing algorithms, D-Greedy and D-MinCost, that carefully alternate between the multi- hop forwarding and data muling strategies. The proposed algorithms are compared with existing approaches in a simulation environment using realistic vehicular traces from the city of Zurich.