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-This paper presents an efficient approach to data collection in mobilewireless sensor networks, with the specific application of sensing in bikeraces. Recent sensor technology permits to track GPS position of each bike.Because of the inherent correlation between bike positions in a bike race, asimple GPS log is inefficient. The idea presented in this work is to aggregateGPS data at sensors using compressive sensing techniques. We enforce, inaddition to signal sparsity, a spatial prior on biker motion because of thegroup behaviour (peloton) in bike races. The spatial prior is modeled by agraphical model and the data aggregation problem is solved, with both thesparsity and the spatial prior, by belief propagation. We validate our approachon a bike race simulator using trajectories of motorbikes in a real bike race,the "crit{\'e}rium du dauphin{\'e} 20XX" .

Compressive Data Aggregation on Mobile Wireless Sensor Networks for Sensing in Bike Races