Compressive data aggregation on mobile wireless sensor networks for sensing in bike races

This paper presents an efficient approach for collecting data in mobile wireless sensor networks which is specifically designed to gather real-time information of bikers in a bike race. The approach employs the recent HIKOB sensors for tracking the GPS position of each bike and the problem herein addressed is to transmit this information to a collector for visualization or other processing. Our approach exploits the inherent correlation between biker motions and aggregates GPS data at sensors using compressive sensing (CS) techniques. We enforce, instead of the standard signal sparsity, a spatial sparsity prior on biker motion because of the grouping behavior (peloton) in bike races. The spatial sparsity is modeled by a graphical model and the CS-based data aggregation problem is solved using linear programming. Our approach, integrated in a multi-round opportunistic routing protocol, is validated on data generated by a bike race simulator using trajectories of motorbikes obtained from a real race, the Paris-Tours 2013.