Skip to Main content Skip to Navigation
New interface
Conference papers

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

Wei Du 1 Jean-Marie Gorce 1 Tanguy Risset 1 Matthieu Lauzier 2 Antoine Fraboulet 2 
1 SOCRATE - Software and Cognitive radio for telecommunications
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : This paper presents an efficient approach to data collection in mobile wireless sensor networks, with the specific application of sensing in bike races. Recent sensor technology permits to track GPS position of each bike. Because of the inherent correlation between bike positions in a bike race, a simple GPS log is inefficient. The idea presented in this work is to aggregate GPS data at sensors using compressive sensing techniques. We enforce, in addition to signal sparsity, a spatial prior on biker motion because of the group behaviour (peloton) in bike races. The spatial prior is modeled by a graphical model and the data aggregation problem is solved, with both the sparsity and the spatial prior, by belief propagation. We validate our approach on a bike race simulator using trajectories of motorbikes in a real bike race, the " critérium du dauphiné 20XX " .
Document type :
Conference papers
Complete list of metadata
Contributor : Jean-Marie Gorce Connect in order to contact the contributor
Submitted on : Thursday, November 24, 2016 - 3:41:56 PM
Last modification on : Wednesday, March 9, 2022 - 3:30:41 PM
Long-term archiving on: : Thursday, March 16, 2017 - 11:45:32 AM


Files produced by the author(s)


  • HAL Id : hal-01395629, version 1
  • ARXIV : 1611.08220



Wei Du, Jean-Marie Gorce, Tanguy Risset, Matthieu Lauzier, Antoine Fraboulet. Compressive Data Aggregation on Mobile Wireless Sensor Networks for Sensing in Bike Races. European Signal Processing Conference (EUSIPCO 2016), European Association for Signal Processing (EURASIP), Aug 2016, Budapest, Hungary. ⟨hal-01395629⟩



Record views


Files downloads