A Quality-Oriented Data Collection Scheme in Vehicular Sensor Networks

Considerable research attention has been dedicated to vehicular sensor networks (VSNs) because of its great potential in traffic monitoring. By taking advantage of sensors embedded in vehicles, a VSN harvests data while vehicles are traveling along the roads and then updates the collected data to the infrastructure to support the intelligent transportation system (ITS) applications. To meet the data collection requirements of different ITS applications, a huge number of update packets are generated, which may exhaust the available wireless communication bandwidth. To improve the efficiency of utilization of wireless bandwidth, in this study, we propose a quality-oriented data collection scheme, which aims to effectively support both the accuracy and real-time requirements stipulated by ITS applications while reducing communication overhead. We formulate a minimized communication overhead (MCO) problem and propose two algorithms, mixed-integer linear programming (WILY) and deviation-detection (DD), to solve the MCO problem. MILP can obtain the optimal solution by having all the data collected by every vehicle, while DD could achieve an efficient solution without this impractical assumption. We conducted extensive experiments by using SUMO to simulate vehicle traces in freeway and downtown environments. The experimental results have demonstrated the effectiveness of the proposed solutions.