Abstract
In vehicular multimedia sensor networks vehicles are equipped with cameras and they continuously capture images from urban streets. Then, vehicles can use roadside wireless access points encountered during travel to deliver recorded image data to remote data collectors, in which the information from several multimedia streams is aggregated and processed to enable new services, such as urban surveillance, or traffic and road monitoring. However, due to constraints on the wireless access network the amount of image data that can be transferred from vehicles is limited, and data redundancy should be avoided. In this paper we address this issue by using submodular optimization techniques to develop an efficient data collection algorithm capable of providing data redundancy elimination under network capacity constraints. We also design an alternative decentralized scheme that operates on longer time scales and relies only on basic aggregate information. We use network simulations with realistic vehicular mobility patterns to verify the performance gains of our proposed schemes compared to a baseline system that ignores data redundancy. Simulation results show that our data collection techniques can ensure a more accurate coverage of the road network while significantly reducing the amount of transferred data.