Vehdoop: A Scalable Analytical Processing Framework for Vehicular Sensor Networks

Abstract

The vehicular sensor network (VSN) technology empowers intelligent transportation systems (ITSs) to support a wide range of road safety and traffic management applications. By taking advantage of the information collection and communication capabilities offered by VSNs, information, such as speed, travel time, dash-camera video, and so on, can be gathered from sensors embedded in vehicles and then delivered to the infrastructure to support ITS applications. The explosive growth in the availability and variety of sensor instruments as well as the number of vehicles provides us with the opportunity to create large-scale ITS applications, which demand large-scale data processing. In order to support large-scale data processing, Google proposed the MapReduce framework. The MapReduce framework provides scalability in a large-scale data cluster by performing aggregate computations as close to the data source as possible. However, supporting ITS applications over VSN is not just a matter of simply applying the existing MapReduce framework to VSN due to the limited wireless bandwidth and the highly dynamic network topology. In this paper, we propose an analytical processing framework for VSNs called Vehdoop . Vehdoop utilizes the computing capability of vehicles to efficiently process sensor data in parallel across a large number of vehicles in a decentralized manner. We conducted extensive experiments using vehicle trajectories generated from Simulation of Urban MObility (SUMO) and a network simulator, NS-3, to simulate vehicle-to-vehicle and vehicle-to-infrastructure communications. The experimental results demonstrate the superiority of Vehdoop.