Model-Driven Traffic Data Acquisition in Vehicular Sensor Networks

In recent years, the global position system (GPS) is widely used in technical products, such as navigation devices, GPS loggers, PDAs and mobile phones. Hence, traffic data collection platforms are proposed to collect GPS data points for traffic monitoring. In traffic data collection platforms, each vehicle equips with GPS modules and the wireless communication interfaces, such as 3G or WiFi networks, and the GPS data sensed (e.g., the speed and the position) are sent to the server. One challenge issue is that if a significant number of vehicles upload their GPS data points at the same time, it is possible that the wireless network cannot offer enough network resources for simultaneous network connections. This paper proposes a framework MDC (standing for Model-based Data Collection) to reduce the amount of data transmission and the number of vehicles reporting their GPS data points. The MDC framework is executed at the server and vehicle side collaboratively. In the vehicle side, given a series of GPS data points, model functions are derived to represent the raw GPS data points. Hence, each vehicle could report some coefficients that describe its movements instead of reporting all position information. Since vehicles move along with road segments that are usually a set of line segments, algorithm LR (standing for Liner Regression) is proposed to determine a set of line functions to represent movements of vehicles. By observing the spatial-temporal locality in traffic data, algorithm KR (standing for Kernel Regression) is developed to derive a set of kernel functions to model a series of speed readings sensed. Moreover, with the spatial-temporal locality feature in traffic data, an in-network aggregation mechanism are proposed to determine a set of groups and for each group, only one vehicle needs to report traffic data, thereby further reducing the number of simultaneous connections. Experimental results show that MDC can collect traffic data effectively and the ef- - ficiently.