VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. <inline-formula><tex-math notation="LaTeX">${\sf VTracer}$</tex-math></inline-formula> is a cost-effective mobile system for online trajectory compression and tracing vehicles, taking the streaming GPS data as inputs. Online trajectory compression, which seeks a <italic>concise and (near) spatial-lossless</italic> data representation before revealing the next vehicle’s GPS position, is gradually becoming a promising way to alleviate burdens such as communication bandwidth, storing, and cloud computing. In general, an accurate online map-matcher is a prerequisite. This two-phase approach is nontrivial because we need to overcome the essential contradiction caused by the resource-constrained GPS devices and the heavy computation tasks. <inline-formula><tex-math notation="LaTeX">${\sf VTracer}$</tex-math></inline-formula> meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the <italic>nearby</italic> smartphones of drivers (i.e., smartphones play the role of cloudlets), which are almost idle during driving. More importantly, they have relatively more powerful computing capacity. We have implemented <inline-formula><tex-math notation="LaTeX">${\sf VTracer}$</tex-math></inline-formula> on the Android platform and evaluate it based on a real driving trace dataset generated in the city of Chongqing, China. Experimental results demonstrate that <inline-formula><tex-math notation="LaTeX">${\sf VTracer}$</tex-math></inline-formula> achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size.