VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing
| Autor: | Zhu Wang, Daqing Zhang, Junfeng Zhao, Yan Ding, Bin Guo, Chao Chen |
|---|---|
| Přispěvatelé: | Chongqing University [Chongqing], Northwestern Polytechnical University [Xi'an] (NPU), School of Electronics Engineering and Computer Science [Beijing] (EECS), Peking University [Beijing], Département Réseaux et Services Multimédia Mobiles (RS2M), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Institut Polytechnique de Paris (IP Paris), Algorithmes, Composants, Modèles Et Services pour l'informatique répartie (ACMES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP) |
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science Real-time computing 0211 other engineering and technologies Cloud computing 02 engineering and technology Tracing External Data Representation Trajectory mapping Trajectory compression [INFO]Computer Science [cs] Electrical and Electronic Engineering Android (operating system) Edge computing 021103 operations research Mobile edge computing business.industry Resource-constrained Global positioning system (GPS) devices Computer Science Applications Control and Systems Engineering Compression ratio Global Positioning System business Information Systems |
| Zdroj: | IEEE Systems Journal IEEE Systems Journal, IEEE, 2020, 14 (2), pp.1635-1646. ⟨10.1109/JSYST.2019.2935458⟩ |
| ISSN: | 2373-7816 1932-8184 1937-9234 |
| DOI: | 10.1109/jsyst.2019.2935458 |
| Popis: | Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. ${\sf VTracer}$ 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 concise and (near) spatial-lossless 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. ${\sf VTracer}$ meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the nearby 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 ${\sf VTracer}$ 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 ${\sf VTracer}$ achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|