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Shao, Yiran (Shenzhen Institute of Advanced Technology, Chinese Academy of Sc), Chen, Zhenwu (Shenzhen Urban Transport Planning Center Co., Ltd.), Yongfeng,Zhen, Yongfeng (Shenzhen Shenzhentong Co., Ltd), Zhu, Fenghua (Institute of Automation, Chinese academy of sciences), Peng, Lei (Shenzhen Institute of Advanced Technology,Chinese Academy of Sci)

LSM TR-Tree: An Efficient Spatial-Temporal Index for Real-Time IoV Data Storage

Scheduled for presentation during the Regular Session "Data Management and Geographic Information Systems" (FrBT2), Friday, September 27, 2024, 15:10−15:30, Salon 5

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Keywords Data Management and Geographic Information Systems

Abstract

With the rapid development of Internet of Vehicles (IoV) technology, the number of smart vehicles and roadside units has increased dramatically,accelerating the rate of real-time data generation within the IoV environment. To optimize the storage efficiency of massive real-time IoV data, this paper proposes a new two-dimensional spatiotemporal index structure, named after LSM TR-Tree.As a spatial index, the TR-Tree not only determines the timing of MBR adjustments based on the real-time trajectory of vehicles, but also incrementally updates the index structure incrementally based on changes in the relative positions of vehicles, significantly enhancing the efficiency of spatial index reconstruction. By integrating the TR-Tree with the LSMTree, a complete spatiotemporal index structure is formed.This study employs the NGSIM US101 dataset and conducts experiments in AsterixDB. The results demonstrate that the LSM TR-Tree shows significant performance advantages in terms of writing efficiency and spatial query response compared to the single temporal index LSM-Tree and traditional spatiotemporal index LSM R-Tree, effectively proving the feasibility and advanced nature of the LSM TR-Tree in handling massive real-time IoV data.

 

 

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