An Adaptive Parallel Method for Indexing Transportation Moving Objects

Autor: Kun-lun Chen, Chuan-wen Li, Guang Lu, Jia-quan Li, Tong Zhang
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Complexity, Vol 2021 (2021)
Druh dokumentu: article
ISSN: 1076-2787
1099-0526
DOI: 10.1155/2021/6645778
Popis: Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects.
Databáze: Directory of Open Access Journals
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