Graph Optimized Data Offloading for Crowd-AI Hybrid Urban Tracking in Intelligent Transportation Systems

Urban tracking plays a vital role for people's urban life in intelligent transportation systems, e.g., public safety, case investigation, finding missing items, etc. However, the current tracking methods consume a large amount of communication and computing resources since they mainly offload a...

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Veröffentlicht in:IEEE transactions on intelligent transportation systems Jg. 24; H. 1; S. 1075 - 1087
Hauptverfasser: Wang, Pengfei, Pan, Yuzhu, Lin, Chi, Qi, Heng, Ren, Jiankang, Wang, Ning, Yu, Zhen, Zhou, Dongsheng, Zhang, Qiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial intelligence
Cameras
Costs
crowd-AI
data offloading
graph computing
Hybrid systems
Intelligent transportation systems
Public safety
Resource management
Sensors
Target tracking
task allocation
Task analysis
Tracking
Urban tracking
Videos
ISSN:1524-9050, 1558-0016
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Zusammenfassung:Urban tracking plays a vital role for people's urban life in intelligent transportation systems, e.g., public safety, case investigation, finding missing items, etc. However, the current tracking methods consume a large amount of communication and computing resources since they mainly offload all related sensing data, i.e., videos, generated by widely deployed cameras to the cloud where data are stored, processed, and analyzed. In this paper, we propose a graph optimized data offloading algorithm leveraging a crowd-AI hybrid method to minimize the data offloading cost and ensure the reliable urban tracking result. To be specific, we first formulate a crowd-AI hybrid urban tracking scenario, and prove the proposed data offloading problem in this scenario is NP-hard. Then, we solve it by decomposing the problem into two parts, i.e., trajectory prediction and task allocation. The trajectory prediction algorithm, leveraging the state graph, computes possible tracking areas of the target object, and the task allocation algorithm, using the dependency graph, chooses the optimal set of crowds and cameras to cover the tracking area while minimizing the data offloading cost separately. Finally, the extensive simulations with large real world data set are conducted showing that the proposed algorithm outperforms benchmarks in reducing data offloading cost while ensuring the tracking success rate in intelligent transportation systems.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3141885