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Smart city article details

Title	Watchdog: Real-Time Vehicle Tracking On Geo-Distributed Edge Nodes
ID_Doc	61474
Authors	Dong Z.; Lu Y.; Tong G.; Shu Y.; Wang S.; Shi W.
Year	2023
Published	ACM Transactions on Internet of Things, 4, 1
DOI	http://dx.doi.org/10.1145/3549551
Abstract	Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances in computer vision and machine-learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of computing budget, making them incompetent to adapt to time-varying and imbalanced tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system that fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy tradeoffs, and decomposes and schedules tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time-bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, achieving exceptional tracking performance with a real-time guarantee. © 2023 Association for Computing Machinery.
Author Keywords	Additional Key Words and PhrasesEdge computing; neural networks; real-time system; road network

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