Towards an Edge Intelligence-Based Traffic Monitoring System
IEEE International Conference on Systems, Man and Cybernetics(2024)
摘要
Cities have undergone significant changes due to the rapid increase in urban
population, heightened demand for resources, and growing concerns over climate
change. To address these challenges, digital transformation has become a
necessity. Recent advancements in Artificial Intelligence (AI) and sensing
techniques, such as synthetic sensing, can elevate Digital Twins (DTs) from
digital copies of physical objects to effective and efficient platforms for
data collection and in-situ processing. In such a scenario, this paper presents
a compre-hensive approach for developing a Traffic Monitoring System (TMS)
based on Edge Intelligence (EI), specifically designed for smart cities. Our
approach prioritizes the placement of intelligence as close as possible to data
sources, and leverages an "opportunistic" interpretation of DT (ODT), resulting
in a novel and interdisciplinary strategy to re-engineering large-scale
distributed smart systems. The preliminary results of the proposed system have
shown that moving computation to the edge of the network provides several
benefits, including (i) enhanced inference performance, (ii) reduced bandwidth
and power consumption, (iii) and decreased latencies with respect to the
classic cloud -centric approach.
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关键词
Traffic System,Traffic Monitoring System,Intelligence,Power Consumption,Distribution System,Smart City,Centralized Approach,Digital Transformation,Digital Twin,Increase In Urban Population,Machine Learning,Deep Learning,Accuracy Of Model,Cloud Computing,Internet Of Things,Real-time Data,System Architecture,Traffic Congestion,Traffic Flow,Edge Computing,Hardware Components,Vehicle Detection,Edge Devices,Edge Layer,Bandwidth Consumption,Camera Module,Digital Space,Software Components,Amazon Web Services,Reduce Processing Time
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