TY - GEN
T1 - Towards an Edge Intelligence-Based Traffic Monitoring System
AU - Barbuto, Vincenzo
AU - Savaglio, Claudio
AU - Minerva, Roberto
AU - Crespi, Noel
AU - Fortino, Giancarlo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
U2 - 10.1109/SMC53992.2023.10393907
DO - 10.1109/SMC53992.2023.10393907
M3 - Conference contribution
AN - SCOPUS:85187262989
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3434
EP - 3439
BT - 2023 IEEE International Conference on Systems, Man, and Cybernetics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Y2 - 1 October 2023 through 4 October 2023
ER -
