With reference to the MeSmart project, the Municipality of Messina is making a great investments to deploy several types of cameras and digital devices across the city for carrying out different tasks related to mobility management, such as traffic flow monitoring, number plate recognition, video surveillance etc. To this aim, exploiting specific devices for each task increases infrastructure and management costs, reducing flexibility. On the contrary, using general-purpose devices customized to accomplish multiple tasks at the same time can be a more efficient solution. Another important approach that can improve the efficiency of mobility services is moving computation tasks at the Edge of the managed system instead of in remote centralized serves, so reducing delays in event detection and processing and making the system more scalable. In this paper, we investigate the adoption of Edge devices connected to high-resolution cameras to create a general-purpose solution for performing different tasks. In particular, we use the Function as a Service (FaaS) paradigm to easily configure the Edge device and set up new services. The key results of our work is deploying versatile, scalable and adaptable systems able to respond to smart city’s needs, even if such needs change over time. We tested the proposed solution setting up a vehicle counting solution based on OpenCV, and automatically deploying necessary functions into an Edge device. From experimental results, it results that computing performance at the Edge overtakes the performance of a device specifically designed for vehicle counting under certain conditions and thanks to our reconfigurable functions.
MeSmart-Pro: Advanced Processing at the Edge for Smart Urban Monitoring and Reconfigurable Services
Gianluca Dini
Penultimo
;
2020-01-01
Abstract
With reference to the MeSmart project, the Municipality of Messina is making a great investments to deploy several types of cameras and digital devices across the city for carrying out different tasks related to mobility management, such as traffic flow monitoring, number plate recognition, video surveillance etc. To this aim, exploiting specific devices for each task increases infrastructure and management costs, reducing flexibility. On the contrary, using general-purpose devices customized to accomplish multiple tasks at the same time can be a more efficient solution. Another important approach that can improve the efficiency of mobility services is moving computation tasks at the Edge of the managed system instead of in remote centralized serves, so reducing delays in event detection and processing and making the system more scalable. In this paper, we investigate the adoption of Edge devices connected to high-resolution cameras to create a general-purpose solution for performing different tasks. In particular, we use the Function as a Service (FaaS) paradigm to easily configure the Edge device and set up new services. The key results of our work is deploying versatile, scalable and adaptable systems able to respond to smart city’s needs, even if such needs change over time. We tested the proposed solution setting up a vehicle counting solution based on OpenCV, and automatically deploying necessary functions into an Edge device. From experimental results, it results that computing performance at the Edge overtakes the performance of a device specifically designed for vehicle counting under certain conditions and thanks to our reconfigurable functions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
