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Edge Intelligence for Rendering Green Camera-Network-as-a-Service
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AI
DOI：10.1109/TGCN.2021.3126276.png)
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Abstract
En 中文
This work proposes the Green Camera-Network-as-a-Service (G-CNaaS) architecture, which provides on-demand camera networks to multiple end-users simultaneously while utilizing minimal energy. G-CNaaS simultaneously reduces the carbon footprint and eliminates the single application-centric approach of traditional camera networks (TCNs) by enabling each camera to participate in multiple Virtual-Camera-Networks (VCNs) and selecting an optimal set of cameras for each VCN. We couple each camera node in every VCNs with a learning model suitable for the requested application. We assign an intelligent edge device to each VCN to analyze time-sensitive events. We introduce the camera selection factor by leveraging the properties of cameras: 1) field-of-view (FoV); 2) angular-distance; 3) observation range; and 4) residual energy to select the optimal camera set. The results of the extensive simulation of the G-CNaaS architecture show that it excels in performance concerning attributes such as the average lifetime, fair distribution of the work among the camera owners, and cost-effectiveness compared to the TCNs. We observe that the expenditure of a user using the TCN varies by 88.7%, while in the case of G-CNaaS, the expenditure varies by 10.28% with the increase in time from 1-60 months. On the other hand, the average energy consumed increases by 59.88% and 99.5% in the presence of 10 and 20 camera sensor owners.
Keywords:
Cameras
Sensors
Streaming media
Carbon footprint
Surveillance
Virtualization
Real-time systems
Edge intelligence
green computing
virtual camera network
network services
virtualization
cost effectiveness
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Citations:
4.3K