Abstract
The rapid development of the Internet of Things is affecting the requirements towards wireless vision sensor networks (WVSN). Future smart camera architectures require battery-operated devices to facilitate deployment for scenarios such as industrial monitoring, environmental monitoring and smart city, consequently imposing constraints on the node energy consumption. This paper provides an analysis of the inter-effects between computation and communication energy for a smart camera node. Based on a people counting scenario, we evaluate the trade-off for the node energy consumption with different processing configurations of the image processing tasks, and several communication technologies. The results indicate that the optimal partition between the smart camera node and remote processing is with background modelling, segmentation, morphology and binary compression implemented in the smart camera, supported by Bluetooth Low Energy (BLE) version 5 technologies. The comparative assessment of these results with other implementation scenarios underlines the energy efficiency of this approach. This work changes pre-conceptions regarding design space exploration in WVSN, motivating further investigation regarding the inclusion of intermediate processing layers between the node and the cloud to interlace low-power configurations of communication and processing architectures.
Highlights
Scenarios such as industrial monitoring [1], environmental monitoring [2], and smart city [3] have to a wide extent changed the constraints towards wireless vision sensor networks (WVSN), requiring several cameras to cover large areas, while performing image-processing and communication tasks with real-time performance
We provide an analysis of the energy efficiency of the smart camera node evaluating the trade-off in energy consumption for different allocations of image-processing tasks between the smart camera node and the cloud
A WVSN consists of a set of tasks, where a specific task ti is not bound to a specific geographical location; as such, it can be mapped to either the smart camera node or the cloud
Summary
Scenarios such as industrial monitoring [1], environmental monitoring [2], and smart city [3] have to a wide extent changed the constraints towards wireless vision sensor networks (WVSN), requiring several cameras to cover large areas, while performing image-processing and communication tasks with real-time performance. Looking back at the evolution of camera-based networks, many changes have been introduced through the years in terms of design and architecture. Such systems consisted mainly of CCTV cameras connected either to monitors for constant visual inspection, or stored to a memory device. Energy efficiency in the smart camera node is a twofold problem, highly influenced by the allocation of imageprocessing tasks. There are different communication technologies alternating in terms of energy efficiency and delay for
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