Energy Consumption Models for Smart Camera Networks
Camera networks require heavy visual data processing and high-bandwidth communication. In this paper, we identify key factors underpinning the development of resource-aware algorithms and we propose a comprehensive energy consumption model for the resources employed by smart camera networks, which are composed of cameras that process data locally and collaborate with their neighbors. We account for the main parameters that influence consumption when sensing (frame size and frame rate), processing (dynamic frequency scaling and task load), and communication (output power and bandwidth) are considered. Next, we define an abstraction based on clock frequency and duty cycle that accounts for active, idle, and sleep operational states. We demonstrate the importance of the proposed model for a multicamera tracking task and show how one may significantly reduce consumption with only minor performance degradation when choosing to operate with an appropriately reduced hardware capacity. Moreover, we quantify the dependency on local computation resources and bandwidth availability. The proposed consumption model can be easily adjusted to account for new platforms, thus providing a valuable tool for the design of resource-aware algorithms and further research in resource-aware camera networks.