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Energy efficient video sensor networks for surveillance applications

University of British Columbia

Video sensor networks (VSNs) provide rich sensing information and coverage, both beneficial for applications requiring visual information such as smart homes, traffic control, healthcare systems and monitoring/surveillance systems. Since a VSN-based surveillance application is usually assumed to have limited resources, energy efficiency has become one of the most important design aspects of such networks. However, unlike common sensor network platforms, where power consumption mostly comes from the wireless transmission, the encoding process in a video sensor network contributes to a significant portion of the overall power consumption. There is a trade-off between encoding complexity and bitrate in a sense that in order to increase compression performance, i.e., achieve a lower bitrate, a more complex encoding process is necessary. The coding complexity and video bitrate determine the overall encoding and transmission power consumption of a VSN. Thus, choosing the right configuration and setting parameters that lead to optimal encoding performance is of primary importance for controlling power consumption in VSNs. The coding complexity and bitrate also depend on the video content complexity, as spatial details and high motion tend to lead to higher computation costs or increased bitrates. In a video surveillance network, each node captures an event from a different point of view, such that each captured video stream has unique spatial and temporal information. This thesis investigates the trade-off between encoding complexity and communication power consumption in a video surveillance network where the effect of video encoding parameters, content complexity, and network topology are taken into consideration. In order to take into account the effect of content complexity, we created a video surveillance dataset consisting of a large number of captured videos with different levels of spatial information and motion. Then, we design an algorithm that minimizes the video surveillance network’s power consumption for different scene settings. Models that estimate the coding complexity and bitrate were proposed. Finally, these models were used to minimize the video surveillance network’s power consumption and estimate the encoding parameters per each node that yield the minimum possible power consumption for the entire network.

Text

2016-08-19

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58889

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/