With an increase in the capabilities of sensors, wireless visual sensor networks (WVSNs) are being researched to perform more complicated tasks as each visual sensor performs video capture, processing and sharing. Because each visual sensor operates by using its own resources including computation and communication under a limited power supply, it is necessary to develop an energy management scheme suitable for WVSNs. In particular, when multiple camera modules of visual sensors are aimed at some regions with different fields of views (FOVs), undesirable power consumption for encoding may occur among distributed visual sensors. Because some overlapped FOVs among captured images give rise to encoding redundancy, this leads to an increase in data quantity and power consumption for the encoding and communication. To resolve this problem, in this paper, we present a novel strategy for lifetime maximization of the WVSN. In order to estimate overlapped FOVs without using complicated procedures such as pattern or object recognition, we propose a geographical model to estimate overlapped FOVs based on location and visual direction. Based on this model, the power-rate-distortion (P-R-D) is determined and used to construct an optimization problem for minimizing the power consumption of each node. Through the proposed scheme, including distributed power allocation and node scheduling under simple information sharing, the network lifetime is maximized. Numerical results demonstrate the validity of the proposed algorithm.
|Number of pages||15|
|Journal||Digital Signal Processing: A Review Journal|
|Publication status||Published - 2016 Mar|
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF-2013R1A1A2A10011764 ).
© 2015 Elsevier Inc. All rights reserved.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Statistics, Probability and Uncertainty
- Computational Theory and Mathematics
- Electrical and Electronic Engineering
- Artificial Intelligence
- Applied Mathematics