Camera scheduling and energy allocation for lifetime maximization in user-centric visual sensor networks.
We explore camera scheduling and energy allocation strategies for lifetime optimization in image sensor networks. For the application scenarios that we consider, visual coverage over a monitored region is obtained by deploying wireless, battery-powered image sensors. Each sensor camera provides coverage over a part of the monitored region and a central processor coordinates the sensors in order to gather required visual data. For the purpose of maximizing the network operational lifetime, we consider two problems in this setting: a) camera scheduling, i.e., the selection, among available possibilities, of a set of cameras providing the desired coverage at each time instance, and b) energy allocation, i.e., the distribution of total available energy between the camera sensor nodes. We model the network lifetime as a stochastic random variable that depends upon the coverage geometry for the sensors and the distribution of data requests over the monitored region, two key characteristics that distinguish our problem from other wireless sensor network applications. By suitably abstracting this model of network lifetime and utilizing asymptotic analysis, we propose lifetime-maximizing camera scheduling and energy allocation strategies. The effectiveness of the proposed camera scheduling and energy allocation strategies is validated by simulations.
Yu
Chao
C
Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA. chyu@ece.rochester.edu
Sharma
Gaurav
G
D000465:Algorithms; D003195:Computer Communication Networks / Q000295:instrumentation*; D004735:Energy Transfer; D007089:Image Enhancement / Q000295:instrumentation*; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D010781:Photography / Q000295:instrumentation*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers