Adaptive Online Energy Saving for Heterogeneous Sensor Networks

Sensor nodes usually work under dynamic changing, hard-to-predict environments and have limited lifetime. We use a novel adaptive online energy saving (AOES) algorithm to save total energy consumption for heterogeneous sensor networks. Due to the uncertainties in execution time of some tasks and multiple working mode of each node, this paper models each varied execution time as a probabilistic random variable to save energy by selecting the best mode assignment for each node, which is called Mode Assignment with Probability (MAP) problem. We propose an optimal sub-algorithm MAP Opt to minimize the total energy consumption while satisfying the timing constraint with a guaranteed confidence probability. The experimental results show that our approach achieves significant energy saving than previous work.