Energy-efficient sensor and task scheduling for extending battery life in a sensor node

Abstract

Wireless sensor nodes are becoming more and more common in various settings and require a long battery life for better maintainability. Since most sensor nodes are powered by batteries, energy efficiency of the sensor node became a critical problem. In an experiment, we observed that battery voltage drops quickly due to a high peak power consumption. When battery voltage dropped to the operation voltage, the sensor stops working even though some useful charge remains in the battery. We propose three off-line algorithms that extend battery life by scheduling sensors' execution time that is able to reduce peak power consumption as much as possible under a deadline constraint. Moreover, we present a DVFS-enabled periodical task execution algorithm and an execution time scaling periodical task execution algorithm to improve the total energy efficiency of the sensor node. We also simulated these sensor scheduling algorithms and the task scheduling algorithms to evaluate their effectiveness. The simulation results showed that one of the three sensor scheduling algorithms dramatically can extend battery life approximately three time as long as in simultaneous sensor activation, and the two task scheduling algorithms are more energy efficient compared with continuous task execution.