Energy-efficient markov chain-based duty cycling schemes for greener wireless sensor networks

Abstract

To extend the lifetime of a wireless sensor network, sensor nodes usually duty cycle between dormant and active states. Duty cycling schemes are often evaluated in terms of connection delay, connection duration, and duty cycle. In this article, we show with experiments on Sun SPOT sensors that duty cycling time (energy) efficiency, that is, the ratio of time (energy) employed in ancillary operations when switching from and into deep sleep mode, is an important performance metric too. We propose a novel randomized duty cycling scheme based on Markov chains with the goal of (i) reducing the connection delay, while maintaining a given time (energy) efficiency, or (ii) keeping a constant connection delay, while increasing the time (energy) efficiency. Analytical and experimental results demonstrate that the Markov chain-based scheme can improve the performance in terms of connection delay without affecting the time efficiency, or vice versa, as opposed to the trade-off observed in traditional schemes. We extend the proposed duty cycling scheme to a partially randomized scheme, where wireless nodes can switch into active state beyond their schedules when their neighbors are active to anticipate message forwarding. The analytical and experimental results confirm the relationship between connection delay and time efficiency also for this scheme.