Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks

TLDR

Wireless sensor networks increasingly rely on harvested environmental energy to extend lifetime, but energy sources vary with weather, making adaptive duty‑cycling essential; existing methods are minimally adaptive and assume known energy profiles. The study aims to design an adaptive duty‑cycling mechanism that keeps sensor nodes energy‑neutral amid variable environmental energy, and introduces a new adaptive control–based technique to outperform prior methods. The authors employ adaptive control theory to devise a tunable duty‑cycling algorithm that reduces duty‑cycle variance over time and outperforms earlier approaches across diverse energy‑source datasets. The new adaptive control method achieves up to two‑thirds reduction in duty‑cycle variance while preserving task performance and energy‑neutral operation, outperforming prior techniques especially in high‑variance environments.

Abstract

Increasingly many wireless sensor network deployments are using harvested environmental energy to extend system lifetime. Because the temporal profiles of such energy sources exhibit great variability due to dynamic weather patterns, an important problem is designing an adaptive duty-cycling mechanism that allows sensor nodes to maintain their power supply at sufficient levels (energy neutral operation) by adapting to changing environmental conditions. Existing techniques to address this problem are minimally adaptive and assume a priori knowledge of the energy profile. While such approaches are reasonable in environments that exhibit low variance, we find that it is highly inefficient in more variable scenarios. We introduce a new technique for solving this problem based on results from adaptive control theory and show that we achieve better performance than previous approaches on a broader class of energy source data sets. Additionally, we include a tunable mechanism for reducing the variance of the node's duty cycle over time, which is an important feature in tasks such as event monitoring. We obtain reductions in variance as great as two-thirds without compromising task performance or ability to maintain energy neutral operation.