Leveraging application context for efficient sensing

Today's platforms for long-term environmental monitoring (e.g. buoys or towers) typically host large solar panels and batteries. Ideally, miniaturized platforms could be used instead, so state of the art power management technique that takes into account battery levels and harvested energy to provide uniform sampling rate. However, the fixed pre-defined intervals is not desirable. The state-of-art adaptive sampling mechanism, optimal adaptive sampling algorithm (OSA) uses data uncertainty and past measurements to determine the optimal sampling rate at the cost of high computational complexity O(n3), thus draining the batteries even further. Even if the sampling were done optimally, there are still significant challenges with data transmission. The state of the art approach for determining optimal transmission policy offers limited control over the energy-delay tradeoff and is not suitable to support wide range of applications ranging from real-time and delay-tolerant. To address these challenges, we have developed a novel power management framework that adapts sampling and transmission rates based on battery level, energy harvesting level and application-context (e.g. characteristics of the gathered data). Our framework is optimal in terms of energy efficiency with low computational complexity. We evaluate the performance of the proposed framework using datasets from two real-world deployments. Our results show that our approach saves significant amounts of energy (between 20% to 60%) by avoiding oversampling when the application does not need it and uses this saved energy to support sampling at high rates to capture event with necessary fidelity when needed.