Abstract-A key problem in sensor networks equipped with renewable energy sources is deciding how do you allocate energy to various tasks (sensing, communication etc.) over time so that the deployed network continues to gather highquality data. The state-of-the-art energy allocation algorithm (Progressive Filling) takes into account current battery level and harvesting energy and fairly allocates as much energy as possible along the time dimension. In this paper we show that by not considering application-context this approach leads to very high and uniform sampling rates. However, sampling the environment at fixed predefined intervals is neither possible (need to accommodate system failures) nor desirable (sampling rate might not capture an important event with desired fidelity). To that end, in this paper we propose a novel interactive power management technique that adapts sampling rate as a function of both application-level context (e.g., user request) and system-level context (e.g harvesting energy availability). We vary several key parameters including application request patterns, geographic locations, time slot length, battery end point voltage and evaluate the performance of our approach in terms of energy efficiency and accuracy. Our simulations use sensor data and system specifications (battery and solar panel specs, sensing and communication costs) from a real sensor network deployment. Our results show that the proposed approach saves significant amounts of energy by avoiding oversampling when application does not need it and uses this saved energy to support sampling at high rates to capture event with necessary fidelity when needed. The computational complexity of our approach is lower ( O(n)) than the state-of-the-art non-interactive energy allocation algorithm (O(n 2 )). [29] and built environment [6] data at unprecedented spatiotemporal granularity. A key problem in sensor networks is deciding how do you allocate energy to various tasks (sensing, communication etc.) over time so that the deployed network continues to gather high-quality data. There has been an extensive research in the area of power management and resource allocation algorithms in sensor networks. However, for sensors equipped with renewable energy sources, the problem is new challenges arise. The current best known algorithm (Progressive Filling) determines fair energy allocation along time dimension in sensor networks with predictable as well as stochastic renewable energy inputs [10] . PF algorithm aims to achieve fair allocation of resources over time -allocate, as much as possible, the energy (resource in their context) in a uniform way. It has two implications, allocating as much energy as possible results in high frequency sampling and fair energy allocation over time results in sampling the environment at fixed rate. Our experience with real-world sensor network deployments in collaboration with limnologists and
Related work has been also reported in REF , leveraging contextual information (both user requests and harvesting energy availability) to intelligently adapt sensor sampling rate.
An Interactive Context-aware Power Management Technique for Optimizing Sensor Network Lifetime
SENSORNETS
Computer Science