QuARES: A quality-aware renewable energy-driven sensing framework

Renewable energy technologies have become a promising solution to reduce energy concerns that arise due to limited battery in wireless sensor networks. While this enables us to prolong the lifetime of a sensor network (perpetually), the realization of sustainable sensor platforms is challenging due to the unstable nature of environmental energy sources. In this paper, we propose an adaptive energy harvesting management framework, QuARES, which exploits an application's tolerance to quality degradation to adjust data collection quality based on energy harvesting conditions. The proposed framework consists of two phases: an offline phase which uses prediction of harvested energy to allocate energy budget for time slots; and an online phase to tackle fluctuations in the time-varying energy harvesting profile. We formulate the energy budget allocation problem as a linear programming problem and implemented heuristics to minimize error in data quality at runtime. Our techniques are implemented in a network simulator, QualNet. In comparison with other approaches (e.g., [8]), our system offers improved sustainability (low energy consumption, no node deaths) during operation with data quality improvement ranging from 30 to 70%. QuARES is currently being deployed in a campus-wide pervasive space at UCI called Responsphere [11].