QuARES: Quality-aware data collection in energy harvesting sensor networks

Renewable energy technology has become a promising solution to reduce energy concerns due to limited battery in wireless sensor networks. While this enables us to prolong the lifetime of a sensor network (perpetually), unstable environmental energy sources bring challenges in the design of sustainable sensor networks. In this paper, we propose an adaptive energy harvesting management framework, QuARES, which exploits an application´s tolerance to quality degradation to adjust application quality based on energy harvesting conditions. The proposed framework consists of two stages: an offline stage which uses prediction of harvested energy to allocate energy budget for time slots; and an online stage to tackle the fluctuation in time-varying energy harvesting profile. We implemented the application and our framework in a network simulator, QualNet. In comparison with other approaches (e.g.,), our system offers improved sustainability (low energy consumption, no node deaths) during operation with data quality improvement ranging from 30-70%. QuARES is currently being deployed in a campus-wide pervasive space at UCI called Responsphere.