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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 one 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, the authors 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.