The Internet of Things and wearables are gaining much popularity recently. However, frequent recharging and battery replacement disrupt the applications of these devices. Ambient energy harvesting may provide a solution to this problem. However, they may fluctuate widely or even disappear completely during the same period.
A recent paper tries to maximize the use of the device under the requirements of dynamic energy consumption. First, the power the device can consume during each period of time is calculated using the harvested energy profile. Initial assignments are corrected after each time period with actual harvested power.
A dataset consisting of predicted and actual power harvest values was generated for 4,772 users to evaluate the proposal. The proposed approach yielded 34.6% higher benefit than previous runtime technologies and only 2% less benefit from an optimal iterative algorithm.
Energy harvesting provides an attractive and promising mechanism for operating low energy appliances. However, it alone is insufficient to enable power neutral operation, which can negate the tedious battery charging and replacement requirements. Achieving an energy neutral process is challenging because the uncertainties in harvested energy undermine the quality of service requirements. To meet this challenge, we introduce a runtime energy allocation framework that optimizes the utility of a target device under power constraints. The proposed framework uses an efficient iterative algorithm to calculate initial energy allocations at the start of the day. The initial allocations are then corrected at each time period to compensate for deviations from the expected energy harvest pattern. We evaluate this framework using solar energy collection methods, motion, and US Time Use Survey data from 4,772 different users. Compared with the latest technology, the proposed framework yields 34.6% higher benefit even under limited energy scenarios. Moreover, measurements on a prototype of a wearable device show that the proposed tire contains less than 0.1% of excess energy compared to the iterative approaches with little loss of utility.
Research paper: Tuncel, Y., Bhat, G., Park, J., and Ogras, U., “ECO: Enabled Energy-Neutral IoT Devices from Runtime Allocation of Harvested Energy,” 2021. Link: https://arxiv.org/abs/2102.13605
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