Today's IoT devices rely on batteries, which offer stable energy storage but contain harmful chemicals. Having billions of IoT devices powered by batteries is not sustainable for the future. As an alternative, batteryless devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in intermittent on-off behaviour. Traditional computing schedulers can not handle this intermittency, and in this paper we propose a first step towards an energy-aware task scheduler for constrained batteryless devices. We present a new energy-aware task scheduling algorithm that is able to optimally schedule application tasks to avoid power failures, and that will allow us to provide insights on the optimal look-ahead time for energy prediction. Our insights can be used as a basis for practical energy-aware scheduling and energy availability prediction algorithms. We formulate the scheduling problem as a Mixed Integer Linear Program. We evaluate its performance improvement when comparing it with state-of-the-art schedulers for batteryless IoT devices. Our results show that making the task scheduler energy aware avoids power failures and allows more tasks to successfully execute. Moreover, we conclude that a relatively short look-ahead energy prediction time of 8 future task executions is enough to achieve optimality.

翻译：当今物联网设备依赖电池提供稳定储能，但电池含有有害化学物质。数十亿台由电池供电的物联网设备在未来将不可持续。作为替代方案，无电池设备采用长寿命电容器运行，通过能量收集器充电。电容器微小的储能容量导致设备出现间歇性开关行为。传统计算调度器无法处理这种间歇性，本文首次提出面向资源受限无电池设备的能量感知任务调度方案。我们提出了一种新型能量感知任务调度算法，能够优化调度应用任务以避免电源故障，并揭示能量预测的最佳前瞻时间。这些见解可作为实用化能量感知调度与能量可用性预测算法的基础。我们将调度问题建模为混合整数线性规划，并通过与现有最优无电池物联网设备调度器进行性能对比评估。结果表明，使任务调度器具备能量感知能力可避免电源故障，并允许更多任务成功执行。此外，我们得出结论：仅需8次未来任务执行的较短能量预测前瞻时间即可实现最优调度。