The recent growth of application variety for Energy Harvesting Wireless Sensor Network (EH-WSNs) poses new demands on matching energy consumption of sensors and actuators with energy harvest. High precision sensing—e.g., measuring gas concentration or fine particle matter—offers valuable insight into the environment yet increases program complexity due to dependent deadlines of multiple involved sensing tasks. Execution of all tasks at once meets all deadlines but causes a high energy demand in a short time period. Due to the small energy buffers of EH-WSNs, solely executing all tasks in batch and adapting the execution interval likely prevents completion of all tasks and threatens availability of the system. This raises the need for task scheduling that guarantees dependencies and deadlines inherited from physical phenomena. Standard mathematical solvers meet this end but are too computationally complex for sensor nodes. For this reason, we introduce and discuss light-weight scheduling strategies for dependent tasks restricted by both time and energy constraints. We show via simulations and a real-world experiment that our algorithm outperforms existing scheduling techniques in both execution time and node downtime. We decrease the latter by a factor of 5 maintaining equal activity or increase activity by up to 28% at equal downtimes. Our approach paves the ground for the emerging variety of modern, complex applications for energy-constrained sensors and actuators.

能量收集无线传感器网络（EH-WSN）的应用程序种类的最新增长对将传感器和执行器的能量消耗与能量收集相匹配提出了新的要求。高精度感测（例如，测量气体浓度或细颗粒物质）为环境提供了宝贵的见识，但由于涉及多个感测任务的截止期限而增加了程序的复杂性。一次执行所有任务可以满足所有截止日期，但会在短时间内导致很高的能源需求。由于EH-WSN的能量缓冲区很小，仅批量执行所有任务并调整执行间隔可能会阻止所有任务的完成并威胁系统的可用性。这就增加了对任务调度的需求，以保证从物理现象继承的依赖性和截止日期。标准的数学求解器满足了这一要求，但对于传感器节点而言，计算量太大。因此，我们介绍并讨论了受时间和精力限制的相关任务的轻量级调度策略。我们通过仿真和实际实验证明，我们的算法在执行时间和节点停机时间方面均优于现有的调度技术。我们将后者保持相同的活动量减少5倍，或者在相同的停机时间将活动量最多增加28％。我们的方法为能量受限的传感器和执行器的各种现代，复杂应用奠定了基础。我们介绍并讨论了受时间和精力限制的相关任务的轻量级调度策略。我们通过仿真和实际实验证明，我们的算法在执行时间和节点停机时间方面均优于现有的调度技术。我们将后者保持相同的活动量减少5倍，或者在相同的停机时间将活动量最多增加28％。我们的方法为能量受限的传感器和执行器的各种现代，复杂应用奠定了基础。我们介绍并讨论了受时间和精力限制的相关任务的轻量级调度策略。我们通过仿真和实际实验证明，我们的算法在执行时间和节点停机时间方面均优于现有的调度技术。我们将后者保持相同的活动量减少5倍，或者在相同的停机时间将活动量最多增加28％。我们的方法为能量受限的传感器和执行器的各种现代，复杂应用奠定了基础。