Hanschke, LarsLarsHanschkeRenner, Bernd-ChristianBernd-ChristianRenner2020-07-102020-07-102020-09Sustainable Computing: Informatics and Systems (27): 100409 (2020-09)http://hdl.handle.net/11420/6723The 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.en2210-53792020Energy harvestingEnergy neutral operationEnvironmental monitoringSchedulingWireless Sensor NetworksJournal Article10.1016/j.suscom.2020.100409Other