Multiprocessor Real-Time Scheduling for Wireless Sensors Powered by Renewable Energy Sources

Ambient energy harvesting has become a popular solution for battery-operated systems with finite energy supply such as wireless sensor networks. This paper investigates the problem of multiprocessor real-time scheduling in a system whose energy reservoir is replenished by an ambient energy source. In particular, we focus on energy-efficient partitioning for periodic real-time tasks in a homogeneous multi-core platform where both timing and energy requirements are considered. We assume that the optimal scheduler, namely the Earliest Deadline — Harvesting (ED-H) [1], is used on every core of the architecture. Our objective is to find a feasible partitioning solution based on the real-time execution of tasks allocated to the sensor nodes based on the actual energy harvesting data to guarantee the desirable absence of both energy starvation situations and deadline violation. For this sake, we propose an Energy Harvesting-Reasonable Allocation (EH-RA) algorithm that amounts to the traditional bin-packing technique by guaranteeing both timing constraints and energy awareness perspectives. Experimental results show that our approach can achieve a significant gain in performance when compared to EDF.