Abstract
Most approaches to resource allocation in energy harvesting systems for Internet-of-Things (IoT) networks do not consider real-time periodic task allocation with Quality of Service (QoS). This paper studies the offline two-dimensional optimization problem for allocating randomly harvested energy flowing causally between slots into different real-time periodic tasks on an IoT device. We formulate an energy allocation problem, for tasks with different energy costs and requested QoS, which aims to maximize a convex reward function subject to energy causality (EC), energy saturation (ES) and task executable (TE) constraints within a certain length of time. We decouple the optimization problem into two subproblems after analysis. First, we propose a novel method to allocate energy to slots based on the Karush-Kuhn-Tucher conditions only considering the EC & ES constraints. The proposed method outperforms the state of the art “Tunnel Policy”, based on geometric programming. Next, an adaption is made to satisfy the TE constraints by allocating in the original constant power slots directly without iteratively checking the wasted energy. Finally, the energy already allocated to slots is put into tasks to complete the two-dimensional allocation. The effectiveness of the proposed methods and task framework are validated by extensive experiments.
Published Version
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