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In recent years, the energy requirements for devices in internet of things (IoT) applications have increased, making energy harvesting (EH) technology an important way to alleviate the energy shortage problem in edge computing and extend the battery life of devices. However, when there was insufficient renewable energy in the environment, the depletion of device power can cause task interruption and affect the performance of IoT. To solve this problem, a task offloading framework that combined energy harvesting and device-to-device (D2D) communication technology was proposed, using a deep reinforcement learning (DRL)-based edge collaborative offloading computing scheme to make autonomous decisions and solve resource allocation problems using simulated annealing algorithms to minimize the total cost of system operation. Simulation results on stable and extreme energy environments show that the proposed scheme can run stably and cost-effectively in single-user multiple-device scenarios. |
