Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high-level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model, and the computation latency model. Further, we…

• NN
  National Natural Science Foundation of China

  Awards: 61533015, 61572538, 61761136012, 61671396, 91638204
• FR
  Fundamental Research Funds for the Central Universities

  Award: 17LGJC23
• NM
  National Mobile Communications Research Laboratory, Southeast University

  Award: 2018D08