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A large number of connected sensors and devices in Internet of Things (IoT) can generate large amounts of computing data and increase massive energy consumption. Real-time states monitoring and data processing of IoT nodes are of great significance, but the processing power of IoT devices is limited. Using the emerging mobile edge computing (MEC), IoT devices can offload computing tasks to an MEC server associated with small or macro base stations. Moreover, the use of renewable energy harvesting capabilities in base stations or IoT nodes may reduce energy consumption. As wireless channel conditions vary with time and the arrival rates of renewable energy, computing tasks are stochastic, and data offloading and renewable energy aware for IoT devices under a dynamic and unknown environment are major challenges. In this work, we design a data offloading and renewable energy aware model considering an MEC server performing multiple stochastic computing tasks and involving time-varied wireless channels. To optimize data transmission delay, energy consumption, and bandwidth allocation jointly, and to avoid the curse of dimensionality caused by the complexity of the action space, we propose a joint optimization method for data offloading, renewable energy aware, and bandwidth allocation for IoT devices based on deep reinforcement learning (JODRBRL), which can handle the continuous action space. JODRBRL can minimize the total system cost(including data buffer delay cost, energy consumption cost, and bandwidth cost) and obtain an efficient solution by adaptively learning from the dynamic IoT environment. The numerical results demonstrate that JODRBRL can effectively learn the optimal policy, which outperforms Dueling DQN, Double DQN (DDQN), and greedy policy in stochastic environments.

Joint Optimization of Data Offloading and Resource Allocation With Renewable Energy Aware for IoT Devices: A Deep Reinforcement Learning Approach