LAUSR

Recently, fog computing has emerged as a prospective technique to provide pervasive and agile computation services for Internet-of-Things (IoT) devices and support advanced applications. Introducing the energy harvesting (EH) technique into the fog computing system can extend the battery lifetime and provide a higher quality of experiences (QoE) for IoT devices. In the EH-enabled IoT fog system, computation offloading is an important issue and has attracted much attention. In most existing works, it is assumed that the IoT device is fully aware of the system state. However, in practical offloading problems, the IoT device may not be able to obtain accurate system state information, and only have a partial observation of the environment. Therefore, in this article, we investigate the decentralized partially observable offloading problem in the EH-enabled IoT fog system, in which multiple IoT devices cooperate to maximize the network performance while meeting their QoE requirements. We formulate the optimization problem as a decentralized partially observable Markov decision process (Dec-POMDP) in which each IoT device makes the task offloading decisions according to its local observation of the environment. The Lagrangian approach and the policy gradient method are adopted to find the optimal solution for the proposed problem. Due to the high complexity of solving the Dec-POMDP, a learning-based decentralized offloading algorithm with low complexity is presented to find the approximate optimal solution. Finally, extensive experimental evaluation and comparison are carried out to show the effectiveness of the proposed scheme.