Optimal resource allocation using reinforcement learning for IoT content-centric services - Gai, Keke; Qiu, Meikang (Applied soft computing, 2018)
•Propose a novel dynamic programming that uses RL techniques for resource allocations in IoT.•Combines QoE with RL to create pre-stored cost mapping tables for optimal resource allocations.•Implement content-centric network to enhance the fulfillment of the resource allocation. The exponential growing rate of the networking technologies has led to a dramatical large scope of the connected computing environment. Internet-of-Things (IoT) is considered an alternative for obtaining high performance by the enhanced capabilities in system controls, resource allocations, data exchanges, and flexible adoptions. However, current IoT is encountering the bottleneck of the resource allocation due to the mismatching networking service quality and complicated service offering environments. This paper concentrates on the issue of resource allocations in IoT and utilizes the satisfactory level of Quality of Experience (QoE) to achieve intelligent content-centric services. A novel approach is proposed by this work, which utilizes the mechanism of Reinforcement Learning (RL) to obtain high accurate QoE in resource allocations. Two RL-based algorithms have been proposed for cost mapping tables creations and optimal resource allocations. Our experiment evaluations have assessed the efficiency of implementing the proposed approach.