A New Block-Based Reinforcement Learning Approach for Distributed Resource Allocation in Clustered IoT Networks

Resource allocation and spectrum management are two major challenges in the massive scale deployment of Internet of Things (IoT) and Machine-to-Machine (M2M) communication. Furthermore, the large number of devices per unit area in IoT networks also leads to congestion, network overload, and deterioration of the Signal to Noise Ratio (SNR). To address these problems, efficient resource allocation play a pivotal role in optimizing the throughput, delay, and power management of IoT networks. To this end, most of the existing resource allocation mechanisms are centralized and do not gracefully support the heterogeneous and dynamic IoT networks. Therefore, distributed and Machine Learning (ML)-based approaches are essential. However, distributed resource allocation techniques also have scalability problem with large number of devices whereas the ML-based approaches are currently scarce in the literature. In this paper, we propose a new distributed block-based Q-learning algorithm for slot scheduling in the smart devices and Machine Type Communication Devices (MTCDs) participating in clustered IoT networks. We furthermore, propose various reward schemes for the evolution of Q-values in the proposed scheme and, discuss and evaluate their effect on the distributed model. Our goal is to avoid inter- and intra-cluster interference, and to improve the Signal to Interference Ratio (SIR) by employing frequency diversity in a multi-channel system. Through extensive simulations, we analyze the effects of the distributed slot-assignment (with respect to varying SIR) on the convergence rate and the convergence probability. Our theoretical analysis and simulations validate the effectiveness of our proposed method where, (i) a suitable slot with acceptable SIR levels is allocated to each MTCD, and (ii) IoT network can efficiently converge to a collision-free transmission causing minimum intra-cluster interference. The network convergence is achieved through each MTCD's learning ability during the distributed slot allocation.