Reinforcement Learning Based Spectrum Sensing and Resource Allocation in WSN-IoT Smart Applications

There is a tremendous demand for resources due to the different emerging IoT applications that must be fulfilled. The large-scale resource-constrained IoT ecosystem needs a novel and robust technique to manage resources. An optimal resource provisioning in IoT ecosystem has to handle the mapping of request-resource and is hard to achieve as the IoT requests and resources are dynamic and heterogeneous. Therefore, artificial intelligence (AI) has a vital role in the IoT network. As changes are dynamic, global optimization cannot be achieved due to classifiers of the wireless signals and high interference. In this research, spectrum selection is integrated with spectrum access by the use of reinforcement learning with stochastic-based reward measures for efficient resource allocation in WSN-IoT applications. Here, state–action–reward–state–action approach with the Gittins index named SARSA-GI is involved. The role of the state–action–reward–state–action model is developed with an energy-efficient approach for optimizing the channel. Next, the Gittins index is designed to reduce the delay and enhance the accuracy of spectrum access. The simulation results are compared with two state-of-the-art methods which achieve 91.4% of reliability, 88.4% of transmission probability, 93.4% of throughput, 66.8% of collision performance, and 57.2% of signal-to-interference noise ratio.