An improved spectrum sharing strategy evaluation over wireless network framework to perform error free communications

Abstract

The possible application of wireless sensor networks is hampered and the widespread use of this novel method is slowed, according to recent surveys conducted within the field of automation in industry, which identified that accuracy pertains indicate currently among the primary obstacles to the dissemination of wireless networking for recognizing and regulating applications. In order to overcome these constraints, it is necessary to raise public understanding of the reasons for dependability issues and the potential approaches to resolving them. Low-power communications of sensor nodes are, in reality, quite susceptible to adverse channel conditions and can readily be affected by transmissions of other co-located devices, making them seem unreliable. In this dissertation, I explore several strategies that may be used to either eliminate interference altogether or reduce its negative consequences. In this paper, we study the creation and modeling of a brand-new spectrum allocation mechanism for wireless sensor networks. Cognitive radio technology can detect spectrum holes in the environment, learn from its surroundings using artificial intelligence, adjust the system’s operating parameters in real-time, and use the secondary spectrum to increase efficiency. In this study, we present a reinforcement learning-based strategy for choosing the power of transmission and frequency that can help individual sensors learn from their prior decisions and those of their peers. Our suggested approach is multiple agents decentralized and adaptable to both the data needs from source to sink and the amount of energy that sensing devices in the network have left over. In comparison to different resource allocation algorithms, the results reveal a dramatic increase in the lifespan of the network.