Enhancement in Optimal Resource-Based Data Transmission Over LPWAN Using a Deep Adaptive Reinforcement Learning Model Aided by Novel Remora With Lotus Effect Optimization Algorithm

Wireless Sensor Networks (WSN) are adopting low-power wide area networks (LPWAN), such as long-range (LoRa) wide area networks, to increase communication standards. LoRa has been used to gather sensor data for many applications, such as environmental monitoring. The existing LoRa system faces degradation in network performance because of interference and congestion with the development of Internet-of-Things (IoT) devices. More than the device parameters and algorithms must be improved in large IoT applications. In massive LoRa systems, resource allocation is effectively performed using new reinforcement learning and machine learning approaches. These approaches have proven to be quite effective. Hence, this work implements an efficient optimal resource allocation scheme for effective data transmission over the LoRa with the minor power requirement with the aid of Deep Adaptive Reinforcement Learning (DARL). The parameters required to minimize the power requirement while transmitting the data are estimated with the help of this DARL model. The variables in the DARL are optimally selected by using a new optimization algorithm named Integrated Remora with Lotus Effect Optimization Algorithm (IR-LEOA) that is executed by combining Remora Optimization Algorithm (ROA) with the Lotus Effect Optimization Algorithm (LEA). The network parameters, such as the transmission power, channel, and spreading factor, are tuned using the same IR-LEOA. The server in the LoRa is matched by the agents generated by the DARL model. Then, the transmission parameters are given to the network’s terminal hub after the agents in the DARL are generated. Throughput, energy efficiency, latency, and transmission rate are analyzed using this optimization strategy. The effectiveness of the model is proved by conducting extensive experimentation.