A Reinforcement Learning Approach for Storing Data in Reactive IoT Network

A IOT-N is a collection of sensors deployed in remote and unapproachable territories to gather information efficiently, but faces technical challenges such as battery power and transmission range.based on a cluster of energy-efficient As energy conservation of sensor nodes is essential for IOT-Ns, data transmission techniques are needed to decrease energy usage and increase device longevity. REINFORCEMENT is a protocol for selecting an efficient cache head(CH) and gateway (GW) node using parameters such as residual energy, lifetime, packet delivery ratio, energy efficiency, and end-to-end delay. It is implemented using Network Simulator 2 (NS2) version 2.34. The performance of REINFORCEMENT protocol is compared with ARPEES (Adaptive Data Transmission Protocol with Energy Efficiency and Event Clustering for IoT Networks) and SEECH (Scalable Energy Efficient Clustering Hierarchy) protocols by varying the number of rounds. The simulation results demonstrate the ability of REINFORCEMENT protocol to achieve higher residual energy, higher lifetime, higher packet delivery ratio, higher energy efficiency, and lower end-to-end delay than ARPEES and SEECH protocols.