Using Reinforcement Learning to Improve the Energy Consumption of Nodes in an IoT Network.

Internet of Things (IoT) has revolutionized the automation of a network of devices utilizing the functions of the individual nodes of sensors and devices connected to the network. Without other external factors to consider, the number of nodes of devices in the network is directly proportional to the energy requirement and consumption. This research presents the development of an abstract model to predict and reduce energy consumption in an IoT ecosystem, based on a reinforcement learning approach. To measure the potential improvement, two devices were deployed in an IoT ecosystem - one was set up with the default configuration, and the second one with a designed heuristic and an integrated reinforcement learning model. Using the second setup, the average daily consumption was reduced by 19.92W, from 2,841.35W to 2,821.43W. This difference is statistically significant (p < 0.001). The node-based savings in energy consumption can collectively bring larger energy savings when applied to all nodes across an IoT ecosystem. [ABSTRACT FROM AUTHOR]