MULTI-AGENT LEARNING FRAMEWORK FOR ENVIRONMENT REDUNDANCY IDENTIFICATION FOR MOBILE SENSORS IN AN IOT CONTEXT
From an IoT point of view, the continuous growth of cheap and versatile sensor technologies has generated a massive data flow in communication networks, which most of the time carries unnecessary or redundant information that requires larger storage centers and more time to process and analyze data. Most of this redundancy is due to fact that network nodes are unable to identify environmental cues showing measurement changes to be considered and instead remain at a static location getting the same data. In this work we propose a multi-agent learning framework based on two theoretical tools. Firstly, we use Gaussian Process Regression (GPR) to make each node capable of getting information from the environment based on its current measurement and the measurements taken by its neighbors. Secondly, we use the rate distortion function to define a boundary where the information coming from the environment is neither redundant nor misunderstood. Finally, we show how the framework is applied in a mobile sensor network in which sensors decide to be more or less exploratory by means of the parameter s of the Blahut-Arimoto algorithm, and how it affects the measurement coverage in a spatial area being sensed.