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

Stackelberg population dynamics: A predictive-sensitivity approach

Hierarchical decision-making processes traditionally modeled as bilevel optimization problems are widespread in modern engineering and social systems. In this work, we deal with a leader with a population of followers in a hierarchical order of play. In general, this problem can be modeled as a leader–follower Stackelberg equilibrium problem using a mathematical program with equilibrium constraints. We propose two interconnected dynamical systems to dynamically solve a bilevel optimization problem between a leader and follower population in a single time scale by a predictive-sensitivity conditioning interconnection. For the leader’s optimization problem, we developed a gradient descent algorithm based on the total derivative, and for the followers’ optimization problem, we used the population dynamics framework to model a population of interacting strategic agents. We extended the concept of the Stackelberg population equilibrium to the differential Stackelberg population equilibrium for population dynamics. Theoretical guarantees for the stability of the proposed Stackelberg population learning dynamics are presented. Finally, a distributed energy resource coordination problem is solved via pricing dynamics based on the proposed approach. Some simulation experiments are presented to illustrate the effectiveness of the framework