An Energy Aware Approach to Trust Management Systems for Embedded Multi-Agent Systems

Abstract

With the growing interest toward pervasive systems such as the Internet of Things or Cyber-Physical Systems, embedded multi-agent systems have been increasingly investigated. In these systems, agents cooperate to achieve their local goals and a global goal that would be impossible for an isolated agent to achieve. However, the dark side of this collaboration is that agents can easily be victim of malicious attacks coming from untrustworthy agents. Consequently, trust management systems are designed to help agents choosing trustworthy counterparts to cooperate based on available information. But gathering the necessary information may be too expensive in terms of energy for small embedded agents and not relevant in all contexts. We propose a solution that allows agents to manage the energy consumption associated with information gathering. Our solution uses a Multi-Armed Bandit algorithm, which is a reinforcement learning technique to allow the agents to adapt themselves and their energy consumption to the context.