Multi-Agent Reinforcement learning Approach to IoT Coordination. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Belkeziz, Radia; Jarir, Zahi; El Kassmi, Ilyass
• Is Part Of: Journal of physics. Volume 1743:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1743/1/012008
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Nowadays, the IoT is evolving at a very fast pace and has proven its usefulness in several areas by creating better applications and services. However, more flexible approaches proving a well-defined architecture meeting the general requirements and building blocks of IoT are still needed despite the results obtained in the literature. In this paper, we focus on the IoT coordination challenge which represents a fundamental property allowing things to collaborate and make a decision when an appropriate change is detected in its environment. This contribution proposes an agent-based approach coupled with Q-learning which is a reinforcement learning technique, to compensate for coordination in its entirety, namely objective coordination and subjective coordination. To illustrate this approach, an evacuation use case is presented.