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The Internet of Thing has been identified as one of the emerging technologies in IT. It interconnects and integrates large numbers of digital and physical entities by capability of appropriate information and communication technologies, to enable building enormous useful and unimaginable services and applications. However, building new IoT services or applications is a fastidious task since it is faced to several challenges such as interoperability, context-awareness, discovery, availability, decision-making. In this article, the authors are interested in coordination challenges that are still open despite the efforts of international organizations and scientific research groups. In fact, the authors outline a recent literature review of existing IoT coordination approaches. In the literature, researchers tend to use orchestration or choreography as a way to meet this challenge. A classification and the vision on this topic are presented. The authors propose an approach that is more likely to respond to the co-ordination challenge.

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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.

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