Context-Aware IoT System Development Approach Based on Meta-Modeling and Reinforcement Learning

Amal Hallou, Tarik Fissaa, Hatim Hafiddi, Mahmoud Nassar
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Abstract

Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users’ preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context- aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems’ awareness of their users’ context.
基于元建模和强化学习的情境感知物联网系统开发方法
在物联网系统中融入情境感知对于增强系统对情境的适应性,尤其是对用户偏好和行为的适应性至关重要。本文提出了一种建模和开发情境感知自适应物联网系统的方法,该系统能够根据用户的偏好调整自己的行动。该方法由三个主轴组成。第一条主线是建立系统架构概览,提供对情境感知物联网系统各组成部分的高层次理解。第二轴涉及创建上下文感知物联网系统元模型,以独立于领域的方式封装物联网系统中管理上下文感知的基本要素、关系和依赖性。第三个轴心提出了一个强化学习推理过程,以便在情境感知物联网系统中实现智能决策。为了验证所提方法的可行性,我们使用 OpenAI Gym 框架进行了模拟,以仿真情境感知智能家居系统。结果凸显了该方法的可行性,以及它在增强现实生活中物联网系统对用户情境感知方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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