A DSPL and Reinforcement Learning Approach for Context-Aware IoT Systems Development
Amal Hallou, Tarik Fissaa, Hatim Hafiddi, Mahmoud Nassar · International Journal of Security and Privacy in Pervasive Computing · 2022
The internet of things is a paradigm of interconnected devices able to communicate and exchange information to achieve users' requirements. In spite of their expansion in the last years, IoT systems still face challenges that hinder gaining major advantages from them. One of these challenges is to automatically adapt the system to the user's context and preferences. As a proposition to deal with this problem, this paper presents a methodology to design and develop IoT systems that adapt their behavior to their context, which can be a user or environmental context. This methodology is based on dynamic software product line engineering and uses Markov process to design the adaptation plan of the system and a reinforcement learning algorithm to implement it.