A Deep Reinforcement Learning Approach for Composing Moving IoT Services
Azadeh Ghari Neiat, Athman Bouguettaya, Mohammed Bahutair
Abstract
We develop a novel framework for efficiently and effectively discovering crowdsourced services that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">move</i> in close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT services considering quality parameters. Additionally, we develop a parallel flock-based service discovery algorithm as a ground-truth to measure the accuracy of the proposed approach. The experiments on two real-world datasets verify the effectiveness and efficiency of the deep reinforcement learning-based approach.