We develop a novel framework for efficiently and effectively discovering crowdsourced services that move 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.
@article{arxiv.2111.03967,
title = {A Deep Reinforcement Learning Approach for Composing Moving IoT Services},
author = {Azadeh Ghari Neiat and Athman Bouguettaya and Mohammed Bahutair},
journal= {arXiv preprint arXiv:2111.03967},
year = {2021}
}