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arXiv · 2111.03967

A Deep Reinforcement Learning Approach for Composing Moving IoT Services

Azadeh Ghari Neiat·Athman Bouguettaya·Mohammed Bahutair
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Abstract

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.

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Azadeh Ghari Neiat, Athman Bouguettaya, Mohammed Bahutair. 2021-11-06. A Deep Reinforcement Learning Approach for Composing Moving IoT Services. https://arxiv.org/abs/2111.03967

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