A Deep Reinforcement Learning Approach for Composing Moving IoT Services release_pu7di2fslff3poaiffhdcddneu

by Azadeh Ghari Neiat, Athman Bouguettaya, Mohammed Bahutair

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2021  

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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Type  article
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Date   2021-11-06
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Language   en ?
arXiv  2111.03967v1
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