arXiv Open Access 2021

A Deep Reinforcement Learning Approach for Composing Moving IoT Services

Azadeh Ghari Neiat Athman Bouguettaya Mohammed Bahutair

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Abstrak

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.

Topik & Kata Kunci

cs.LG

Penulis (3)

Azadeh Ghari Neiat

Athman Bouguettaya

Mohammed Bahutair

Format Sitasi

APA MLA BibTeX

Neiat, A.G., Bouguettaya, A., Bahutair, M. (2021). A Deep Reinforcement Learning Approach for Composing Moving IoT Services. https://arxiv.org/abs/2111.03967

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Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓