A Deep Reinforcement Learning Approach for Composing Moving IoT Services

• URL: http://arxiv.org/abs/2111.03967v1
• Date: Sat, 6 Nov 2021 22:02:31 GMT
• Title: A Deep Reinforcement Learning Approach for Composing Moving IoT Services
• Authors: Azadeh Ghari Neiat, Athman Bouguettaya, Mohammed Bahutair
• Abstract summary: 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. The experiments on two real-world datasets verify the effectiveness and efficiency of the deep reinforcement learning-based approach.
• Score: 0.12891210250935145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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