A Deep Reinforcement Learning Approach for Composing Moving IoT Services
This addresses the challenge of dynamic service composition in IoT for users needing real-time, location-based services, but it appears incremental as it applies existing deep reinforcement learning to a specific domain.
The paper tackles the problem of discovering and composing moving IoT services near a user over time by proposing a deep reinforcement learning-based approach, which is shown to be effective and efficient in experiments on two real-world datasets.
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.