  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.
