A Deep Reinforcement Learning Approach for Composing Moving IoT Services

TL;DR

This paper presents a deep reinforcement learning framework for dynamically discovering and composing moving IoT services in proximity to users, improving efficiency and accuracy in service selection.

Contribution

It introduces a novel deep reinforcement learning approach for composing moving IoT services and a parallel flock-based algorithm for validation.

Findings

The approach effectively discovers relevant services in real-world datasets.

It outperforms baseline methods in accuracy and efficiency.

Experimental results confirm the method's practicality for real-time IoT service composition.

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.