# A Deep Reinforcement Learning Approach for Composing Moving IoT Services

arXiv: https://arxiv.org/abs/2111.03967 · HTML: https://mlsanity.com/paper/2111.03967/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 65 references that are in the map.
- First posted on arXiv 6 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Crowdsourcing: a new tool for policy-making?](https://mlsanity.com/paper/1802.03113/) (2018)
- [Mobile Crowdsourced Sensors Selection for Journey Services](https://mlsanity.com/paper/1812.08877/) (2018)

## Built on by (most cited first, 2 of 2)

- [Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning](https://mlsanity.com/paper/2211.16687/) (2022)
- [A Drift Stable Quantum Federated Learning for Intelligent Services](https://mlsanity.com/paper/2607.21647/) (2026)

## Abstract (arXiv)

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.