# Mobile Crowdsourced Sensors Selection for Journey Services

arXiv: https://arxiv.org/abs/1812.08877 · HTML: https://mlsanity.com/paper/1812.08877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [A Deep Reinforcement Learning Approach for Composing Moving IoT Services](https://mlsanity.com/paper/2111.03967/) (2021)

## Abstract (arXiv)

We propose a mobile crowdsourced sensors selection approach to improve the journey planning service especially in areas where no wireless or vehicular sensors are available. We develop a location estimation model of journey services based on an unsupervised learning model to select and cluster the right mobile crowdsourced sensors that are accurately mapped to the right journey service. In our model, the mobile crowdsourced sensors trajectories are clustered based on common features such as speed and direction. Experimental results demonstrate that the proposed framework is efficient in selecting the right crowdsourced sensors.