AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Article Link
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Cover Article

AlphaMobileSensing: A virtual testbed for mobile environmental monitoring

Qi Zhou1, Haoran Zhong1, Linyan Li2, Zhe Wang1,3( )
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
School of Data Science, City University of Hong Kong, Hong Kong, China
HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China
Show Author Information

Abstract

Environmental monitoring plays a critical role in creating and maintaining a comfortable, productive, and healthy environment. Built upon the advancements of robotics and data processing, mobile sensing demonstrates its potential to address problems regarding cost, deployment, and resolution that stationary monitoring encounters, which therefore has attracted increasing research attentions recently. To facilitate mobile sensing, two key algorithms are needed: the field reconstruction algorithm and the route planning algorithm. The field reconstruction algorithm is to reconstruct the entire environment field from spatially- and temporally-discrete measurements collected by the mobile sensors. The route planning algorithm is to instruct the mobile sensors where the mobile sensor needs to move to for the next measurements. The performance of mobile sensors highly depends on these two algorithms. However, developing and testing those algorithms in the real world is expensive, challenging, and time-consuming. To address these issues, we proposed and implemented an open-source virtual testbed, AlphaMobileSensing, that can be used to develop, test, and benchmark mobile sensing algorithms. AlphaMobileSensing aims to help users more easily develop and test the field reconstruction and route planning algorithms for mobile sensing solutions, without worrying about hardware fault, test accidents (such as collision during the test), etc. The separation of concerns can significantly reduce the cost of developing software solutions for mobile sensing. For versatility and flexibility, AlphaMobileSensing was wrapped up using the standardized interface of OpenAI Gym, and it also provides an interface for loading physical fields that were generated by numerical simulations as virtual test sites to perform mobile sensing and retrieving monitoring data. We demonstrated applications of the virtual testbed by implementing and testing algorithms for physical field reconstruction in both static and dynamic indoor thermal environments. AlphaMobileSensing provides a novel and flexible platform to develop, test, and benchmark mobile sensing algorithms more easily, conveniently, and efficiently.

AlphaMobileSensing is open sourced at https://github.com/kishuqizhou/AlphaMobileSensing.

Electronic Supplementary Material

Download File(s)
bs-16-7-1027_ESM.pdf (110.8 KB)

References

【1】
【1】
 
 
Building Simulation
Pages 1027-1040

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Zhou Q, Zhong H, Li L, et al. AlphaMobileSensing: A virtual testbed for mobile environmental monitoring. Building Simulation, 2023, 16(7): 1027-1040. https://doi.org/10.1007/s12273-023-1001-9

1403

Views

8

Crossref

8

Web of Science

10

Scopus

0

CSCD

Received: 16 December 2022
Revised: 16 January 2023
Accepted: 06 February 2023
Published: 28 February 2023
© Tsinghua University Press 2023