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Application of the Reconfigurable Environmental Intelligence Platform for HVAC Control

Published: 15 November 2023 Publication History

Abstract

Researchers of varying experience levels need to be able to quickly prototype and deploy sensor networks so that they can collect data and analyze their phenomena of interest as soon as possible. However, custom sensor deployments are subject to several constraints including computing resources available and types of data that are being acquired. We show how the use of the Reconfigurable Environmental Intelligence Platform (REIP) streamlines the process of multimodal environmental sensing. We offered the original sensors to a group of student researchers to monitor indoor occupancy alongside temperature and humidity in a modern office building. We demonstrate how the modular design of REIP made such a study feasible for young researchers in the context of a course project. The study resulted in findings leading to practical solutions on how air conditioning and ventilation systems can be operated more efficiently to minimize the building’s energy use without affecting the comfort of its residents. It also demonstrates the potential of REIP for the rapid prototyping of multimodal sensor networks that can, in turn, enable a more data-driven approach to decision-making.

References

[1]
Sudershan Boovaraghavan, Chen Chen, Anurag Maravi, Mike Czapik, Yang Zhang, Chris Harrison, and Yuvraj Agarwal. 2023. Mites: Design and Deployment of a General-Purpose Sensing Infrastructure for Buildings. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 1, Article 2 (mar 2023), 32 pages. https://doi.org/10.1145/3580865
[2]
U.S. Energy Information Administration (EIA). 2022. Frequently Asked Questions. https://www.eia.gov/tools/faqs/faq.php?id=1174&t=1
[3]
Yurii Piadyk, Bea Steers, Charlie Mydlarz, Mahin Salman, Magdalena Fuentes, Junaid Khan, Hong Jiang, Kaan Ozbay, Juan Pablo Bello, and Claudio Silva. 2022. REIP: A Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping. Sensors 22, 10 (2022). https://doi.org/10.3390/s22103809
[4]
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, Los Alamitos, CA, USA, 4510–4520. https://doi.org/10.1109/CVPR.2018.00474

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    BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
    November 2023
    567 pages
    ISBN:9798400702303
    DOI:10.1145/3600100
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 November 2023

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    Author Tags

    1. Control systems
    2. environmental monitoring
    3. sensor networks.

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    Overall Acceptance Rate 148 of 500 submissions, 30%

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