Dataset Information

Honeycomb: An open-source distributed system for smart buildings.

ABSTRACT: Restricted by the hierarchical and centralized system architecture, smart buildings face challenges such as limited adaptability and robustness, single application functionalities, and complex configurations. To address the above shortcomings, we learn from the activity patterns of natural bee swarms and propose Honeycomb, an open-source smart-building solution with fully distributed architecture. Honeycomb is a robust, flexible smart-building solution without any central server or global leader. An asynchronous leaderless spanning tree-based communication pattern is developed to generate and maintain the communication topology of Honeycomb in real time. Benefiting from this communication pattern, Honeycomb has plug-and-play ability. Various distributed applications are designed for building operating tasks and are deployed in a real Honeycomb prototype. The prototype demonstrates significant energy efficiency improvement from the control of the heating, ventilation, and air conditioning (HVAC) system with video-based occupancy information. Feedback on our Honeycomb prototype through questionnaires of users shows high acceptance of the controlled indoor environment.

SUBMITTER: Xing T

PROVIDER: S-EPMC9676562 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Honeycomb: An open-source distributed system for smart buildings.

Xing Tian T Yan Hu H Sun Kailai K Wang Yifan Y Wang Xuetao X Zhao Qianchuan Q

Patterns (New York, N.Y.) 20221018 11

Restricted by the hierarchical and centralized system architecture, smart buildings face challenges such as limited adaptability and robustness, single application functionalities, and complex configurations. To address the above shortcomings, we learn from the activity patterns of natural bee swarms and propose Honeycomb, an open-source smart-building solution with fully distributed architecture. Honeycomb is a robust, flexible smart-building solution without any central server or global leader ...[more]

PMID: 36419455

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Project description:BackgroundHigh-quality plant phenotyping and climate data lay the foundation for phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations and at different scales.FindingsCropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralized data management. Data transfer and annotation are accomplished automatically through an hypertext transfer protocol-accessible RESTful API installed on both device side and server side of the CropSight system, which synchronize daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance while different experiments are being conducted.ConclusionsAs a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future.

Dataset Information

Honeycomb: An open-source distributed system for smart buildings.

Publications

Honeycomb: An open-source distributed system for smart buildings.

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