NDLI: Zodiac: Organizing Large Deployment of Sensors to Create Reusable Applications for Buildings

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Zodiac: Organizing Large Deployment of Sensors to Create Reusable Applications for Buildings

Content Provider	ACM Digital Library
Author	Narayanaswamy, Balakrishnan Balaji, Bharathan Agarwal, Yuvraj Verma, Chetan
Abstract	Large scale deployment of sensors is essential to practical applications in cyber physical systems. For instance, instrumenting a commercial building for 'smart energy' management requires deployment and operation of thousands of measurement and metering sensors and actuators that direct operation of the HVAC system. Each of these sensors need to be named consistently and constantly calibrated. Doing this process manually is not only time consuming but also error prone given the scale, heterogeneity and complexity of buildings as well as lack of uniform naming schemas. To address this challenge, we propose Zodiac - a framework for automatically classifying, naming and managing sensors based on active learning from sensor metadata. In contrast to prior work, Zodiac requires minimal user input in terms of labelling examples while being more accurate. To evaluate Zodiac, we deploy it across four real buildings on our campus and label the ground truth metadata for all the sensors in these buildings manually. Using a combination of hierarchical clustering and random forest classifiers we show that Zodiac can successfully classify sensors with an average accuracy of 98% with 28% fewer training examples when compared to a regular expression based method.
Starting Page	13
Ending Page	22
Page Count	10
File Format	PDF
ISBN	9781450339810
DOI	10.1145/2821650.2821674
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hvac Sensor metadata Smart buildings Ontology Active learning Automated naming Bacnet Building management systems
Content Type	Text
Resource Type	Article

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