Dezhi Hong

Brick: Towards a Unified Metadata Schema For Buildings

Commercial buildings have long since been a primary target for applications from a number of areas: from cyber-physical systems to building energy use to improved human interactions in built environments. While technological advances have been made in these areas, such solutions rarely experience widespread adoption due to the lack of a common descriptive schema which would reduce the now-prohibitive cost of porting these applications and systems to different buildings. Recent attempts have sought to address this issue through data standards and metadata schemes, but fail to capture the set of relationships and entities required by real applications. Building upon these works, this paper describes Brick, a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight complex unmodified applications on these buildings.

Towards Automatic Spatial Verification of Sensor Placement in Buildings

Most large, commercial buildings contain thousands of sensors that are manually deployed and managed. These sensors are used by software and firmware processes to analyze and control building operations. Many such processes rely on sensor placement information in order to perform correctly. However, as buildings evolve and building subsystems grow and change, managing placement information becomes burdensome and error-prone. An automatic verification process is needed. We investigate empirical methods to automate spatial verification. We find that a spatial clustering algorithm is able to classify relative sensor locations -- for 15 sensors, spread across five rooms in a building -- with 93.3% accuracy, 13% better than a k-means clustering-based baseline method. Analysis on the raw time series data has a classification accuracy of only 53%. By decomposing the signal into intrinsic modes and performing correlation analysis, an observable, statistical boundary emerges that corresponds to a physical one. These results may suggest that automatic verification of placement information is possible.

Clustering-based Active Learning on Sensor Type Classification in Buildings

Commercial and industrial buildings account for a considerable portion of all energy consumed in the U.S., and thus reducing this energy consumption is a national grand challenge. Based on the large deployment of sensors in modern commercial buildings, many organizations are applying data analytic solutions to the thousands of sensing and control points to detect wasteful and incorrect operations for energy savings. Scaling this approach is challenging, however, because the metadata about these sensing and control points is inconsistent between buildings, or even missing altogether. Moreover, normalizing the metadata requires significant integration effort. In this work, we demonstrate a first step towards an automatic metadata normalization solution that requires minimal human intervention. We propose a clustering-based active learning algorithm to differentiate sensors in buildings by type, e.g., temperature v.s. humidity. Our algorithm exploits data clustering structure and propagates labels to their nearby unlabeled neighbors to accelerate the learning process. We perform a comprehensive study on metadata collected from over 20 different sensor types and 2,500 sensor streams in three commercial buildings. Our approach is able to achieve more than 92% accuracy for type classification with much less labeled examples than baselines. As a proof-of-concept, we also demonstrate a typical analytic application enabled by the normalized metadata.

Forma Track: Tracking People based on Body Shape

Knowledge of a person’s whereabouts in the home is key to context-aware applications, but many people do not want to carry or wear a tag or mobile device in the home. Therefore, many tracking systems are now using so-called weak biometrics such as height, weight, and width. In this paper, we propose to use body shape as a weak biometric, differentiating people based on features such as head size, shoulder size, or torso size. The basic idea is to scan the body with a radar sensor and to compute the reflection profile: the amount of energy that reflects back from each part of the body. Many people have different body shapes even if they have the same height, weight, or width, which makes body shape a stronger biometric. We built a proof-of-concept system called FormaTrack to test this approach, and evaluate using eight participants of varying height and weight. We collected over 2800 observations while capturing a wide range of factors such as clothing, hats, shoes, and backpacks. Results show that FormaTrack can achieve a precision, recall, direction and identity accuracy (over all possible groups of 2 people) of 100%, 99.86%, 99.7% and 95.3% respectively. Results indicate that FormaTrack can achieve over 99% tracking accuracy with 2 people in a home with 5 or more rooms.

Automated metadata transformation for a-priori deployed sensor networks

Sensor network research has facilitated advancements in various domains, such as industrial monitoring, environmental sensing, etc., and research challenges have shifted from creating infrastructure to utilizing it. Extracting meaningful information from sensor data, or control applications using the data, depends on the metadata available to interpret it, whether provided by novel networks or legacy instrumentation. Commercial buildings provide a valuable setting for investigating automated metadata acquisition and augmentation, as they typically comprise large sensor networks, but have limited, obscure metadata that are often meaningful only to the facility managers. Moreover, this primitive metadata is imprecise and varies across vendors and deployments. This state-of-the-art is a fundamental barrier to scaling analytics or intelligent control across the building stock, as even the basic steps involve labor intensive manual efforts by highly trained consultants. Writing building applications on its sensor network remains largely intractable as it involves extensive help from an expert in each buildings design and operation to identify the sensors of interest and create the associated metadata. This process is repeated for each application development in a particular building, and across different buildings. This results in customized building-specific application queries which are not portable or scalable across buildings. We present a synthesis technique that learns how to transform a buildings primitive sensor metadata to a common namespace by using a small number of examples from an expert, such as the building manager. Once the transformation rules are learned for one building, it can be applied across buildings with a similar primitive metadata structure. This common and understandable namespace captures the semantic relationship between sensors, enabling analytics applications that do not require apriori building-specific knowledge. Initial results show that learning the rules to transform 70% of the primitive metadata of two buildings (with completely different metadata structure), comprising 1600 and 2600 sensors, into a common namespace ([1]) took only 21 and 27 examples respectively(Figure 1c). The learned rules were able to transform similar primitive metadata in other buildings as well(Figure 1d), enabling writing of portable applications across these buildings. The techniques developed here may be applicable to the other large legacy sensor networks, such as industrial processing, or urban monitoring.

Portable Queries Using the Brick Schema for Building Applications: Demo Abstract

Sensorized commercial buildings are a rich target for building a new class of applications that improve operational and energy efficiency of building operations that take into account human activities. Such applications, however, rarely experience widespread adoption due to the lack of a common descriptive schema that would enable porting these applications and systems to different buildings. Our demo presents Brick [4], a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. Using a web application, we will demonstrate real buildings that have been mapped to the Brick schema, and show application queries that extracts relevant metadata from these buildings. The attendees would be able to create example buildings and write their own queries.