An Open Building Analytics Middleware for Smart Buildings
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OpenBAN: An Open Building ANalytics Middleware for Smart Buildings Pandarasamy Arjunan †, Mani B. Srivastava §, Amarjeet Singh †, Pushpendra Singh † †Indraprastha Institute of Information Technology §University of California Delhi, India Los Angeles, United States {pandarasamya,amarjeet,psingh}@iiitd.ac.in {mbs}@ucla.edu Abstract for monitoring, automating, and optimizing different build- Towards the realization of smart building applications, build- ing operations [2, 3]. Sensors in buildings generate a large ings are increasingly instrumented with diverse sensors and number of data streams that are processed by different en- actuators. These sensors generate large volumes of data ergy management applications for control actions. With in- which can be analyzed for optimizing building operations. creasing availability and affordability of sophisticated sens- Many building energy management tasks such as energy ing, control and computational methods, a variety of novel forecasting, disaggregation, among others require complex applications have been proposed in the recent past. Example analytics leveraging collected sensor data. While several applications include occupancy based lighting, heating and standalone and cloud-based systems for archiving, sharing cooling control system for reducing energy wastage [4, 5, 6]; and visualizing sensor data have emerged, their support for energy forecasting for demand response [7]; energy disag- analyzing sensor data streams is primitive and limited to gregation for providing appliance level energy consumption rule-based actions based on thresholds and simple aggre- feedback [8], and activity recognition [9]. gation functions. We develop OpenBAN, an open source Despite their potential utility, these novel building energy sensor data analytics middleware for buildings, to make an- management applications are still not in widespread usage alytics an integral component of modern smart building ap- today because, (i) these applications are mostly prototyped plications. OpenBAN provides a framework of extensible as isolated implementations using off-line data processing sensor data processing elements for identifying various build- tools, (ii) current legacy building management systems of- ing context, which different applications can leverage. We ten do not provide adequate support for integrating new validate the capabilities of OpenBAN by developing three building management applications, and (iii) it is non-trivial representative real-world applications which are deployed in to replicate an application in a setting which is different our test-bed buildings: (i) household energy disaggregation, for which it was originally designed. Modern building man- (ii) detection of sprinkler usage from water meter data, and agement systems, proposed by the research communities, (iii) electricity demand forecasting. We also provide a pre- provide rich and sophisticated support for communicating liminary system performance analysis of OpenBAN when with sensors and actuators, and for archiving, sharing, and deployed in the cloud and locally within a building. visualizing the data [10, 11, 12, 13]. However, the support for processing sensory data is far less mature, mostly lim- Categories and Subject Descriptors: ited to rule-based actions performed on the basis of simple H.4 [Information Systems Applications] : Miscellaneous thresholds and ranges. D.2.11 [Software Engineering] : Software Architectures Realization of many of the novel energy management ap- plications requires continuous computation of context in- General Terms: Design, Architecture, Deployment formation from sensor data. For example, “whenever the Keywords: Middleware, Building Management System, Smart meeting room is unoccupied, turn off the air conditioner”. Buildings, Context Aware Applications The ability to generate and utilize such context information is absent from most of the existing building management 1. INTRODUCTION systems. To support the computation of context informa- Buildings are an attractive target for using novel Cyber- tion about building operations from distributed sensory data Physical control systems to achieve energy sustainability streams, we propose OpenBAN, a sensor data analytics mid- goals [1]. They are increasingly instrumented with many dleware for buildings. OpenBAN architecture is designed to subsystems that use heterogeneous sensors and actuators scale across local and cloud-based deployments, and to sup- port diverse array of services and platforms designed for net- worked sensors. It provides a runtime environment for de- veloping and scheduling Contextlet — a pipeline of process- ing elements for inferring a particular building context from sensory data. Furthermore, OpenBAN is designed to enable building facilities department to connect various building sensor data streams with different existing and new control applications through a powerful analytics engine capable of inferring context information. Moreover, OpenBAN facil- . MOBIQUITOUS 2015, July 22-24, Coimbra, Portugal Copyright © 2015 ICST DOI 10.4108/eai.22-7-2015.2260256 Raw sensor data Sensor event Context itates inclusion of several analytical algorithms as part of App 1 App 2 Action Action the sense-analyze-act pipeline for developing novel building App 1 App 2 App 1 App 2 energy management applications. Analysis Analysis Action Action Action Action Using our prototype implementation, we developed three concrete applications to demonstrate the utility of Open- Simple triggers/rules Context Inference Engine BAN for a range of applications based on our testbed build- Middleware Middleware Middleware ings: (1) disaggregating household appliance usage; (2) iden- tifying sprinkler usage violation from water meter data, and (3) forecasting hourly energy usage from smart meter data. S1 S2 Sn S1 S2 Sn S1 S2 Sn The primary contributions of our work are as follows: Sensors Sensors Sensors • We propose OpenBAN middleware architecture that (a) Primitive (b) Trigger based (c) Context based facilitates integrating distributed sensor data streams to infer rich context information about building op- Figure 1: Evolution of building middleware systems based on sup- erations using different machine learning algorithms. port for processing sensor data. (a) primitive middleware provides no support for sensor data analytics, (b) rule-based middleware • We validate the suitability of OpenBAN by deploying provides trigger-actions based on thresholds, and (c) context- it for three real-world energy management applications based middleware provides sophisticated analytics for inferring in our test-bed buildings. context from sensory data. • We release an open source implementation of Open- their corresponding applications. Since these sensor data BAN containing several analytical algorithms and a streams encompass several operational characteristics of a host of features pertinent to building applications. building (e.g., occupancy information, energy usage pat- Rest of the paper is organized as follows. In Section 2, terns), storing and thereafter analyzing them can help opti- we present the background details and motivation. We then mize building operations. Motivated by this, recent efforts present the system architectural details in Section 3. In have sought to redesign the legacy building management Section 4, we present the implementation details of the sys- systems for not only storing such rich sensory data, but, tem. In Section 5, we present the details of three representa- also enabling novel applications to process them. Examples tive energy management applications developed using Open- of such research systems are HomeOS [10], SensorAct [11], BAN and preliminary performance analysis. We present the BuildingDepot [13], and BOSS [12]. Most of these systems related work in Section 6. Section 7 outlines the future work provide new abstractions, in the form of RESTful APIs or followed by Section 8 concludes the paper. RPCs, for accessing the underlying distributed network of sensors, actuators, and their data. In addition to this, these 2. BACKGROUND AND MOTIVATION systems provide an application runtime environment for de- Modern commercial buildings often employ a Building veloping and executing novel applications that can access Management System (BMS) for managing their complex and process the sensory data [10, 11, 13, 14]. operations. A BMS consists of many subsystems includ- Such redesigned middleware systems for buildings, have ing, HVAC for indoor climate control; lighting systems for opened up opportunities for both researchers and developers ambient brightness; access control systems for security, and to experiment complex yet futuristic building control appli- smart meters (electricity, water and gas) for monitoring util- cations such as personalized HVAC control [14], occupancy ity consumption. Sensors and actuators in these subsystems prediction [5], and energy monitoring [15]. Although these are networked using standard protocols such as BACnet 1 applications are relatively complex, their processing pipeline and connected with their corresponding control applications is still similar to existing commercial BMS and HAS appli- through BMS software. Generally, a BMS is installed with cations: access raw sensory data, process it and perform a fixed number of closed-loop control applications which are the desired control actions. While these redesigned mid- accessible only by the facilities department. dleware systems provide abstractions for accessing