With the introduction of cloud technology and by extension the rapid emergence of Internet of Things (IoT), the barrier to entry for creating smart building solutions has never been lower. These solutions offer commercial real estate customers potential cost savings and the ability to enhance their tenants’ experience. You can differentiate your business from competitors by offering new amenities and add new sources of revenue by understanding more about your buildings’ operations.

There are several building management systems to consider in commercial buildings, such as air conditioning, fire, elevator, security, and grey/white water. Each system continues to add more features and become more automated, meaning that control mechanisms use all kinds of standards and protocols. This has led to fragmented building systems and inefficiency.

In this blog, we’ll show you how to use AWS for the Edge to bring these systems into one data path for cloud processing. You’ll learn how to use AWS IoT services to review and use this data to build smart building functions. Some common use cases include:

  • Provide building facility teams a holistic view of building status and performance, alerting them to problems sooner and helping them solve problems faster.
  • Provide a detailed record of the efficiency and usage of the building over time.
  • Use historical building data to help optimize building operations and predict maintenance needs.
  • Offer enriched tenant engagement through services like building control and personalized experiences.
  • Allow building owners to gather granular usage data from multiple buildings so they can react to changing usage patterns in a single platform.

Securely connecting building devices to AWS IoT Core

AWS IoT Core supports connections with building devices, wireless gateways, applications, and services. Devices connect to AWS IoT Core to send and receive data from AWS IoT Core services and other devices. Buildings often use different device types, and AWS IoT Core has multiple options to ingest data and enabling connectivity within your building. AWS IoT Core is made up of the following components:

  • Device Gateway is the entry point for all devices. It manages your device connections and supports HTTPS and MQTT (3.1.1) protocols.
  • Message Broker is an elastic and fully managed pub/sub message broker that securely transmits messages (for example, device telemetry data) to and from all your building devices.
  • Registry is a database of all your devices and associated attributes and metadata. It allows you to group devices and services based upon attributes such as building, software version, vendor, class, floor, etc.

The architecture in Figure 1 shows how building devices can connect into AWS IoT Core. AWS IoT Core supports multiple connectivity options:

  • Native MQTT – Multiple building management systems or device controllers have MQTT support immediately.
  • AWS IoT Device SDK – This option supports MQTT protocol and multiple programming languages.
  • AWS IoT Greengrass – The previous options assume that devices are connected to the internet, but this isn’t always possible. AWS IoT Greengrass extends the cloud to the building’s edge. Devices can connect directly to AWS IoT Greengrass and send telemetry to AWS IoT Core.
  • AWS for the Edge partner products – There are several partner solutions, such as Ignition Edge from Inductive Automation, that offer protocol translation software to normalize in-building sensor data.
Data ingestion options from on-premises devices to AWS

Figure 1. Data ingestion options from on-premises devices to AWS

Challenges when connecting buildings to the cloud

There are two common challenges when connecting building devices to the cloud:

  • You need a flexible platform to aggregate building device communication data
  • You need to transform the building data to a standard protocol, such as MQTT

Building data is made up of various protocols and formats. Many of these are system-specific or legacy protocols. To overcome this, we suggest processing building device data at the edge, extracting important data points/values before transforming to MQTT, and then sending the data to the cloud.

Transforming protocols can be complex because they can abstract naming and operation types. AWS IoT Greengrass and partner products such as Ignition Edge make it possible to read that data, normalize the naming, and extract useful information for device operation. Combined with AWS IoT Greengrass, this gives you a single way to validate the building device data and standardize its processing.

Using building data to develop smart building solutions

The architecture in Figure 2 shows an in-building lighting system. It is connected to AWS IoT Core and reports on devices’ status and gives users control over connected lights.

The architecture in Figure 2 has two data paths, which we’ll provide details on in the following sections, but here’s a summary:

  1. The “cold” path gathers all incoming data for batch data analysis and historical dashboarding.
  2. The “warm” bidirectional path is for faster, real-time data. It gathers devices’ current state data. This path is used by end-user applications for sending control messages, real-time reporting, or initiating alarms.
Figure 2. Architecture diagram of a building lighting system connected to AWS IoT Core

Figure 2. Architecture diagram of a building lighting system connected to AWS IoT Core

Cold data path

The cold data path gathers all lighting device telemetry data, such as power consumption, operating temperature, health data, etc. to help you understand how the lighting system is functioning.

Building devices can often deliver unstructured, inconsistent, and large volumes of data. AWS IoT Analytics helps clean up this data by applying filters, transformations, and enrichment from other data sources before storing it. By using Amazon Simple Storage Service (Amazon S3), you can analyze your data in different ways. Here we use Amazon Athena and Amazon QuickSight for building operational dashboard visualizations.

Let’s discuss a real-world example. For building lighting systems, understanding your energy consumption is important for evaluating energy and cost efficiency. Data ingested into AWS IoT Core can be stored long term in Amazon S3, making it available for historical reporting. Athena and QuickSight can quickly query this data and build visualizations that show lighting state (on or off) and annual energy consumption over a set period of time. You can also overlay this data with sunrise and sunset data to provide insight into whether you are using your lighting systems efficiently. For example, adjusting the lighting schedule accordingly to the darker winter months versus the brighter summer months.

Warm data path

In the warm data path, AWS IoT Device Shadow service makes the device state available. Shadow updates are forwarded by an AWS IoT rule into downstream services such an AWS IoT Event, which tracks and monitors multiple devices and data points. Then it initiates actions based on specific events. Further, you could build APIs that interact with AWS IoT Device Shadow. In this architecture, we have used AWS AppSync and AWS Lambda to enable building controls via a tenant smartphone application.

Let’s discuss a real-world example. In an office meeting room lighting system, maintaining a certain brightness level is important for health and safety. If that space is unoccupied, you can save money by turning the lighting down or off. AWS IoT Events can take inputs from lumen sensors, lighting systems, and motorized blinds and put them into a detector model. This model calculates and prompts the best action to maintain the room’s brightness throughout the day. If the lumen level drops below a specific brightness threshold in a room, AWS IoT Events could prompt an action to maintain an optimal brightness level in the room. If an occupancy sensor is added to the room, the model can know if someone is in the room and maintain the lighting state. If that person leaves, it will turn off that lighting. The ongoing calculation of state can also evaluate the time of day or weather conditions. It would then select the most economical option for the room, such as opening the window blinds rather than turning on the lighting system.

Conclusion

In this blog, we demonstrated how to collect and aggregate the data produced by on-premises building management platforms. We discussed how augmenting this data with the AWS IoT Core platform allows for development of smart building solutions such as building automation and operational dashboarding. AWS products and services can enable your buildings to be more efficient while and also provide engaging tenant experiences. For more information on how to get started please check out our getting started with AWS IoT Core developer guide.