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    Building Management Systems

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    Nadine Cranenburgh

    Introduction

    The concept of IoT Building Management Systems (BMS) as a service is poised to change the building industry. As the price of internet connected sensors comes down, a large number of sensors can be placed in a building to provide multiple data points connected to advanced cloud based analytical systems. This delivers superior BMS performance to traditional engineering approaches.

    Building owners own their data, while allowing service providers to help them optimise the efficiency and sustainability of their facilities. This approach also facilitates auditing of the actual performance of building management systems during the critical Defects Liability Period.

    Traditional vs IoT BMS

    The purpose of BMS is to achieve sustainable buildings and cities. They should increase efficiency, resilience, security and productivity, as well as reducing environmental impact.  They may also incorporate intelligence (as in smart cities) and have the capacity to detect and fix damage.

    Traditional BMS were pioneered several decades ago. They started the process of automated control and data collection. A diagram of a traditional BMS is shown below.

    5aa876dfed0a8_TraditionalBMS.jpg.29b096b579e5c15843844a522a44e516.jpg

    Diagram courtesy of Bob Sharon, Blue IoT

    Traditional BMS were often proprietary systems. They were expensive to purchase, and modifications to data extraction rules or reporting functions (“steering wheel options”) were also costly. This meant that many BMS owners did not use their systems to their full potential.

    Other challenges included the long lead times to make changes to the system, the high cost of the cabling to connect extra sensors, specialist programming services required (also costly), and limited alarm complexity without blowing out the budget.

    Data extraction and report customisation was typically complex and expensive, as was integrating additional data sets from additional systems or devices. And in many cases, the BMS vendor owned the data.

    A diagram of an IoT BMS is shown below.

    BMS.jpg.0d339348490aaec376f77bd17337faab.jpg

    Diagram courtesy of Bob Sharon, Blue IoT

    IoT BMS solved many of the issues of traditional BMS systems through open system architectures, wireless technology instead of cabling, increased agility and integration, and reduced operation, modification and maintenance costs.

    A general comparison between traditional and IoT BMS is shown in the table below. One comment is that some tradition BMS are also starting to become more open.

    comparison.jpg.94cb3e344d0af09e88b4e3b32f29e9f3.jpg

    Diagram courtesy of Bob Sharon, Blue IoT

    Democratisation of data is another advantage IoT BMS have over traditional systems. Open source platforms where the client owns the data allow system modifications to be easily made, and the client to change vendors to meet their service requirements. This trend is set to increase in BMS and other IoT applications.

    The transition of BMS from traditional to IoT systems is still progressing. So for mission critical applications it may be advisable to use an open source traditional BMS with two-way communications and control form the cloud, with the option to shift to complete cloud operation as the technology matures.

    Architecture considerations for IoT BMS

    Considerations when choosing the data aggregation and IoT architecture for an IoT BMS include:

    • Which protocols should connect the sensors and IoT platform?
    • What form of communications technology best suits the application (eg Zigbee, wifi 802.x, Sigfox, Bluetooth low energy (BLE) and LoRaWAN?
    • How will your application engage with the cloud?
    • Who will own the application data (vendor, building owner, users of devices)?
    • Is an open or closed architecture most suitable?

    It is also recommended that a highly resilient (tier 3 or tier 4) data centre is used for BMS to ensure that data management meets requirements.

    Sensors and Predictive maintenance

    One of the problems with traditional BMS is the cost of adding additional sensors, which means that the minimum number is used. With IoT BMS, this cost is greatly reduced, which opens up opportunities for a wide range of data collection to be integrated. It is important to pay attention to data calibration and validation, to ensure that high quality, accurate data is collected.

    A diagram of some of the sensors which could be used in an IoT BMS is shown below.

    5aa87a70ed32f_BMSsensors.jpg.9b9201d44306a5f3ed074c1a07ca2fab.jpg

    Diagram courtesy of Bob Sharon, Blue IoT

    Self-healing and predictive maintenance

    In particular accelerometers, vibration transmitters and switches can be used to monitor critical rotating machines, and perform predictive maintenance.

    For example, accelerometers can be used to measure vibration and measure the harmonics of motors. Through monitoring, faults can be fixed before they fail.

    Advanced machine learning tools will be invaluable for implementing self-healing machines that can dramatically reduce maintenance costs and risks of outages and out of hours maintenance.

    These cost reductions can offset the cost of installing an IoT BMS.

    Data analytics

    There are various data analytics platforms that can take data from thousands of sensors in disparate building management systems (over thousands of buildings if necessary) and create effective interactive analytics and visualisations for end users. This data can be interpreted by engineers and other experts to solve issues that are detected.

    Security

    Security of IoT BMS is crucial to ensure that hackers do not take control of the BMS or use it as a pathway to corporate networks, both of which can cause significant damage. A robust, holistic security architecture should be chosen, which implements security at every level including choice and security measures and levels for all of the following components of the BMS:

    • sensor hardware
    • communications protocol
    • cloud
    • IoT platform
    • gateways
    • cloud
    • data centre

    Other considerations are whether encryption is used, if AI is used to check for unwanted signatures, whether a mesh network being used for sensor communication (can introduce additional risks), which geographic locations the data is going to before it reaches the data centre (and associated risks vs timely transmission of data).

    While risks cannot be entirely eliminated, they can be greatly reduced with careful security planning and design.

    An example of how BMS security can be implemented using LoRaWAN is shown below.

    LORAWAN.jpg.e38c745103dab4ca30aeca18efdf4516.jpg

    Diagram courtesy of Bob Sharon, Blue IoT

    LoRaWAN has the advantage of being able to be encrypted, and the sensors are isolated. The data goes from the sensor directly to the gateway. From the gateway, it goes out over either 3G or 4G, or to another LoRaWAN base station, depending on the system design. This lowers the risk of hacking and additional AI layers can be added for further security. Two-way communication may also be available depending on the class of LoRaWAN used. This example is suitable for low bandwidth data.

    Sources:

    The content on this page has been primarily sourced from:

    See also the article of the same title in our discussion forum with some comments.

     

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