VET4SBO Level 3 module 1 - unit 2 - 0.009 en

ECVET Training for Operatorsof IoT-enabledSmart Buildings (VET4SBO)
2018-1-RS01-KA202-000411
Level: 3 (three)
Module: 1 Interdependencies of building operation
sub-systems
Unit 1.2 Cases

L3-M1-U1.2 Cases (uBACstac)
• By enabling products from different vendors to interact with one another,
interoperable building systems based on the BACnet open standard improve
building management, increase operational efficiency and flexibility, and hold
down service and expansion costs.
• The power of today’s BACnet-based systems continues to increase as the
number of interoperable devices increases.
• Aplicable platforms: Atmel ATmega, ARM Cortex-M, and Linux-based systems.
• uBACstac - BACnet Protocol stack for small devices. With uBACstac you can build
simple BACnet-enabled devices such as meters, humidifiers, thermostats and
other application specific controllers.
• It supports different profiles like B-ASC and B-AAC profiles.

• The uBACstac design is very flexible.
• The uBACstac can be run on a wide range of architectures,
ranging from bare-metal micro-controllers to real-time OSs to
general purpose OSs.
• It does not impose any restrictions on the platform: no threads
are required, no synchronizationprimitives (mutexes,
semaphores, etc.), and no memory allocation(malloc/new).
These facilities can be used with uBACstac, but the portable
uBACstac code does not use these.

• uBACstac features a highly effective implementation of BACnet protocol.
• Zero copy architecture all the way from ASN.1 encodersin an application to sending data to a
UART, and back. The PDU payload is never copied or moved.
• State of the art MS/TP implementation with optional fully backward-compatible extension to
the BACnet standard allowing a window size of one on an MS/TP network.
• Low-level receive/transmit UART functions are fully decoupled from the portable MS/TP FSM
implementation, allowing the most effective implementation for a platform (DMA/ISR, even a
daughter micro-controller).
• The uBACstac is compatible with lock-free implementation. The libraries do not explicitly use
any synchronization primitives, so they are compatible with any synchronization
implementation.

uBACstac features:
• A small-footprint BACnet stack for small devices with or without OS. Implements
state-of-the-art MS/TP and BACnet/IP with Foreign Device.
• Provides truly portable code—the same core uBACstac library, the portable
MS/TP implementation and the example application run on all platforms
unmodified, including bare-metal microcontrollers and embedded OSs.
• Supports a wide range of processors, from entry level 8-bit AVR to powerful 32-
bit ARM7 and Cortex-M, or even more powerful 64-bit processors running Linux.

• Provides the stack in source code form, with example programs and reference ports
to a few hardware platforms, both with embedded OS and without OS. Also, for
ease of application development, includes a port to Linux and even Linux-on-
Windows as an "instrumental" platform.
• Implements a modular design, with clean separation between platform-dependent
and portable code.
• Provides highly configurable source code: unwanted features can be turned off,
decreasing the executable memory footprint.
• Makes it possible to implement a device conforming to the BACnet B-SS, B-SA, B-
ASC, and B-AAC profiles, optionally with COV notifications and more.

L3-M1-U1.2 Cases (measurement system)
• System that includes sensors that took measures for temperature, movement,
light and moisture with the aim to achieve a better management of the building
and also to make the building “smart” and efficient.
• Communication between the various sensors that can be installed in the building
and the Cloud Server with the users.
• The users would have remote access to sensors’ data, and they will be able to
make some actions.
Wireless Sensor Network Architecture for Smart Buildings, VenkataReddy Adama

• The topology of the network would be hybrid, relaying on star and mesh topologies. This could
offer a reliable network, easy to be managed in error detecting and troubleshooting.
• The mesh topology which already is and will be more popular in the future provides many
benefits.
• One of these benefits is the tolerance that it has in errors. The star topology, which is widely used
in home networks, provides also fault tolerance but since the middle connection point is working
properly.
• The installed Cloud Server would operate autonomouslyby using a voltage stabilizer (UPS) to
avoid any problem.

• All the users could easily connect to the network through the Wi-Fi
connection of the building and remotely through their mobile
providers.
• The installed network will support the communication protocol IPv6
and a Network Adaptive Multisensory Real-time Transmission
Protocol (NAMRTP).
• This protocol can transmit from the remote environment to the
database real-time multisensory data in a reliable way.

• As a large number of devices may be placed at different locations throughout the
building, connecting them using wires, e.g., by Ethernet, is often not practical.
• The IEEE 802.11 wireless LAN standard solves the problem of laying wires, but
the energy consumptionof the radio transceiver makes it infeasible to operate
devices on batteries.
• This problem is adressed by employing a wireless sensor network based on the
IEEE 802.15.4 physical layer standard which is optimized for energy efficient
communicationwith low data rates.
• The 6LoWPAN header compression scheme allows to efficiently send IPv6
packets over IEEE 802.15.4-based networks.

• Auto-configurationof network nodes is a mandatory feature when managing
large networks.
• The statelessaddress auto-configuration mechanisms provided by the IPv6
protocol.
• After startup, a node sends a router solicitation request to the link-local multicast
address. A router will respond with a router advertisement message containing
configuration parameters for the current network. Otherwise, a sensor node can
obtain its IPv6 address by issuing a DHCP request.
• Once the address configuration phase has been completed, a node starts to
advertise its offered services to the other nodes in the network. We use the
Multicast DNS (mDNS) protocol [1] proposed by Apple which is also known as
Bonjour (formerly Rendezvous).

• mDNS uses special DNS resource records to provide information
about services offered by a device such as the service type, domain
name and additional configuration parameters.
• A node can query a certain service type by sending a DNS packet to
the link-local multicast address.
• Nodes which offer services of the corresponding type will send an
answer. mDNS is implemented in many existing network peripherals,
e.g., printers, scanners and for different operating systems (currently
mainly Mac OS and Linux).

• Web services allow the interaction between different devices over
the network in order to exchange data or to trigger certain actions.
• Data is exchanged between peers using the Hypertext Transfer
Protocol (HTTP).
• Traditional enterprise solutions tend to use the Simple Object Access
Protocol (SOAP) together with XML for the data representation.
Recently, a more lightweight solution called Representational State
Transfer (REST) has been proposed which is built on top of the GET,
POST, PUT and DELETE methods of HTTP.

• Sensor nodes are responsible to provide information about their
sensors and actuators to the central monitoring server.
• One possibility to guarantee that the server has up to- date
information is that the sensor node periodically sends status
• messages to the monitoring server.
• An alternative approach would be that the server polls the state of
the sensors according to demand.

• While the rest approach is preferable if no actuators are connected to
the sensor node, the second variant offers more flexibility since the
same interface can be used to read out the state of a sensor as well
as to modify the state of actuators.
• Our approach implements a RESTful Application Programming
Interface (API) on sensor nodes to provide access to sensors and
actuators through the Web.

L3-M1-U1.3 Demonstrations through existing
platforms

platforms
https://www.monnit.com/

platforms
The FIPA Agent Management Reference model
McArthur, S. D. J and Davidson, E. M. and Catterson, V. M. and Dimeas, A. L. and Hatziargyriou,
N. D. and Ponci, F. and Funabashi, T. (2007) Multi-agent systems for power engineering applications -
part 2: technologies, standards and tools for building multi-agent systems. IEEE Transactions on Power
Systems, 22 (4). pp. 1753-1759. ISSN 0885-8950 OPEN ACCESS

platforms
Class hierarchy of part of an
upper ontology based on CIM

platforms
Agent design methodology stages and their output, used during the
design of the PEDA system
Layered architecture employed by JADE agents

L3-M1-U1.4 Costs analysis
• A fundamental breaktrough is occurring in building controls,
driven by a new generation of powerful Internet of Things
(IoT) devices.
• These networked sensors and controls can be quickly and
cost-effectively deployed in commercial buildings and
industrial sites to generate large amounts of valuable data
for analytics and automation.

• Building managers benefit from this granular new data and
control capability with the ability to fine-tune processes at a
greater number of building systems to lower energy and
maintenance costs.
• The resulting savings can provide compelling return on
investment (ROI), even for buildings under 100,000 square
feet.

• Building management systems (BMS) or building automation systems
(BAS) are the traditional solution to addressing the problem of energy
waste.
• The average cost to deploy a basic BMS is about $2.30 per square
foot, equivalent to $250,000 for a 100,000 square foot building.
• This cost means low ROI is a challenge, which limits most BMS
deployments to the major subsystems, such as HVAC and lighting in
high-traffic areas, and of only the largest buildings, over 100,000
square feet.

• BMS is rarely deployed into buildings under 100,000 square
feet, which constitute about 90% of the total building stock in
the U.S.
• Even in the 10% of larger buildings, BMS often isn’t used in
low-traffic areas such as warehouses, stockrooms, or garages,
or to distributed equipment such as pumps, generators, or
parking lot lights on campuses and industrialsites.
• Hundreds of millions of square feet of real estate and millions
of remote equipment assets are not monitored or managed at
all for energy or operational savings.

• Solutions are now arriving in the form of IoT-generation
connected devices.
• Advancements in sensor and controls technology now enable a
new wave of advanced, non-invasive, cost-effective, and quick-
to-install products.
• Because properly-deployed and connected IoT products can
overcome the capital barriers of installing traditional BMS, a
vanguard applicationfor these devices is for energy
management in buildings and remote equipment.

• For the first time, these new products can be cost-effectively
deployed by non-specialized personnel and extend the reach of
existing BMS systems,
• or even begin to replace BMS in mainstream applicationsin
under-100,000 square foot buildings.
• The “big data” these new IoT devices generate can be gathered
into cloud-basedmanagement and analytics services via
existing networks, and the devices can be easily monitored and
controlled by facilities managers via smartphones and tablets.

• Focusing on HVAC, lighting, and some types of
electrical loads, it is reasonable to expect savings in
the range of 10% to 25% when implementing proactive
energy management programs in mid-sized buildings.
Source:
https://www.iotone.com/

• Adding IoT-based controls and monitoring to a building can
cost from just $5,000 to $50,000, which is a fraction of
traditional BMS costs.
• The process typically requires a systems integrator or in-house
electrician and IT network professional.
• An energy engineering specialist is recommended to analyze
the data and make recommendations on process optimization
and automation in order to maximize savings.

• Focusing on HVAC, lighting, and some types of electrical loads, it is reasonable
to expect savings in the range of 10% to 25% when implementing proactive
energy management programs in mid-sized buildings.
• For a typical 75,000 square foot building, this equates to an annual potential
savings of $15,000 to $50,000 per year.
• Some buildings can save over $100,000 annually, and ROI can occur in six months
to two years. Beyond the pure monetary savings, additional benefits related to
sustainability and environmental stewardship can also be realized, with detailed
data to support them.

Solving the interoperability challenges
• Perhaps the biggest challenge to providing cost-effective, high-ROI energy
management to mid-sized buildings has been the lack of interoperability
between devices.
• The interoperability problem is compounded by the need to interconnect both
legacy equipment in the buildings
• There are dozens of protocol standards and literally hundreds of different
implementations just among the best-in-class of these devices.

Data Are Expensive to Acquire & Utilize
• The cost of sensors has dropped precipitously in recent years. In 2004,the
average cost per sensor was $1.30.In 2020,the average cost per sensor is
expected to be $0.38.Unfortunately, the cost reductions in sensors have not
resulted in a significant decrease in the cost of a full BMS installation.
• As of 2014,the cost to deploy a basic BMS was at least $2.50per square foot
and could be as high as $7.00 per square foot. While the cost of sensors has
plummeted, the cost of equipment controls has remained stubbornly high.

There May be Limited Value for Data
• Datahave little value on its own. A data set is only as good as the insights that
can be derived.
• In the case of BMS data, insights usually involve equipment schedules, set
points, and systemconfiguration optimizations.
• For example, by identifying that the HVAC system is running when the
building is unoccupied, a building can make significant reductions to
operating expenses through utility consumption.

• Likewise, highly granular data sets around startup and shutdown processes may yield
optimization insights for system configuration.
• There are plenty of examples of poorly configured building management systems that can
yield significant savings if optimized.
• However, this is much more likely to be the case in unique building types, such as hotels and
stadiums that have constantly varying occupancy rates and schedules. For office and
multifamily apartment buildings, which have relatively consistent schedules and occupancy
rates year-round, the BMS may already be close to optimized.
• While there is likely to be “performance drift” in any building type over time, the point is
that no amount of data will yield significant results if the system is already close to
optimized and rarely requires changes.

Scalability
• The final limitation of using BMS data to optimize a portfolio of buildings is the
inherent lack of scalability.
• Perhaps each building in the portfolio has the same BMS vendor, but that is highly
unlikely.
• Each vendor is going to have its own proprietary data protocol, which requires a
developing and maintaining a number of different processes and integrations.
• Not only is this hard to manage and maintain, but the BMS vendors often have
competing products and thus are incentivized to make their data inaccessible to
third parties.

• While part of the difficulty of extracting BMS data may be competitive, there is also
a legitimate concern about security.
• Building data is not only valuable to competitors, because it is tied to controls of
building equipment, it can be extremely dangerous to occupancy if manipulated.
• There is also the consideration that a portfolio will contain a mix of properties with
and without building management systems installed.
• Leveraging BMS data to find operational waste may be effective in portions of the
portfolio, but a separate solution will be required for the buildings without a BMS.
• This requires an additional round of diligence and technology evaluations that will
slow down rollout and stifle scalability. The point is, relying on BMS data to drive
operational improvements will necessarily run into scalability issues.

Thank you for your attention.
https://pixabay.com/illustrations/thank-you-polaroid-letters-2490552/

Disclaimer
For further information, relatedto the VET4SBO project, please visit the project’swebsite at https://smart-building-
operator.euor visit us at https://www.facebook.com/Vet4sbo.
Downloadour mobile app at https://play.google.com/store/apps/details?id=com.vet4sbo.mobile.
This project (2018-1-RS01-KA202-000411) has been funded with support from the European Commission (Erasmus+
Programme). Thispublicationreflects the views only of the author, and the Commission cannot be held responsible
for any use which may be made of the informationcontainedtherein.

VET4SBO Level 3 module 1 - unit 2 - 0.009 en

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