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OpenCity: An Open Architecture Testbed for Smart
Cities
Nasibeh Zohrabi1,Patrick
J.
Martin2,Murat Kuzlu3,Lauren Linkous2,Roja Eini2,Adam Morrissett2,
Mostafa Zaman2,Ashraf Tantawy2, Oezguer Gueler5,Maher Al Islam2,Nathan Puryear2,
Halil Kalkavan4,Jonathan Lundquist2,Erwin Karincic2,Sherif Abdelwahed2
1Department
of
Engineering, Pennsylvania State University, Media,
PA
19063, USA
2Electrical and Computer Engineering, Virginia Commonwealth University, Richmond,
VA
23284, USA
3Electrical Engineering Technology, Old Dominion University, Norfolk,
VA
23529, USA
4Department
of
Computer Science, Old Dominion University, Norfolk,
VA
23529, USA
5eKare, Inc., Fairfax,
VA
22031, USA
nmz5171 @psu.edu, martinp@vcu.edu, mkuzlu@odu.edu, linkouslc@vcu.edu, einir@vcu.edu, morrissettal2@vcu.edu,
zamanm@vcu.edu, amatantawy@vcu.edu, oguler@ekareinc.com, alislarnm@vcu.edu, puryearna@vcu.edu,
hkalkOOI@odu.edu, lundquistjd@vcu.edu, erwink@vcu.edu, sabdelwahed@vcu.edu
Abstract-This
paper
presents
an
open architecture testbed for
smart
cities, called OpenCity, which is hosted
at
Yirginia Com-
monwealth University (YCU).
The
OpenCity platform consists
of
data
collection
and
processing units, database management,
distributed performance management algorithms,
and
real-time
data
visualization. This
smart
city testbed aims to
support
various educational
and
research activities related to
smart
city
development. The testbed provides anear-real-life platform to
allow students to
learn
about
the unique features
of
smart
cities
and
explore supporting technologies. In addition,
it
allows
researchers to develop, deploy,
and
validate new techniques,
tools,
and
technologies to
support
future
smart
city developments.
The
OpenCity platform will
support
various ongoing
important
research directions in
smart
cities, including
smart
homes
and
buildings,
urban
mobility,
smart
grid,
and
water
management.
In addition, it will be extendable to include
other
potential
applications
and
components as needed.
The
testbed will be
validated by developing
and
deploying amanagement system
that
focuses on users' experience
and
resource efficiency. The
management system incorporates learning techniques
and
model-
based predictive control approaches to take into account the
current
and
future information
of
uncertain
parameters
as well
as the subjective
data
(e.g., user-related data) in the design. The
OpenCity management
structure
enables real-time control
and
monitoring
of
complex components in the testbed.
Index
Terms-Smart
City, OpenCity,
Data
Analytics,
Smart
Buildings, Intelligent Transportation Systems
I.
INTRODUCTION
More than
50
percent
of
the world's population currently
reside
in
urban areas
[1],
and by 2050, two-thirds
of
the
human population are predicted
to
live in urban areas
[2].
It
is
envisioned that the Internet
of
Things (loT) will provide
areal-time information stream for city managers and public
service providers to better understand the strain placed
on
the city's infrastructure and resources. The loT
is
anetwork
of
physical devices (e.g., home appliances, medical sensors,
vehicles) embedded with sensing, actuation, computing, and
communications capabilities that enable distributed monitor-
ing and control to reach common goals.
By
building smart,
loT-based services that serve human users, smart cities will
enhance the safety, wellness, and quality
of
life for their
citizens
[3].
In 2015, the
US
Government invested over $160 million
in
the "smart cities" initiative
to
improve the quality
of
life, eco-
nomic competitiveness, and sustainability
of
future cities
[4].
This initiative involves anumber
of
fundamental research
ar-
eas, such
as
smart buildings
[5], [6],
smart grid
[7],
intelligent
transportation systems
[8],
smart water/waste management
[9],
smart healthcare [10], and cybersecurity [11]. One important
research challenge
is
the development
of
assured, data-driven
management systems that control and monitor the city's oper-
ations
in
asafe, secure, and reliable manner.
Smart cities are complex systems-of-systems that contain
amultitude
of
hardware and software platforms.
To
better
understand realistic, emergent behavior, atestbed that emulates
several core smart city systems
is
needed. This paper intro-
duces asmart city testbed, called OpenCity, which
is
hosted
at Virginia Commonwealth University (VCU). OpenCity will
provide amore cost-effective way for researchers to develop
technologies and techniques to deliver optimized programs and
services to smart city residents. The testbed will establish a
standards-based framework and aphysical, real-time exper-
imental environment to develop and evaluate new decision-
making and system management solutions.
In recent years, several other smart city and loT testbeds
were developed. For example, Smart Santander
[12]
imple-
mented afull scale testbed that emphasizes mobile device
management and data collection. The FIT loT Lab
[13]
is
deployed across multiple buildings and institutions.
It
provides
users the ability to develop and deploy mobility applications
at internet and city scale. Another recent testbed, City
of
Things [14], integrates multiple radio and protocols to enable
researchers to experiment with city scale data and network
management. The key difference between OpenCity and these
platforms
is
the facilitation of integrated, command and con-
trol of smart city resources. For example, due
to
regulatory
limitations, it
is
not possible
to
perform full scale tests
978-1-6654-4919-9/21/$31.00 ©2021 IEEE
2021 IEEE International Smart Cities Conference (ISC2) | 978-1-6654-4919-9/21/$31.00 ©2021 IEEE | DOI: 10.1109/ISC253183.2021.9562813
Authorized licensed use limited to: Virginia Commonwealth University. Downloaded on November 30,2021 at 05:57:16 UTC from IEEE Xplore. Restrictions apply.
of
autonomous mobility applications; therefore, we require
lab-scale testbeds to develop and evaluate decision support
systems.
The main contribution
of
the OpenCity platform is to pro-
vide an accessible experimental environment for researchers
to
explore algorithms and technologies that will make intelli-
gent, sustainable cities. This new testbed relies on loT-based
communication technologies, autonomous systems, software
development, and database management to address the issue
of
plug-and-play, scalability, and interoperability. The project
team will also develop aset
of
distributed management con-
trol algorithms that supports decision-making processes for
efficient smart city operations. The proposed platform will
allow researchers to collaborate and share data
as
well
as
grow acommunity
of
smart city enthusiasts that contributes
to innovative smart city applications.
In the OpenCity platform, we focus on different applications
to smart cities, including smart homes and buildings, and
smart transportation systems. Adata-driven management struc-
ture will be developed that supports autonomous and semi-
autonomous operation
of
smart city infrastructure. Addition-
ally, the proposed management structure will allow researchers
to learn how to incorporate various human aspects into the
management structure. This testbed supports educational and
research activities by engaging with government and industry
partners to accelerate understanding
of
analytics and smart
technologies and transition research into real-life solutions.
The rest
of
the paper is organized as follows. Sections II and
III introduce the OpenCity design layout and communication
architecture. Section IV presents the design architecture
of
OpenCity intelligent transportation system. Section Vpresents
the design considerations for four different types
of
OpenCity
smart buildings. Section VI presents the OpenCity manage-
ment architecture. Finally, the conclusion is given in Section
VII.
II.
OPENCTTY
HIGH
LEVEL
DESIGN
The OpenCity platform includes the subsystems illustrated
in Fig.
1:
smart residential and commercial buildings, intelli-
gent transportation system, as well as the associated communi-
cation backbone and data analytics infrastructure.
All
entities
in
this smart city model are assumed to have
an
intelligent
node that performs local sensing and control, or optionally
uses control signals issued from edge or cloud management
systems.
The goal
of
this testbed
is
to provide asmart city experi-
mental environment that includes
a)
data collection, processing
and analytics, b) an loT communication network, c) real-
time data visualization, and d) aconfigurable and extendable
management system. Fig. 2illustrates an OpenCity "block"
that has four buildings placed in atwo by two grid and
surrounded on all sides by roads. Afour-way intersection
featuring multiple traffic lights runs through the center. These
modular blocks may be connected with others to construct
alarger city grid. The OpenCity block includes four distinct
buildings:
a)
Residential, b) Commercial office, c) Hospital,
and
d)
Water treatment plant. The transportation system will
operate autonomous vehicles, road sensors, and traffic signals
that may communicate with OpenCity infrastructure. The fol-
lowing sections describe the features and design considerations
of
each OpenCity subsystem.
Fig. I:
An
illustration
of
atypical Smart City computing
architecture.
Fig.
2:
The OpenCity platform design featuring four buildings,
intersecting roads, and peripheral features for testing the
intelligent transportation system.
III.
OPEN
CITY
COMMUNICATION
ARCHITECTURE
The OpenCity architecture, illustrated in Fig.
3,
consists
of
decentralized software services and cyber-physical entities
that are bound together with acommon message transport
protocol, Message Queuing Telemetry Transport (MQTT)'.
The MQTT protocol is an OASIS standard and is heavily
used in the commercial sector for smart city applications.
The loose coupling
of
all systems with acommon publica-
tion/subscribe messaging system allows OpenCity to scale and
provides additional flexibility as new experimental platforms
are incorporated into the testbed. This architecture emulates
the smart city concept in Fig.
1,
and promotes loose coupling
1https://mqtt.org/mqtt-specification
Authorized licensed use limited to: Virginia Commonwealth University. Downloaded on November 30,2021 at 05:57:16 UTC from IEEE Xplore. Restrictions apply.
/Server@OpenCity
"
.--
--
----
--
---- ----
--
---- ------ ---- ------ ----- ----- ----- ----- -----
--
----
--~
.--------------------,
External
System Services (
Web
Services >
Users
I [ Databases ]
[I
Device
t]
[Alarms and Ji: : .
Management Notifications ::
[userAccess]~~tII>~
i[M
MQTT
t]
~~
~~:
l.
[Visualization]
:J
'.
anagemen :
"-._---------
-------------------------- --------------------------
------_/
',------------------_.'
Protocol Command Sensor
Maintenance and Control Data
M
IT
Inter-Domain Messages
~~~
...
~~
~~~
...
~~
I
Intra-Domain
Messages
It
Intra-Domain
MeSSages:
(0
-------
-----------------
-------,
DDS
~
__________
In!~r!)C;!I..P_l"QtPQQI
__________
~I
Transportation
Systems
Building
Systems
Nodes@OpenCity
Fig.
3:
OpenCity's system-of-systems architecture.
among the different communication domains and testbeds
using the MQTT protocol. Each main part
of
the architecture
is discussed below.
1) Server@OpenCity: The Server@OpenCity hosts aweb
server, databases and several services that enable command,
control, and sensing
of
the smart city platforms. The web
server provides real-time visualization and aweb-based user
interface. OpenCity testbed researchers and students will use
this interface
to
query or visualize data collected by the
OpenCity server. Data collected from nodes are stored on the
database servers with NoSQL databases. Server@OpenCity
provides device management, control, alarm and notifications,
and analytics services. It also hosts aNode-RED service
as
adevelopment tool to support the connection and data
processing
of
deployed loT platforms within OpenCity.
2) Node@OpenCity: The other domains
of
the smart city,
e.g., transportation and buildings, are managed by individual,
low-power computing nodes, such
as
Raspberry Pis. Each
OpenCity node, called Node@OpenCity, maintains its local
data and communicates with other nodes on its chosen in-
ternal protocol. For example, the transportation system uses
aDDS-based protocol to coordinate among transportation
agents; however, buildings may also use MQTT for internal
coordination.
To
formalize acommon set
of
MQTT message
payloads across OpenCity, we define and implement all data
types with Google Protocol Buffers2.Using this approach,
each message is formally defined and compiled into multiple
implementation languages, allowing implementation flexibility
within each OpenCity sub-domain. This architecture allows
developers
to
choose the communication infrastructure that
best suits adomain's needs.
2https://developers.google.com/protocol-buffers
IV.
OPENCTTY
INTELLIGENT
TRANSPORTATION
SYSTEM
The OpenCity transportation system comprises devices rel-
evant to intelligent transportation use cases: autonomous ve-
hicles, road sensors, and traffic signals. The transportation
leverages the Robot Operating System version 2(ROS2) to
maximize code re-use and ease algorithm integration into all
agents within the transportation infrastructure. This framework
provides modern robotics and autonomy software program-
ming and networking abstractions. Furthermore, more recent
industry platforms, e.g., AutoWare3and CARMA4,use the
ROS2 framework to implement their core autonomy stack.
Fig. 4illustrates the transportation system's network
ar-
chitecture and its interconnection to the OpenCity testbed.
ROS2 provides the communication fabric among transporta-
tion agents over the OpenCity's WiFi network. Communication
to and from the OpenCity core server and other non-ROS2
based infrastructure is facilitated by bridging the autonomous
systems to OpenCity's MQTT network. This network structure
facilitates the management, decision support, and command
services that reside on the testbed's more capable servers.
The OpenCity intelligent transportation system design has
two subsystems: vehicles and traffic signals. These intelli-
gent agents communicate and coordinate amongst each other
when running local autonomy algorithms; however, the MQTT
bridging mechanism enables centralized control use cases.
The prototype intelligent transportation architecture is tested
on two
of
our experimental platforms: amodified Waveshare
PiRacer (Fig. 5a) and aRaspberry Pi based traffic signal
module (Fig. 5b). Like their real-world counterparts, these
platforms are equipped with avariety
of
sensors. The Piracer
uses the Raspberry Pi Camera Module for its environment
3https://www.autoware.auto/
4https://highways.dot.gov/research/operations/CARMA-products
Authorized licensed use limited to: Virginia Commonwealth University. Downloaded on November 30,2021 at 05:57:16 UTC from IEEE Xplore. Restrictions apply.
Part Usage
Raspberry Pi 4Model BBuilding Control and Management
Raspberry Pi Zero WSensor/Actuator Control and Management
Micro servo Door and Window Movement
DHT22 Sensor Temp and Humidity Monitoring
MQ-2 Gas Sensor Flammable Gas and Smoke Sensor
Light Sensor Light Level in Building
Passive Infrared (PIR) Motion Sensor
IR LED
PlR
Trigger /Emulate Human Presence
Raspberry Pi Camera Building Security and Monitoring
Electric Heating Pad Individual Room Heating
5V Cooling Fan Small Fan for Cooling
RGB LED strip Lighting in Buildings
Water Level Sensor Tank Water Level and Leaks
buildings from all sides. The building framing uses T-slot
aluminum that allows for the horizontal and vertical expansion
of
buildings. Our current designs are between three and
five floors; however, the T-slot aluminum framing can be
adjusted for any number
of
floors. As shown in Fig.
6,
layers
of
plywood are mounted to the aluminum with brackets at
regular intervals for the floors. The pictured design features an
additional T-slot aluminum piece and holes cut in the plywood
for an elevator. All internal walls for the buildings will be
made
of
plywood and are designed to be adjustable so that
floor plans can be rearranged
as
needed.
Table Ilists some
of
the important components used in
the buildings, which are selected based on several criteria:
compatibility with popular micro controllers and computers
(e.g., Raspberry Pi or Arduino); low-cost; low-power; and
ability to be used across multiple types
of
buildings. Actuators
that cannot be powered off
of
the 5V circuit will be connected
to aseparate 12V circuit and operated with aRaspberry
Pi controlled mechanical relay. The rest
of
the sensors and
actuators will be wired to Raspberry Pi Zero WGPIO pins to
collect data and send signals to actuators, e.g., for controlling
the angle
of
the micro servos for opening or closing windows.
Data collected on the Pi Zero Ws will be wirelessly transmitted
to the Raspberry
Pi
4Model Bthat acts
as
the building
manager. This building manager interfaces with the OpenCity
MQTT communication network to send data packets to the
server for storage and processing, or receive commands to
pass on to the Pi Zero W
s.
TABLE
I:
Buildings component list
Fig.
6:
Smart building internal structure.
----... ROS 2Comms.
-
.....
MQTT Comms.
MQTT Node
DROS 2Node
(b)
ROS 2Network (Domain 2)
,
't'
~r~~------~
,
,
(a)
,------.
,
,
,
ROS 2Network (Domain
1)
Transportation System
Network Architecture
Fig.
5:
The transportation system's hardware platforms.
Fig.
4:
High-level communication architecture for the trans-
pOltation system.
perception and aRaspberry
Pi
4for its computation. Fig. 5b
shows one
of
the traffic signal prototypes for the transportation
testbed. It uses aRaspberry Pi 4
as
the controller and the
PiTraffic hat for the LED stacks.
V.
OPENCITY
INTERACTIVE
BUILDING
SYSTEM
In the United States, residential and commercial buildings
account for 40%
of
the total energy use, 70%
of
the electricity
use, 36%
of
the total greenhouse gas emissions, and 12%
of
fresh water consumption. Aproper building control framework
can help reduce up to 30%
of
energy costs [15]. Moreover,
since Americans spend 90%
of
their lives in buildings [16],
it is clear that an efficient building management system can
save time, money, and energy. Intelligent buildings would
learn occupants' energy needs, integrate renewable energy and
flexible loads into their energy forecasts, respond to changing
weather conditions and uncertainty in renewable outputs [17].
While energy management products for asingle home
or building are available today, development
of
real-time,
networked control
of
multiple buildings is still
in
its infancy. In
the OpenCity testbed, smart buildings integrate various tech-
niques and technologies to create afacility that is safer, more
comfortable, efficient, and cost-effective for its occupants. This
testbed would allow city planners to understand interaction
among building energy consumers, building schedules, and
comfort requirements.
Fig. 6shows the internal structure
of
the building
in
the
VCU OpenCity testbed. This modular design has removable
facades that allow access to the internal components
of
the
Authorized licensed use limited to: Virginia Commonwealth University. Downloaded on November 30,2021 at 05:57:16 UTC from IEEE Xplore. Restrictions apply.
ical Device security lab are: Connected ultrasounds, patient
monitors, ventilators, and IV pumps.
Fig.
7:
Asimplified process flow diagram for the water dis-
tribution system. Details for the office and hospital buildings
are the same
as
the residential building and omitted to avoid
cluttering the diagram.
From hospital
TK-R
P-02
From Office building
To
Business building Supply Tank
To
hospital Supply Tank
Residential Supply Tank
C.
Water Distribution System
The water distribution system supplies faucet water to the
three key buildings
in
the smart city: the residential building,
hospital, and business building. Fig. 7is asimplified process
flow diagram for the system, showing only the residential
building details. The water distribution system comprises a
supply tank, aVariable Frequency Drive (VFD) driven pump,
water distribution piping, and control valves that represent user
consumption. The consumed water is directed to asingle return
tank that serves the three buildings. The return tank is equipped
with an on/off pump that circulates the returned water to the
three buildings' water supply tanks, comprising aclosed-loop
circulation system.
The system
is
equipped with the necessary instrumentation
to measure tank levels, pump discharge pressures, and the
flow rate at each floor. The speed control
of
each supply tank
pump enables the control
of
the hydraulic system operating
point (pressure, flow) under avariety
of
user consumption
profiles. The testbed will be used to assess and compare the
performance
of
several control techniques, including classical
feedback control, feed-forward control, and reinforcement
learning.
The system is designed to allow implementing and test-
ing both centralized and distributed control designs. Each
actuator, e.g., apump or avalve, has its own embedded
controller that has local wireless communication with the
relevant sensors. This provides more flexibility in adopting
advanced control techniques, e.g., feed-forward control by
reading user consumption valve position sensors.
To
safe-
guard against system safety hazards, e.g., over-pressure, an
independent safety system is utilized with its own sensors to
provide an additional layer
of
protection. The overall system
is connected to the smart city cyber infrastructure via MQTT
broker communication.
B.
Hospital Building
The hospital building will have the same sensors and servos
as
the residential and office buildings. Hospital buildings have
designated bathrooms, waiting, diagnostic, and triage rooms.
This building integrate with VCU's Medical Device Security
lab. It will be possible to query data through this building
and receive data collected from real medical devices. The
Medical Device Security lab has three focal points in working
to improve security in commercial and home healthcare en-
vironments: Offensive security and penetration testing, FPGA
based secure
by
design systems, and secure loT and sensing
applications.
All
digital network communication between the OpenCity
lab and Medical Device Security lab will occur through
the Medical Device Security Lab firewall using RSA-4096
public/private key authentication. Inbound and outbound con-
nections between the labs will only be allowed to and from
the dedicated OpenCity loT VLAN, which will be for sensing
and loT devices that communicate with OpenCity's hospital
building. This structure ensures the safety
of
both the OpenCity
and Medical Device Security labs and the integrity
of
the
penetration testing and development environments within the
Medical Device Security Lab.
loT
devices and sensors may live within the OpenCity
hospital building or within the Medical Device Security Lab.
Linux based virtual machines or devices communicate be-
tween the two labs using keyed authentication.
It
would be
advantageous to put industrial grade and realistic size sensors
in the medical device security lab on the
loT
network. An
example experiment may be asensor placed in hospital beds
that reports usage data back to the smart city for purposes,
such
as
indicating hospital capacity to emergency services.
The lab could also house standard building sensors that feed
back into the smart city lab.
Sensor data collected within the Medical Device Security
Lab and output from medical devices will be used to build
digital simulations
of
this equipment within the OpenCity
hospital building. These simulations can be updated
as
new
device behavior
is
discovered, to include vulnerabilities found
within the devices themselves where applicable to the Smart
City Research. Examples
of
existing equipment in the Med-
The following subsections describe OpenCity's four distinct
buildings:
a)
Residential building, b) Commercial office build-
ing, c) Hospital building, and
d)
Water treatment plant.
A. Residential
and
Office Buildinf?s
The residential and office buildings will have asimilar
distribution
of
sensors and actuators per room. Sensors monitor
temperature and humidity, light levels, motion (which will be
triggered with an IR LED
to
emulate human presence), and
smoke and flammable gasses. These sensors can also monitor
other comfort factors such
as
noise level
as
needed. Actuators
will be included in both building types to control physical
features such as doors and windows.
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The key performance measures for the system are the
energy consumed in water distribution, and areliable water
distribution. The ultimate objective
is
to minimize the energy
and water consumption while reliably satisfying the user
convenience constraints, e.g., minimum
flow
rate.
To
enable
system performance evaluation, we develop astochastic model
for the user consumption profile, which includes the time of
the
day,
the duration of opening each valve, and the percentage
opening (water
flow)
for each usage. The model accounts for
different genders and age groups, and differentiates between
the consumption
of
different building types. These models
enable
us
to
run stochastic simulations to assess the perfor-
mance
of
different control strategies under avariety
of
user
consumption patterus.
VI.
OPENCITY
MANAGEMENT
ARCHITECTURE
OpenCity management architecture
is
developed
to
ensure
four testbed performance objectives: cost-effectiveness, users'
comfort, safety, and security. Fig. 8presents an overview of
OpenCity management architecture. The architecture consists
of
three main blocks: the system module, environment module,
and control module. In the system module, the dynamics of
OpenCity components are modeled and tuned through model-
based forecasting strategies or learning.
In
the environment
module, the environment variations are predicted based on
the offline historical data and online measurements. The
current and future values
of
system dynamics are streamed
into the control module, where acost function
is
formulated
with desired performance specifications, such
as
safety, cost-
effectiveness, and convenience. Optimal control decisions are
generated by solving the optimization problem in the control
module, and they are fed into the testbed actuators.
From the modeling viewpoint, two types
of
management
strategies are deployed depending on the component specifi-
cations and requirements; i.e., model-based and learning-based
management. Model-based techniques are implemented for
controlling OpenCity processes with well-understood behavior,
such
as
indoor condition regulation in smart buildings. In a
model-based design, amathematical representation
of
process
dynamics
is
utilized
to
minimize the deviation
of
the controlled
variables from the desired values [18]. Although model-based
control approaches are awell-studied framework, creating an
exact mathematical model for the complex processes
is
akey
challenge [19]. Also, OpenCity components are subject
to
uncertainties and modeling such systems
is
difficult [20].
Learning-based management techniques are implemented
for OpenCity components that are difficult to model from first
principles, such
as
occupants' perception
of
comfort/feedback,
and environmental disturbances. Since alearning-based man-
agement system does not utilize models to describe component
characteristics, its performance
is
not affected by modeling
inaccuracies. In the management architecture, learning-based
management structures are exploited along with the model-
based management techniques
to
take into account the dy-
namic information of users and environmental disturbances
during OpenCity decision-making process. Integrating this
information into the management system leads to adesign
that enables adaptation to uncertain variations and real-time
performance improvement over time.
OpenCity components operate in uncertain physical environ-
ment; therefore, decision making for the system
is
strongly in-
fluenced by unexpected disruptive events. One
of
the important
challenges in OpenCity management
is
ensuring its reliability
and adaptability to unforeseen crises.
To
enhance the decision-
making process in the event
of
these situations, we develop
adecision support system (DSS) that operates along with
the management system. The decision support system uses
Markov decision processes (MDP) to model awide range
of
common disruptive scenarios that may occur in the OpenCity
management system. In the event
of
acritical situation, an
initial action
is
chosen from aset
of
possible actions, and
areward value
is
assigned
to
it. Then, by transiting to new
states and adopting actions with higher reward values, the
decision making process
is
gradually improved. The developed
OpenCity DSS anticipates, adapts, and rapidly recovers from
unexpected emergencies.
VII.
CONCLUSION
The smart city concept promises citizens improved quality
of
life through increased access
to
public services.
To
do
so,
smart cities require ascalable architecture that integrates
heterogeneous sensing, communication, control, and analytics
technologies. There are multiple application-level protocols
that enable smart city data collection, e.g., MQTT, CoAP
(Constrained Application Protocol), WebSocket, and analytics,
e.g., Apache Kafka, Amazon Kinesis. However, there
is
still
the need for an open architectures and secure platform
to
enable researchers, application developers, and city planners
to
easily discover, assess, and mitigate real-life smart city
challenges and problems. The presented smart city testbed,
OpenCity, meets these requirements through its data collec-
tion and processing units, database management, distributed
performance management algorithms, and real-time data visu-
alization.
The main contribution
of
the OpenCity
is
to provide an
accessible experimental environment for researchers to explore
algorithms and technologies that would be integrated into in-
telligent, sustainable cities. The OpenCity testbed includes four
types
of
smart buildings (including residential, commercial
office, hospital, and water treatment plant), intersecting roads,
intelligent transportation systems (including autonomous
ve-
hicles and traffic signals),
as
well
as
the associated com-
munication backbone and data analytics infrastructure. In the
testbed architecture, Server@OpenCity serves
as
acentralized
data hub and access that provides several software services
to
platform users. The intelligent nodes, Nodes@OpenCity,
maintain their local data, communicate with other nodes and
Server@OpenCity. In addition, the integrated management
systems (i.e., model-based predictive control approaches and
learning-based techniques) enable real-time management
of
the OpenCity subsystems considering various uncertainties,
including user behavior and environmental variations.
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Environment measurements
Environmental Module
Comfort
Criteria
Safety
Fig.
8:
OpenCity management architecture.
Database &Data
Analytics Engine
Cost Effectiveness
Security
Performance
Set Points
The design and development
of
the OpenCity testbed con-
tributes
to
both the scientific community and society by
(a)
developing novel collaborative and interdisciplinary re-
search approaches (b) training students in advanced topics
and technologies for addressing real-life challenges that our
community faces,
(c)
providing guidance and support
to
local
communities to employ advanced technologies and innovative
management systems in their smart city plans, and (d) increas-
ing partnerships between academia and industry. Through the
presented architecture with software services, it
is
expected
that OpenCity can support various educational and research
activities related
to
smart city development.
ACKNOWLEDGMENT
This work
is
supported by the Commonwealth Cyber Initia-
tive (CCI), an investment in the advancement
of
cyber R&D,
innovation, and workforce development in Virginia. For more
information about CCI, visit cyberinitiative.org.
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