Catalyzing the Internet of Things and Smart Cities ： Global City Teams Challenge
Catalyzing the Internet of Things and Smart Cities: Global City Teams Challenge
Sokwoo Rhee
Smart Grid and Cyber-Physical Systems Program Office
National Institute of Standards and Technology
US Department of Commerce
Gaithersburg, Maryland, USA
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=920338
sokwoo.rhee@nist.gov
April 11, 2016
Proceedings
First International Workshop on Science of Smart City Operations and Platforms
Vienna, Bundesland Vienna, Austria
Abstract
Many smart city and Internet of Things (IoT)
solutions are suffering from fragmentation and lack of economies of scale. To address this issue, the National Institute of Standards and Technology (NIST) initiated the Global City Teams Challenge (GCTC) to catalyze collaboration among different stakeholders. The goal is to design and deploy IoT and smart city solutions that are replicable, scalable, and sustainable, thereby leading to the identification and adoption of a consensus framework for smart city technologies. The second round of GCTC is currently in its first phase. Future smart city projects would benefit from a widely distributed IoT communications
fabric that can serve as an infrastructure for the deployment of truly sharable and replicable smart city solutions.
Index Terms
Internet of Things, Smart City, Global City Teams Challenge, GCTC, Replicability, IoT Fabric
Research Areas
Electronics & Telecommunications, Information Technology, Measurements
I. Introduction
The concept of Cyber-Physical Systems (CPS) or Internet
of Things (IoT), which has been around for more than a decade
[1], is currently creating a great deal of buzz in the marketplace
and media, with a promise to enhance the way we live our
lives. There are three major arenas for IoT applications—in the
consumer, industrial, and public sectors. Recent interest has
mainly focused on the consumer side, including consumer
appliances, home area networks and other applications.
Industrial applications are promising to improve business
outcomes for many sectors, including manufacturing, asset
management and healthcare.
In the case of public sector applications, the Internet of
Things is a major enabling concept to accelerate the
development and deployment of smart city solutions. This
article discusses the overall architecture of IoT and the issues
of current practice of smart city deployments. The article then
presents a new collaborative approach that uses the concept of
a “challenge” for the acceleration of broader and faster
adoption.
II. IoT and Smart Cities Architectures
To understand the basic characteristics of IoT and smart
cities, it is useful to analyze the composition of a typical IoT
solution and show how the architecture can be mapped to that
of smart cities. Figure 1 illustrates a simplified layered
architecture of IoT.
Figure 1: Simplified IoT and Smart Cities Architecture
At the bottom of the structure is the Hardware layer,
where tangible hardware elements such as sensors, actuators,
chips, and radios are found. The elements in this layer typically
interact directly with the environment, with other hardware
elements, or sometimes with the users/consumers.
The next layer is the Communications layer, which is
sometimes called “connectivity.” This layer connects and binds
different components in the Hardware layer so that information
can flow between layers or between hardware components.
This is where well-known technologies such as Ethernet, WiFi, cellular, and short-range wireless are found. For some
applications, the Communications layer is minimal (e.g., scaled
down to an internal bus or to simplified connectivity among
different hardware components).
The next layer is the Data Analytics layer. This layer
receives data from the Communications layer, and then stores,
analyzes, and processes them. This is where “big data”
applications could reside, for example, in the case of
applications that require collection and analysis of data from a
large number of sources. However, it should also be noted that
this layer could be relatively thin and simple, especially in the
case of embedded applications. In other words, the Data
Analytics layer does not necessarily imply the need for a huge
database and an extremely fast processor. Many distributed IoT-based control systems employ a
relatively small-scale Data Analytics layer. An example of a
small-scale layer can be found in a smart thermostat that could
also function as a local decision maker within the home
network.
On the other hand, many IoT solutions deployed at a citywide scale may require a big centralized data repository and
more powerful processors to handle a larger amount of data
from multiple sectors and applications. An example of such a
system could be a city’s disaster command center that is
designed to provide simultaneous visibility into different
departments (e.g., water, energy, transportation, healthcare,
etc.).
The main function of the Data Analytics layer is to collect
data from the lower layers and extract useful information from
the set of data. Note that the set of data itself may not have
significant value and may not be very useful to the user. The
information extracted from the data, however, could be
valuable in taking actions and achieving a desired end result.
The top layer is the Service layer. This layer is where
intelligence resides and decisions are made. This layer receives
information from the Data Analytics layer, and then makes
decisions on next steps. The next steps could include
displaying the information on a monitor screen or operating
and controlling actuators. The Service layer is important
because it is in the position in the architecture to create the
highest value for the users of the system. Many business
decisions are made in this layer, including human-in-the-loop
actions. The human-machine interface can be an important
factor in this layer.
Once the decision of the next step is made at the Service
layer, sometimes (but not always) information starts flowing in
the reverse manner (i.e., from Service layer down to the
Hardware layer). This is especially true for systems based on
some type of autonomous control. On the other hand, it is
sometimes a human being who makes the decision and
executes it. In either case, the end result is some type of action
that closes the loop of the information flow. A similar
representation of IoT data flow was proposed in another article
[2].
Many developers consider IoT to be the combination of just
the two bottom layers (Hardware and Communications). It is
important to note, however, that these two layers are merely a
part of the whole IoT architecture. In many cases, the top two
layers (Data Analytics and Service) play more important roles
in defining and producing the real value from the system. Also
in many cases, the design and implementation of the top two
layers may be more complex and unclear than the bottom two
layers. In many cases, the top two layers are heavily coupled
with business cases that are important factors in determining
sustainability and replicability of the solutions.
In the case of smart city applications, it is often easier to
conceptualize the architecture as two groups of layers—
Infrastructure and Applications. “Infrastructure” typically
refers to the bottom two layers of the IoT architecture, and
“Applications” refers to the top two layers. In some cases,
however, the Data Analytics layer could belong to the
infrastructure group, depending on the nature of its
functionality. Many solutions/products that belong to the
application group have more flexibility in deployments than the
ones belonging to the infrastructure group. This simple IoT
architecture can serve as an initial template to map different
smart city solutions to build consensus on their technical
interoperability, which is essential in addressing the challenges
in accelerating the market momentum for IoT and smart cities.
III. Challenges for advancing IoT in Cities
Smart cities use smart technologies such as IoT and CPS to
improve the quality of life of the residents and citizens.
Although progress in deploying IoT solutions has been quite
impressive, the IoT market still suffers from the issue of
“fragmentation, [3]” and the smart city market shares similar
concerns. Many smart city solution projects are isolated and
heavily rely on custom-solution developments. Naturally,
many of them are “one-off” projects with heavy emphasis on
customization and inadequate consideration for future
upgradability and extensibility. As a result, these deployments
are isolated and do not enjoy economies of scale. Although
many cities share the same issues (i.e., parking problems,
traffic jams, air pollution, etc.), they often do not share best
practices and end up reinventing the wheel. In this landscape, it
is very difficult to create common standards for development
and deployment of interoperable solutions.
IV. Global City Teams Challenge
To address this issue, the National Institute of Standards
and Technology (NIST) has teamed up with US-Ignite and
private sector partners to create the Global City Teams
Challenge (GCTC) program [4][5]. The goal of GCTC is to
establish and demonstrate replicable, scalable, and sustainable
models for incubation and deployment of interoperable,
standards-based IoT solutions and to demonstrate measurable
benefits in smart communities/cities. “Replicability” means
that the solutions should be designed to operate in more than
one city or community with minimal customization.
“Scalability” means that the solution should be functional
regardless of the size and volume of the deployment.
“Sustainability” means that the project should be designed to
last beyond its initial funding stage. In other words, the
deployed solution must either (1) create its own revenue to
support the operational cost or (2) provide enough tangible
benefits that the municipal governments are willing to cover
the operation cost using their budgets. Many of today’s smart
city deployments lack one or more of these characteristics.
GCTC places significant emphasis on the ability to measure
tangible benefits for residents and citizens, thus empowering
leaders within communities to demonstrate the benefits of
adoption.
A. Approach
To achieve the goal of GCTC, the program was designed to
create a voluntary environment for multi-stakeholder
collaboration. As can be seen in Figure 2, multiple cities and
technology innovators are brought into the program and asked to coalesce around shared challenges (e.g., air pollution, traffic
management, emergency response) to create teams called
“Action Clusters.” Each Action Cluster creates a project plan
with a timeline to demonstrate their accomplishments in a
tangible manner. Because each action cluster includes multiple
members, it is likely that the outcome of the solution will be
replicable to other cities. In the case that a team has only one
municipal partner, the team is encouraged to establish
additional partnerships with other cities by demonstrating
measurable and quantifiable benefits of the solution. It is also
important to note that replicability and interoperability should
be based on collaboration that is global rather than just
regional.
Figure 2: GCTC Approach
Cities have two strong reasons for participating in GCTC.
For the cities that have already gone through successful
deployments, it is an opportunity to promote their solutions and
be the origin of replication for other cities that are facing
similar challenges. For the cities that are just starting to
consider the deployment of smart city solutions, it is an
opportunity to learn from other cities’ projects and to showcase
their own city as a ready partner to organizations with
replicable smart city technologies.
For corporations, GCTC is an opportunity to identify new
business partners, demonstrate their proven solutions, and
enlarge their market.
Academic institutions participate in order to find
opportunities for joint R&D with cities/communities and
partners that will enable the joint development and deployment
of new technologies. The process also allows researchers to
identify key common characteristics and components among
different applications and implementations, which will help the
market to find convergence on best practices and eventually
lead to broadly adopted standards.
B. GCTC 2015
The first round of GCTC culminated on June 1, 2015, after
a nine-month-long process of team building, incubation,
solution development, and deployment. More than 60 teams,
composed of over 200 organizations and three dozen
cities/communities around the world, gathered at the National
Building Museum in Washington, D.C., to present and
demonstrate the impact of their smart city solutions. Many
high-profile visitors and speakers, including King WillemAlexander and Queen Maxima of the Netherlands and U.S.
Secretary of Transportation Anthony Foxx, came to celebrate
and encourage the teams’ accomplishments. The event was
attended by over 1300 people and was covered by many media
outlets.
C. GCTC 2016-2017
Based on the success of GCTC 2015, the next round was
launched in November 2015. This new GCTC round is
composed of two phases. The first phase will continue until
June 2016, with the focus on building the teams and defining
the project goals, timelines, and Key Performance Indicators
(KPI) of the quantifiable impacts to residents and citizens.
Participants will demonstrate and pilot the solutions and will
build partnerships with as many cities as possible. The second
phase will focus on deploying the solutions, achieving the
goals (based on the KPIs devised during Phase 1), and
measuring the impacts. Phase 2 will culminate in June 2017.
GCTC 2016-2017 carries over the key elements of GCTC
2015, and adds two more ambitious goals, encouraging the
teams to:
- deploy the shared and replicable solutions in multiple cities, potentially on multiple continents and
- provide tangible measurements of the improvements made by the solutions, such as reduction of average commute time, reduction of air pollution, reduction of water loss.
V. Further Discussions: IoT Smart City Fabric
One of the missing links in accelerating the deployment of
IoT/CPS and smart city solutions is the lack of a “connectivity
fabric”--a commonly shared IoT/CPS network infrastructure
among cities and communities [6]. As of today, there is no easy
mechanism for an IoT solution to be deployed and become
operational in a plug-and-play manner. For example, a simple
flood-level sensor deployed in one city may not share the same
backbone infrastructure required to exchange data with sensors
in other cities. The current landscape of IoT and smart city is
similar to that of the communications infrastructure of preInternet days.
It is essential that a communications fabric infrastructure be
developed that can enable IoT devices and smart city solutions
to identify and communicate in a plug-and-play manner, to
create synergy between sectors, to reduce overhead, and to
catalyze the mass adoption of affordable solutions by the
residents in cities and communities. The IoT/Smart City fabric
would enable sharing and replication of the solutions beyond
the city limit, just as the Internet broke the physical-distance
barrier for communications and commerce. Combined with
multi-stakeholder collaboration programs such as GCTC, the
IoT/Smart City fabric—built to be open and neutral--could
allow many cities and communities, large and small, to enjoy the benefits of advanced technologies to improve the quality of
life.
Starting with its Challenge programs [7][8], NIST has
already taken steps in the direction of promoting consensus
around reference architectures for interoperability. Informed by
GCTC, NIST has taken the first step to establish an
international technical public working group to help develop an
“IoT-Enabled Smart City Framework.” [9]
Acknowledgment
We would like to acknowledge the National Science
Foundation, U.S. Department of Transportation, International
Trade Administration, and all other partners and participants
for their support and contribution to GCTC.
References
- ↑ Industry Advisory Board, RWTH Aachen University, “Cyber-Physical Systems - History, Presence and Future,” February 2013. http://www.ima-zlw-ifu.rwth-aachen.de/fileadmin/user_upload/INSTITUTSCLUSTER/Publikation_Medien/Vortraege/download//CPS_27Feb2013.pdf
- ↑ E. P. Goodman, Rapporteur, “The Atomic Age of Data: Policies for the Internet of Things,” Communications and Society Program, The Aspen Institute, 2015, p. 5. http://csreports.aspeninstitute.org/documents/Atomic_Age_of_Data.pdf
- ↑ M. Smolaks, “Internet Of Things In Danger Of Fragmentation” TechWeek Europe, July 2013 http://www.techweekeurope.co.uk/workspace/internet-of-things-in-danger-of-fragmentation-120566
- ↑ Global City Teams Challenge http://www.nist.gov/cps/sagc.cfm
- ↑ Global City Teams Challenge https://www.us-ignite.org/globalcityteams/
- ↑ S. Rhee, G. Mulligan, “SmartAmerica Challenge,” 2013-2014, p. 6. http://www.nist.gov/el/upload/Smart-America-Challenge-r1-25p.pdf
- ↑ E. P. Goodman, Rapporteur, “The Atomic Age of Data: Policies for the Internet of Things,” Communications and Society Program, The Aspen Institute, 2015, p. 48. http://csreports.aspeninstitute.org/documents/Atomic_Age_of_Data.pdf
- ↑ S. Rhee, “Internet of Things and Global City Teams Challenge,” January 2015, p. 33. http://www.nema.org/Policy/Documents/IoT%20Global%20City%20Summary_NEMA_Sokwoo%20Rhee_01.08.2015.pdf
- ↑ International Technical Working Group on IoT-Enabled Smart City Framework https://pages.nist.gov/smartcitiesarchitecture/