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
[1].
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, [2]” 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 [3][4]. 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
- ↑ 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/
