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.
- ↑ 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
