IEEE Technology and Society Magazine - September 2016 - 23
INDUSTRY PERSPECTIVE
Emanuele
Crisostomi
Robert Shorten
Fabian Wirth
Smart Cities: A Golden
Age for Control Theory?
he recent radical pro-
gress in the fields of
future Internet and
Smart City re lated
technology innova-
tion are transforming society. Zam-
bonelli describes upcoming society
as a sociotechnical urban superor-
ganism [1]. One of the main drivers
of this transformation is digital
technology, whose impact on the
structure and on the dynamics of
social networks is also explored
in [2]. The ubiquitous presence of
networked embedded devices is
the cornerstone of the Internet of
Things (IoT) paradigm [3], which
thanks to portable devices (e.g.,
smartphones), is quickly evolving
into a so-called Internet of People
(IoP) dimension [4]. The social im-
pacts of future Internet and Smart
City related technologies range
over very different fields, including
health-care [5], [6]; natural disaster
prevention [7]; cooperative transpor-
tation [8], [9]; as well as enabling
the realization of low-carbon societ-
ies [10], advanced E-Commerce [11],
and real-time human behaviors and
activity monitoring [12].
Accordingly, research questions
arising in the context of the Smart
City are driving exciting new activi-
ties in a number of classical disci-
plines. Among these, control theory
has much to offer and much to gain
as a discipline, by embracing some
of the questions that are of con-
cern as planners and municipali-
ties re-imagine growing cities. Smart
City research, at a very high level,
is about making best use of exist-
ing resources in cities, as we try to
manage congestion, pollution, food
production, and maintain living stan-
dards in the face of ever increasing
pressure on natural resources. Man-
aging resources, optimally, is a clas-
sical consideration of control theory.
Modern control theory is a rather
established discipline, and most
electrical and mechanical engineers
are now familiar with the basics of
this theory. While a classical version
of control theory has still much to
offer the city, there are new "twists"
that arise in the context of Smart
City research that offer the oppor-
tunity for theorists to explore new
boundaries that go beyond classi-
cal control. Specific challenges and
some examples are now briefly iden-
tified and described.
First, classical control is typically
concerned with regulating a single
system such that the system behav-
ior achieves a desired behavior in an
optimal way, given the constraints
imposed by the problem and the
available resources. Even in areas
where large-scale coupled systems
are studied, it is the behavior of all
components of the system that is
analyzed and designed. In contrast,
T
Digital Object Identifier 10.1109/MTS.2016.2592782
Date of publication: 7 September 2016
september 2016
∕
IEEE Technology and Society Magazine
in Smart City applications it is not the
behavior of individual agents that is
of interest. Rather, the aggregate
effect of the actions of a multitude of
agents is the variable of interest. For
this it is not necessary or maybe it is
even harmful to synchronize behav-
iors, and it is not even necessary
that all agents behave in a rational
way. Examples of this appraisal can
be found in the allocation of parking
spaces, in the regulation of demand
for shared resources like water and
electricity, or in the supply of medi-
cal assistance. De-synchronization
in fact eases the burden of supply-
ing the resource, and the quality of
the supply is measured by aggregate
effects. Bounds on the required qual-
ity of service for individuals, on the
other hand, are more or less strin-
gent depending on the application
area, e.g., medical infrastructure
vs. parking spaces. A further differ-
ence arises when dealing the scale
of Smart City applications. Typi-
cally, classical control is concerned
with the control of systems of fixed
dimension. On the other hand, in
the Smart City we typically wish to
control and influence the behavior
of large-scale aggregations of popu-
lations. In such situations, even the
dimension of the system may be
uncertain and varying, and the need
for scale-free control of very large-
scale systems is pressing. Scale-free
control for big systems, except in the
case of passive control design, is a
23
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