Conference Session A6
Paper #6134
Disclaimer — This paper partially fulfills a writing requirement for first year (freshman) engineering students at the
University of Pittsburgh Swanson School of Engineering. This paper is a student, not a professional, paper. This paper is
based on publicly available information and may not be provide complete analyses of all relevant data. If this paper is used
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INCORPORATING A CYBER-PHYSICAL SMART EMERGENCY RESPONSE
SYSTEM TO OPTIMIZE DISASTER RELIEF AID
Rebecca Weaver, rlw64@pitt.edu, Mahboobin, 10:00, Max Puntil, rmp65@pitt.edu, Sanchez, 10:00
Abstract — The smart emergency response system (SERS) is
a cyber-physical arrangement that utilizes a MATLAB based
mission control center to connect multiple rescue resources
in attempt to maximize emergency response in disasters and
create a system that will revolutionize the current approach
to disaster relief. The control center uses any communication
means available at the time such Bluetooth and cell data
along with Wi-Fi generated by drones with antennas to
connect first responders, humanoid robots, drones, and
autonomous aircraft and ground vehicles in one rescue
system. Help requests are received via both 911 operators
and an authorized mobile app. Mission control then analyzes
the requests and uses algorithms to generate action plans for
missions while optimizing the system’s resources to serve all
rescue requests efficiently and effectively.
Currently, the emergency response system for large scale
disasters in place in the U.S. lacks unity. Responders at the
local, state, and federal level are trained to respond to the
best of their ability, but there lacks an overarching system
that can organize these rescues to maximize their efficiency.
An overhaul in the system is necessary, and the smart
emergency response system (SERS) is the engineering
solution that provides the synchronized system necessary to
curtail the destructive effects of any disaster, terroristic or
natural.
Key Words — Cyber Physical System, Disaster Difficulties,
Emergency Response, Internet of Things, Optimization
Algorithms, Risk Decision Making
AN OVERVIEW OF SERS
Though many problems that plague today’s world can be
fixed or resolved through work and human effort, disasters
can occur sporadically and can cause catastrophic amounts of
damage without any warning at all. The traditional
emergency response system is designed to handle only
everyday emergencies, and therefore is often crippled by
human and environmental limitations in the event of
largescale disasters. In these extreme cases, society needs a
solid, concrete, and working system in order to create
University of Pittsburgh Swanson School of Engineering 1
2016/02/12
efficiency and effectiveness when human behavior becomes
erratic and time is of the essence. As a solution, research into
this problem over the last decade has pointed to the
development of an automated emergency response system
which would improve disaster response immensely by
providing decision makers in times of crisis with formulated
suggestions for concise plans of rescue. Such a system
develops these suggestions through any combination of the
use of algorithms, logic, probability theory, case based
reasoning, and other related deterministic models. Even more
recently, in the last year, an emergency response system was
proposed that incorporated both the automated emergency
response system and the internet of things in order to merge
together the cyber and physical components of an emergency
response system. This project was called the Smart
Emergency Response System (SERS) and in this paper we
will evaluate and explain the concept of such a system and
how it has the potential to revolutionize disaster response.
The purpose of a SERS is to aid survivors of a disaster
who are trapped or displaced from their homes, and to
administer information to emergency personnel to help locate
these survivors and bring them to safety [1]. In order to
gather information on survivors’ locations, a MATLABbased control center, connected through 911 operators and a
mobile app, receives distress calls and creates an optimal
mission plan based off of locations, hazards, and any other
relevant acquired data. The control center outputs the
generated response plan, designating both available
emergency first responders and autonomous responders to
serve specific tasks in such a way that optimizes time and
resources. Although this paper will not go into depth
describing them, it is worth mention that these autonomous
responders augment the ranks of the first responders and
consist of search-and-rescue dogs equipped with cybernetic
vests, six foot humanoids, and sensory drones [1]. All units
are sent into action to perform their respective tasks and are
able to send data back to mission control displaying their
current condition, their position on a given route to the target,
and much more.
Once again, although we will not go to any length to
describe this process, it is worth mentioning for completeness
the procedure by which the SERS is tested to ensure that the
Max Puntil
Rebecca Weaver
software controlling the guidance systems is fully functional
and ready to use in the field. Using a highly accurate
simulation program known as Simulink within the MATLAB
control center the components are tested to make sure that
they will run smoothly and without error in a crisis situation.
In addition to running simulations prior to the disaster,
Simulink also enables the ability to observe the progress of
the autonomous vehicles during the mission plan through the
use of a 3D real-time Google Earth map which enables
personnel in the mission control center to virtually watch the
vehicles follow a waypoint to their designated target. The
benefit of this is that mission control can be certain that the
components are en route and remain steady on the waypoint
until they reach their designated target. All of these different
entities come together to form what is known as a smart
emergency response system.
disaster, but there are many flaws in the how the emergency
response system is structured and how it is designed and
planned to work. Many of the natural disaster systems that
are in effect today do not have a very high correlation of how
the strategy is planned and what is actually implemented
when the disaster strikes, and due to that the success of these
said systems often do not reach their full potential.
Communication
During disasters, communication is often one of the
largest challenges that is faced. With so many different things
happening all over a widespread area, it is very difficult for
communication to take place simultaneously between many
different sources. According to E.L. Quarantelli, a scientist at
the Disaster Research Center, there are five different types of
difficulty within the communication process inside of disaster
relief: intra-organizational, between organizations, from
organization to the public, from the public to organizations,
and within the systems of an organization. The two categories
in which disaster relief systems specifically showcase their
problematic entities are in intra-organizational and within the
systems
of
an
organization.
Intra-organizational
communication is crucial during a disaster scenario because it
is extremely important to keep everyone on a response team
and at a control center constantly up to date. Since disasters
are so unpredictable and drastic changes can happen in a
fraction of a second, it is not always easy for this feat to be
accomplished. Oftentimes, the communications that are
relayed throughout organizations are transmitted on channels
on radio stations and walkie-talkies [2]. During crisis
scenarios, these channels, usually very general and smooth,
becomes increasingly complicated. Many of the officials who
would run these channels and be responsible for relaying the
information are overwhelmed by the capacity of data rushing
into servers. Due to these factors, the channels of
communication that are used by these organizations are
rendered insufficient to transmit data that is vital, and as a
result, communication suddenly becomes a significant hurdle.
The Components of SERS
In this paper we will specifically explain and go in depth
describing both the MATLAB-based algorithms of the SERS
and the system’s ability to use create an ad hoc network for
communication and data acquisition. These are the two main
components, computational and communicational, which
seem to be the most crucial to the functioning and
understanding of the SERS. This is because without the
algorithms there would be no optimized response plan and
without the specialized communication between components
the system would be no more efficient than the emergency
response system currently in place. When discussing the
optimization algorithms of the SERS we will also present
examples of different uses of other algorithms in emergency
response. We will discuss these other methods both to
compare the approaches and thereby attempt to help facilitate
understanding of the MATLAB algorithms themselves, and
we will present them also to show in what areas the
computational aspect of the SERS might grow. To better
describe how the SERS utilizes the internet of things in
creating its makeshift communication frame we discuss an
example of another model of an emergency response
communications system that employs the same concept.
Coordination
In addition to communication, coordination in an
emergency response system is a key component to keep the
situation from becoming complete chaos. However, it is
noted that many times, during disaster scenarios, coordination
between the organizations and the response team can be
somewhat vague. When dealing with a large scale tragedy,
many times organizations will disagree on the proper course
of action to be taken in which the situation will be handled
most efficiently and effectively, and that can affect how the
response team will run. If there is confusion at any point in
time during a disaster mission, it may not only render it
ineffective in saving lives, but may also put the lives of the
response team themselves at great risk. Not only does this
type of discoordination occur between the response teams and
DISASTER DIFFICULTIES
Throughout the world today, each country has its own way
of handling natural disasters. While some of these methods
are noticeably more effective than others, there are common
flaws that resonate throughout each and every one of them.
Current methods of handling large scale disasters are riddled
with problems and limitations that severely affect the
efficiency and the effectiveness of the system itself, and these
limitations not only hinder the total amount of time and
money saved, but lives saved as well. Within these
emergency response systems themselves, there are many
different areas and components that have their own specific
issues. Not only do all of these limitations occur during the
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the organizations, but it prevails between response teams as
well. Many times a unit will be unprepared to adjust to the
rapidly changing environment that a disaster entails. As a
result, many times uncertainty and disarray will wreak
throughout a response team, and their duties become
befuddled. Although all of these issues may occur in a regular
disaster response system, there are many ways in which a
response system can be designed in order to mostly eradicate
all of these preventable complications.
provides a way to assign duties synonymous with efficient
response plans to all rescue groups.
DATA EVALUATION AND PLAN
FORMATION
The data evaluating component of SERS in which the
system analyzes the current situation and suggests plans of
action to the control center personnel is based on a series of
algorithms from MATLAB’s optimization toolbox programs
in which “optimization techniques are used to find a set of
design parameters that give the best possible result” [4]. In
this process the disaster data collected from the other
components of SERS is molded into optimization problems
as objective functions with constraints. A specified solver is
used to run the problem, monitor optimization progress, and
inspect both intermediate and final solutions. In addition to
the solving capabilities of the program problem definitions,
algorithm options, and results can be bounced back and forth
between the MATLAB workspace and the toolbox’s
respective “Optimization app” to facilitate access to the
system and make it more user friendly. Ultimately, the
algorithms are used to aid in the development of a response
plan that best optimizes the SERS’s resources. In the next
sections we will discuss other uses of mathematics used in
previous models of emergency response systems that could
potentially be incorporated into SERS to make it even more
comprehensive and effective.
DESIGN OF A DYNAMIC EMERGENCY
RESPONSE SYSTEM
As a way to better explain and analyze the smart
emergency response system setup, in this section we describe
the framework and general requirements for the design of
emergency response systems (ERS) as defined by Turoff,
Chumer, Walle, and Yao. These authors simplify the
necessary criteria for an effective emergency response system
into a series of nine premises which include system training,
information focus, crisis memory, exceptions as norms,
nature of crisis, role transferability, information timeliness,
free exchange of information, and coordination [3]. In
addition to outlining these premises we connect them to the
respective SERS components that fulfill these roles.
Insofar of system training, a key benefit of the SERS is
that it provides technological support to decision makers in
time of crisis, effectively reducing the need for any major
system practice since personnel no longer have to learn all the
different ways they are expected to respond in cases of
disaster as these response plans are up to the system to
formulate. Information focus, which needs to be primarily on
the main disaster at hand and not the side situations, also is
eliminated as a problem since the automated system decides
on the information at the forefront of the personnel’s focus.
Since data on the crisis is constantly being collected and
analyzed, this satisfies the need for crisis memory, or the
understanding of what actually happened before, during, and
after the crisis, and which is paramount to the improvement
of the process itself. The problem of exceptions as norms is
itself one of the primary reasons the SERS was developed
since the system is able to dynamically and effectively
observe and meet all developments in the crisis along the
way, and scope and nature of crisis is another problem the
system subliminally tackles better than any previous ERS due
to its immense capacity for data acquisition and therefore
crisis definition. The SERS also eliminates the need for role
transferability because the MATLAB optimization of
resources and supplementation of first responders with
autonomous responders greatly reduces the chances of
personnel having to spread themselves over multiple roles.
Through the incorporation of the internet of things, both
information timeliness and the free exchange of information
are significantly improved and facilitated. Finally,
coordination is all but in the definition of the SERS as it
Ontology-Supported Case-Based Reasoning
A case-based reasoning (CBR) emergency response
system uses a case-retrieving process to base its emergency
response advice on solutions from previous related disaster
events. While the MATLAB system of SERS focuses solely
on the optimization of resources, the incorporation of a CBR
system in SERS would provide extra support and insight for
decision makers into how the overall action plan may be
undertaken. Amailef and Lu discuss one particular design of
an ontology-supported case-based reasoning (OS-CBR)
emergency response system where the case-retrieval process
is combined with ontology to relate the variables within a
hierarchy and subdivide them according to similarities and
differences. This hybrid system creates “a more convenient
retrieving process in disaster situations in order to reach
conclusions and give recommendations based on knowledge
from previous disaster events” [5]. The addition of ontology
to the knowledge based structure of the case-retrieval process
enables better connection between the request vocabulary and
the case base vocabulary because case structure definitions
are created which allows the stored cases to be more easily
and accurately connected to the search terms inputted to the
system. Unlike the MATLAB optimization algorithms, such a
program strictly proposes general solution methods rather
than crisp and exact specific figures of resources and game
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plans, and in this way would augment rather than replace the
MATLAB based control center already established in the
SERS.
Fuzzy fault tree analysis (FFTA) is another hybrid
approach to intelligent emergency response systems which
combines probability theory and fuzzy logic to accommodate
for the uncertainty of available data in risk emergency
response decision-making. Rather than solving for the
optimization of resources in emergency situations like the
MATLAB system does, such a method can be used in the
earlier stages of emergency development when it is still
possible for responders to take action to minimize further
damage. Zhou discusses this method in depth and explains in
depth mathematically how traditional comparative fault tree
analysis (FTA) can be combined with fuzzy set theory, a
method which uses connected sets of possible numbers
termed fuzzy numbers rather than single values in order to
more accurately represent the uncertainty of real world data.
He goes on to demonstrate how this combination overcomes
previous limitations in FTA in which the method was not real
world applicable and creates a system that can effectively be
applied to real world crises [6].
FFTA turns an emergency in its early stages into a logical
relationship and attempts to map out most of the potential
evolution states under different emergency alternatives.
Using its FTA component the process can “capture the
dynamic evolvement process of emergency and estimate the
probabilities of emergency scenarios before the response
actions are implemented” [6]. It does this by comparing the
different evolution states to determine their respective
probabilities and damage costs. Fuzzy numbers are included
in this process to account for the uncertainty of real world
data and thus allow the probabilities to be determined more
objectively, and the overall evaluation of emergency
alternatives is combined and sorted by endpoint method. The
method quickly becomes very complex as it considers many
different measures of different emergency response
alternatives and their respective impacts and still needs
further development. However, it has been successfully
implemented in a case study of a crane hitting the high bent
and provides another valid method of data analysis that could
be incorporated into SERS. Once again, the addition of this
method to the MATLAB based control center would augment
and not replace its capabilities and would prove particularly
useful for the evaluation of uncertain data in cases where the
emergency situation has potential to escalate further if
unchecked.
FIGURE 1 [5]
The OS-CBR Approach and Working Process
As shown in Figure 1 the OS-CBR system works in four
steps which include data acquisition, information extraction,
the case-based reasoning, and knowledge presentation. An
information extraction algorithm is used to acquire the
emergency data by combining information from several
different sources; if such a method were incorporated into
SERS these sources would include the smart system’s app,
911 distress calls, and information acquired through the
SERS’s temporarily established ad hoc network as we will
discuss in a later section. Then a case attribute domain
ontology consisting of six main entities including physical
target, disaster location, human target, weapon used, stage of
execution, and disaster event with each entity divided into
categories and sub categories is used to formally extract the
information variables and define the interrelationships
between them [5]. Next, the system searches it knowledge
base for related cases and uses its CBR including an ontology
case retrieval algorithm and case adaptation algorithm to
create its response plan suggestions based purely on past
success stories in similar situations which it then presents to
first responder decision makers as substantial guidance along
with the acquired knowledge in the last step. Using case
studies the OS-CBS approach has been successfully
implemented as a sub-section to other emergency response
systems and is a viable option of addition to SERS.
THE SMART ASPECT OF SERS
With
the
current
emergency
communications
infrastructure in place, when handling everyday crises,
emergency communications systems (ECS) operate
optimally. However, as put by Sterle, “in case of severe
conditions,
availability
and
appropriateness
of
communications services for first responders is a
multidimensional challenge” [7]. The SERS smart aspect
aims to fix this critical problem in emergency disaster
response and is the final piece of the system that links all the
FTA-AHP Fuzzy Fault Tree Analysis
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other components together. For example, as complex and allencompassing as they are, the algorithms mentioned in the
previous sections of the SERS need a reasonable amount of
data to process in order to develop their solutions and work
optimally. The Internet of Things (IoT) along with an ad hoc,
makeshift network of sorts, consisting of any means of
network available to the system at the time including
BlueTooth and cell data, Wifi generated by ground troops,
and these sensors, is essentially what enables this data
acquisition when normal communication infrastructure fails
due to the extenuating circumstances of a disaster.
The IoT can be considered as the wireless communication
achieved between everyday objects when they are equipped
with technology such as sensors, computational software, and
wifi routers. These data collecting technologies all work to
create one network that connects the devices to each other
remotely, hence the name [8]. Because these sensors can
collect almost any kind of data from the machines or objects
that they are placed within and send it out remotely, the IoT
enables data to be collected and stored at a much faster rate
than ever before, even in the event of failed normal
communications infrastructure, and it is for these reasons that
the integration of the IoT within the SERS is what makes the
system so effective.
plan which will allow the components to perform their
respective assignments flawlessly.
IoT in Data Acquisition
The incorporation of the internet of things technologies in
SERS also provides a means of acquiring a wealth of
important data both environmental and human. Sterle’s
outline of a heterogeneous emergency response
communication system provides an excellent example,
besides the SERS, of incorporating IoT technologies into the
ERS process as a means of acquiring disaster. The outline of
this system explains how commercially available sensors can
be used for situation surveillance, environment monitoring,
and well-being monitoring. Examples of IoT sensors in
environment monitoring might include avalanche trackers,
water level sensors, heat sensors, CO and butane probes,
temperature sensors, and humidity sensors, and in humans
these sensors could relay data about heart-rate, heat exposure,
location, acceleration, and more [7]. The data that could be
collected from these kinds of sensors during times of crisis
could provide an immeasurable wealth of information to first
responders to support their on-site intervention management.
And in the event of failure of their normal emergency
communications system, the distributed architectures of the
IoT sensors would provide a survivable communications
means in disaster situations.
IoT in Communication on the Ground
Within any emergency response system, one of the most
fundamental mechanisms is a form of communication
between all of the components. Without this, the response
teams and mission control would become completely
unaware and unresponsive to one another. However,
implementing the internet of things creates a smart
emergency response system completely devoid of this
problem. One of the highlights of the internet of things is that
any sensor placed within a device can pick up data
transmitted through another sensor; this exchange is known
as machine-machine (M2M) communication and it is one of
the many reasons that the internet of things is so important in
the smart emergency response system. Using this M2M
communication, the autonomous components of the
emergency response system are able to remotely
communicate with one another transferring data. As a result,
they are able to “see” where the other components are
spatially. This spatial awareness allows for less error and
collision between the autonomous vehicles, which increases
the overall efficiency and performance of the system itself.
Not only does the internet of things allow for
communication between the components of SERS, it also
makes communication possible between mission control and
the components as well. When mission control receives a
distress signal from a survivor in need of assistance, it
instantaneously extracts all the data from the call and begins
to transmit it to the components within the system. This data
includes the location of the survivor, and the selected mission
Creating a Communications Network
As a solution to the communications problem when the
traditional communications infrastructure fails in times of
crisis, the SERS is extremely versatile in network capabilities
and is capable of using almost any network available to it at
the time. Without this novel approach to emergency
communications the MATLAB based optimization solutions
and response plans could not effectively be carried out,
making this last piece of the SERS vital to its overall
functionality. The diagram below effectively maps out these
communication capabilities and processes.
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Rebecca Weaver
evaluated by a MATLAB based system which effectively
determines an exact response plan to optimize resources and
coordinate responders. In this paper we discussed other
examples of emergency data evaluating systems both similar
to the MATLAB process so as to facilitate understanding yet
also different enough as to be potential additions to the SERS
computing component. Its unique network capabilities also
serve to solve previously crippling issues of communication
in traditional emergency response systems. The amount of
data collected by the SERS communication components is
imperative in catastrophes where there is a multitude of
information needs for effective emergency response
management. As outlined by Turoff, Chumer, Walle and Yao
and discussed in an earlier section, these information and
communication needs include information focus, crisis
memory, information timeliness, free exchange of
information, and coordination [3]. All of these previously
considered major problems with emergency management are
solved by the communication capabilities of the SERS. The
complexity of the SERS creates one of the most efficient and
effective proposed emergency response plans with better
flexibility and durability, system lifetime, and fault-tolerance
than most, if not all, systems in place.
FIGURE 2 [7]
The Heterogeneous Communications Approach
PUTTING IT ALL TOGETHER
As shown in Figure 2 if we focus on the middle ovals in
particular, such a communications approach consists of onsite
infrastructure “comprising of mobile devices for
communication throughout the intervention among members
of the onsite unit” and a “backhaul-supported system,
constructed as a heterogeneous communication infrastructure
comprising core networks and different professional and
commercial access networks” [7]. At its core, the SERS is
able to set up and maintain connectivity with and between
available networks both professional and commercial at all
times. In the event of a major catastrophe, the SERS will
establish a connectivity using cellular data and the satellite
network or a WiFi backhaul system. It is able to achieve these
communications services automatically and with minimal
delay creating a highly reliable communications network.
Since all of the components of SERS have now been
discussed, it is apparent that the concept of a smart
emergency response system must be looked at with utmost
seriousness. During large scale disasters, damage can be
monumental to not only infrastructure and funding, but
human lives as well. An emergency response system that
produces optimal response times and minimal damage is
paramount, and it is for these reasons that the smart
emergency response system should be incorporated into
society. In using the highly complex and optimized mission
plans generated by the MATLAB control center, mission
control can implement effective and efficient response plans
for any disaster scenario which may occur today. The speed
and accuracy of the program generating such a response will
be sure to save not only time and energy, but lives as well
which could have been lost when taking the time to plan a
course of action. In addition, the IoT integration allows for
continuous data transmission between all of the components
of the smart emergency response system, thus eliminating
any chance of miscommunication between units or rushing
into the field without any coordination. Through the SERS
adaptable network capabilities, response teams and mission
control will be constantly in touch and up to speed with new
information ensuring that the designated targets are brought
to safety. Additionally, although this aspect was not within
the scope of the paper, it is worth mention that combining the
Simulink feature with the MATLAB control center will allow
those within mission control to constantly monitor the
components of the system while in the field via a 3D Google
Earth map, making sure that the mission runs without error
MAKING DISASTER AID MORE
EFFICIENT
In effect, the SERS combines many different
technological concepts to create one revolutionary emergency
response system capable of handling the abnormal disaster
events that our current infrastructure cannot. The SERS has a
very complex and all-encompassing nature so we chose to
focus on the data evaluation and communication aspects of
this system as they are so important and so closely tied.
Through its unique network and internet of things
technologies and capacities, the system is able to acquire a
wealth of data about many different aspects of a disaster
during all stages of development. This data is processed and
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Rebecca Weaver
and allowing for proper planning in the event some
misfortune does occur. The power to save money, time, and
most importantly lives rests in our hands when it comes to
disaster scenarios, and through the assimilation of a smart
emergency response system into society, we are capable of
keeping the general public as safe as it can be.
We would like to thank our respective engineering 0012
professors and visiting writing instructors for clarifying the
aspects of the annotated outline. We would also like to thank
our peers for allowing us to bounce our ideas off of them.
Last but not least, we would like to extend a huge thank you
to our writing instructor Nancy Koerbel for her expertise and
time in grading our assignments.
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ACKNOWLEDGMENTS
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