An overview of Crowd Dynamics Management in IoT System IEEE paper. To understand the dynamics of the system.

1. A Feedback Control Based Crowd Dynamics
Management in IoT System
Yuichi Kawamoto , Naoto Yamada , Hiroki Nishiyama , Nei Kato, Yoshitaka Shimizu, Yao Zheng
Presenter : Arnab Bhattacharjee
Atul Kumar Sahay
IEEE internet of Things Journal , Volume 4 , Issue 5 , 2017

2. Contents
• MOTIVATION
• INTRODUCTION
• APPROACH AND DEFINITION FOR MODEL
CONSTRUCTION
• BEHAVIOUR MODEL OF CROWD DYNAMICS
MANAGEMENT APPLICATIONS
• EVALUATION METRIC
• RESULTS
• CONCLUSION
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3. MOTIVATION
• Internet of Things (IoT) provides a new perspective on applications pertaining to
smart cities.
• Smart city applications focus on resolving issues facing people in everyday life, and
have attracted a considerable amount of research interest.
• The typical issue encountered in such places of daily use such as stations,
shopping malls, and stadiums is crowd dynamics management.
• The model uses feedback control theory, and enables an integrated evaluation of
the control effectiveness of crowd dynamics management methods under various
scenarios
• Real-time crowd dynamics management can be achieved by gathering information
relating to congestion and propose less crowded places.
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4. INTRODUCTION
• Internet of Things (IoT) has emerged as a widely recognized trend that is currently
attracting a considerable amount of attention from governments, industry, and
academia.
• Not only smart-phones and personal computers, but also home appliances,
industrial machines, smart watches, vehicles, and so forth.
• Perspective has become possible through the development of various
communication and computation technologies like WLAN
• It is possible to have a large number of simultaneously connected devices and a fast
initial link setup to access points (APs) using the IEEE 802.11ah or the IEEE 802.11ai
standard.
• We can observe various types of information remotely via the connected devices
(e.g., global positioning system (GPS), and biological, visual, thermal, and air
pollution)
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5. 5
Crowd at shopping mall Crowd at Railway Station

6. What is Crowd Management?
• Crowd management is the organized and substantiated planning and the
direction given to the orderly progress of events where large groups of people
gather together.
• Why it is important :-
- The more people , the higher the odds of something dangerous or reckless occurring
. - During emergency situation , crowd dynamics and create roadblock to effective
safety measure and evacuation.
In this paper ,crowd dynamics management is to resolve congestion
by controlling the movement of crowds using feedback strategy.
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7. APPROACH AND DEFINITION FOR MODEL CONSTRUCTION
• The concept of crowd dynamics management involves resolving crowd
congestion in real time automatically by repeatedly gathering information
about the state of the environment and moving crowds accordingly.
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8. • Separated crowd factors to three levels :
* Operation(Micro-scale factors) - Each pass , Influence of disturbing obstacle or
collision
* Tactical – Roots they select towards destination , Staging points.
* Strategic(Macro-scale factors) - Destination ,Users moved eventually ,Time taken.
Here control effectiveness of crowd dynamics management can be
determined by the number of users moved each time from this
viewpoint. We will deal about Macro-scale factors in this paper.
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9. How do we gather crowd density data?
• Visual Sensors as stereo cameras
• Laser range scanners.
• GPS information
• Radio Frequency Identification (RFID)
• Wireless LAN positioning
*Applications control the movement of crowds by providing moving instruction to
them depending on the deviation between the given state of the congested area
and the desired state*
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10. • We assume that the control effectiveness of crowd dynamics management from a
macroscopic point of view can be determined as the number of moved users each
time. The trajectory of the number of moved user is related to a metric.
• Three characteristics that are expected to affect the performance of applications:
* The sensitivity of users - Represents their reaction to the instructions.
* The lag time of changes - Time taken by users to move from one area to
another
* The power of instruction -The intensity at which the instructions are
being given.
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11. • Techniques of implementing instructions to crowds are largely divided
into two types :
*Sending information directly from infrastructures - Route information , Distribution
of coupons electric bulletin boards, Announcement over speakers.
*Changing the hardware specifications of the crowds environment – Handling the
opening and closing of doors to change routes in a station , turning the ceiling lights
of corridors.
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12. BEHAVIOUR MODEL OF CROWD DYNAMICS MANAGEMENT
APPLICATIONS
• Proposed method to construct an overall behavior model of crowd dynamics
management applications by utilizing feedback control theory.
Model based on time variation to represent the dynamic situation of the state
of the environment in real time.
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13. • Applying feedback control theory
The objective of feedback control theory is to change the given state of a
”controlled object” by a ”controller” based on the value of the desired state and
the given state determined by a ”feedback factor.”
(a) Controlled Object (b) The Controller (c) Feedback Factor
Movement of the
Crowd
Bulletin Board Behavior of APs
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14. • Simple use case to apply feedback control
To construct the application, we need assumed implementations related to three
behaviors of the application :-
*Collecting area information - Each user has a device that can transmitting his/her
position information through the closest AP in the area.
*Analyzing congestion- The server counts the number of the users in both areas and
calculates the deviation of the between the given state and the desired state.
*Controlling users - To control a specified number of users, the server suggests an
instruction to users to move through an electronic bulletin board
(Once the users move following the instruction, the application repeats this flow
until a stationary state is attained)
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15. • Representation using block diagram
• Control effectiveness of the application at crowd dynamics management
appears in terms of the trajectory of the number of moved users each time from
a macroscopic viewpoint.
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16. List of the Notation defined
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17. • The model is based on time variation, which is represented by a function of t.
• Transfer function of the overall behavior of the application - goverall(t)
• Input for goverall(t) - total number of the users desired to move, ndesired(t)
• Output for goverall(t) - the total number of moved users until t , nmoved(t)
• Application controls the movement - ndesired(t) - nmoved(t)
• If we calculate the relationship between the input and the output by using the
function of t, it becomes a complex calculation.
• To simplify the calculation, we introduce the function s ( Laplace)
Nmoved(s) = Goverall(s).Ndesired(s)
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18. • Each part of the application
The application controls the movement of users (assumption). Thus, the control
object is to be associated with the sensitivity of the users and the lag time.
So , reaction of user each time ( in time domain) :
where a is the sensitivity of user.
• If a has a high value, this means that many users have moved after watching the
board, and reducing the strength of the control command becomes easier.
Reaction of users in the s domain :
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19. In the application it has been assumed that transit time is the lag time. Transit time
is denoted by Tmove
Thus, this delay is treated as a dead time component in feedback control theory and
is defined as e−Tmove·s , Tmove in the complex number domain s.
So ,model of the controlled object as Gobj(s) based on Greact(s) and e−Tmove·s
The parameters of the controller are set according to the criterion of controlling.
So here the model of the controller is :
, where K the coefficient of the proportional relationship
between the scope and the deviation.
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20. H(s) is considered as the feedback factor . This paper is working on characteristics of
crowd so at the range considered there is no loss .
Thus H(s) =1
• Constructing the overall model:
We apply the model of each part of the application to the feedback loop and
construct the transfer function of the overall behavior of the application based on it.
Nmoved(s) is the output of transfer function Goverall(s), and that the difference
between the desired state and the given state Δ(s) is the input
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21. Δ(s) can further be defined as :
When we set Ndesired(s) as the input and Nmoved(s) as the output of the transfer
function representing the overall behavior of the application Goverall(s), we can
obtain Goverall(s) based on previous equations :
Ndesired(s) is considered a constant value ( for simplicity) . The input of the constant
value at time domain t is transformed into step input in s.
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22. EVALUATION METRIC
• Since the application controls user movement based on the repeated calculation of
the deviation of the given state value and the desired state value, deviation in the
specified interval describes performance of the application
• Based on δ(t), the magnitude of deviation Ie. This metric measures the difference
between the total number of moved users nmoved(t) and the total number of users
that need to be moved ndesired(t) from the start time to the deadline time. Ie is
expressed as follows:
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23. Evaluation Setting
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24. RESULTS
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25. (a)Relation between trajectory of user
ratio and sensitivity.
(b)Relation between trajectory of user
ratio and the power of instruction
(c) Relation between trajectory of user
ratio and transit time. 25

26. (a) Relation between the magnitude of
deviation and sensitivity.
(b)The relation between the magnitude of
deviation and the power of instruction.
(c) Relation between the magnitude of
deviation and transit time. 26

27. CONCLUSION
• Focused on crowd dynamics management applications that are expected to resolve
crowd congestion through IoT
• Overall behavior model and an evaluation metric for crowd dynamics management
applications by utilizing feedback control theory for presenting proof-of-concept of
control effectiveness of crowd dynamics management.
• Can contribute to the development of future crowd dynamics management
applications as an evaluation tool.
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