The document discusses an IoT software architecture aimed at improving emergency evacuations in buildings through an optimized network flow algorithm for real-time evacuation handling. It addresses research questions regarding optimal building dimensions for safety and minimizing evacuation time while highlighting the limitations of static evacuation plans. The proposed system focuses on simulations, real-time monitoring, and adapting evacuation strategies based on changing conditions.

1. An IoT Software Architecture
for an Evacuable Building
Architecture
Henry Muccini1, Claudio Arbib1, Paul Davidsson2,
Mahyar T. Moghaddam1
1 University of L’Aquila
2 Malmo University, Sweden
Slides available on my SlideShare account

2. Henry Muccini @HICSS 2019
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The co-authors
Software
Architecture
Operational
Research
IoT and
People
IoT architectures for
Evacuation Handling

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Context and Research Questions
RQ1: Which are the optimal building
dimensions/structures for a safe emergency
evacuation?
RQ2: How to minimize the time to evacuate
people in a building?
Context: safe emergency
evacuation in a closed space

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Context: building safe evacuation

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Context: building safe evacuation

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Context: building safe evacuation

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Limitations of the static emergency
evacuation plan
 possibly leading all pedestrians to the same route and
making that area highly crowded;
 ignoring the individual movement behavior of people
and special categories (e.g. elderly, children,
disabled);
 lack of a comprehensive understanding for evacuation
manager and operators by a real-time situational
awareness
 ignoring abrupt
congestion, obstacles or
dangerous routes and
areas;

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What if?

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Context: Solution Space
Cyber-
Physical
Spaces
IoT
Architecture
Network
Flow Model

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Context: Solution Space
Data Network Flow Model

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Goal of this Work
Simulations for:
 Building constraints
 Bottleneck discovery
 Comparing routing
optimization models
Monitoring for:
 Run-time adaptation of the
evacuation plan
 Discard paths that
become unfeasible
 Integration in mobile
applications
Design Time evacuability
assessment
Run-Time evacuation

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Solution for our goals
Evacuation Model:
 We propose a network flow algorithm
that is capable to support a precise
simulation at design-time and an optimal
evacuation handling at real-time.
IoT-based System Architecture:
 We propose an IoT System Architecture to
create an infrustructure for run-time
monitoring
1
2

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Evacuation Model
Network Flow Algorithm

14. 14
In brief
We propose a network flow algorithm that is
capable to support a precise simulation at design-
time and an optimal evacuation handling at real-time.

15. 15
Network Flow Algoritm
We solve a linearized, time-indexed flow problem
on a network that represents feasible movements of
people at a suitable frequency as a way to minimize
the total evacuation time
max the number of persons that occupy cell
0 (safe places) at time 

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Flow
conservation
law
Capacity
Congestion
model
Congestion
curve

17. 17
Model Construction
The model construction requires to explicitly
set a number of parameters:
1. Model granularity
2. Walking Velocity
3. Door capacity
4. Cell capacity

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Model Granularity - basic approach:
based on the rooms dimensions
1

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Model Granularity
Cell Shape
Compatiblility √ Compatiblility X Compatiblility √
Isometry X Isometry √ Isometry √
Rectangular Hexagonal Square

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Model Granularity
Cell Size
Low resolution High Resolution
Larger Error Smaller Error
Smaller CPU time Larger CPU time

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Graph
Nodes -> cell in the grid (space)
 Node 0 -> safe area
Arcs -> passages between adjacent cells
CS:112 nodes and 264 arcs

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Walking Velocity
This parameter is important to perceive the
distance that an individual can possibly walk
during a specific period of time.
It can vary for different categories of people, such as
child, adult, elderly, disable
2
FW

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Door capacity
Door capacity = how many people «p» may pass
through a door of size «d», every second «s»
Daamen et al. [12] focuses on the relationship
between door capacity, user composition and stress
level
 1.03 p/d/s - 3.23 p/d/s (d=1 meter)
 resulting from a literature review
 In our case, and since t = 5 seconds
 pessimistic – optimistic: 5 – 16 pp/d=1/s=5
3
CS: 5 – 16 pp/d=1/s=5

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Cell capacity
According to UK fire safety regulations, the
maximum allowed density corresponds
 0.3 square meters per standing person,
 0.5 s.m. for public houses, (2pp per s.m.)
 0.8 s.m. for exhibition spaces, (1.25pp per s.m.)
 1.0 s.m. for dining places, (1pp per s.m.)
 2.0 s.m. for sport areas, (0.5pp per s.m.)
 6 s.m. for office areas (0.2pp per s.m.)
4
CS: 1.25 pp per s.m.

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Risk Consideration
Static Risk
Such as earthquake: have a momentary impact on
building = static change in the graph
Dynamic Risk
Such as fire that propagates (Future work)
1 2 3
46 5
00

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IoT-based System Architecture
IoT infrustructure for run-time monitoring

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IoT Patterns
IoT Components
(data producers)
IoT Components
(data producers)
IoT Components
(data producers)

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IoT Patterns
IoT Components
(data producers)
IoT Components
(data producers)
IoT Components
(data producers)
Data consumer
Data consumer
Data consumer

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Self-adaptive IoT patterns

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CAPS MDE framework for IoT
Patterns Simulation
Centralized Master/Slave
Collaborative Regional Planning

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Simulation

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Simulation

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CS:112 nodes and 264 arcs
Walking Velocity: 1.20 m/s
Door Capacity: 5 – 16 pp/d=1/s=5
Cell Capacity: 1.25 pp per s.m.
Simulation:
 N persons,
 randomly distributed in the building rooms.
The code for simulation was written on OPL language and solved
on CPLEX version 12.8.0.

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Simulated Cases
N=1008 persons
Pessimistic case = door capacity: 5pp/d=1/s=5
Our model: 4 min 15’’ (Table)
Shortest path: 5 min 35’’
Optimistic case = 16 pp/1/5

36. 36Simulation with Risks
2 over 4 emergency exits are blocked
Evacuation time:
 pessimistic: 8 min. 25’’ against 4 min. 15’’
 optimistic: 4 min. against 1 min 20’’

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Simulation with different emergency
exits width
real optimal

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Ongoing and Future Work

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Microscopic Flow Modeling
 Based on Individuals Movements
 Reaction time, Grouping, Social Attachment
Fire propagation:
 has a nature of (run-time) arc removal
 maximize the amount of people in the safe node
Optimization/Simulation Approach
 We implemented our algo into PEDSIM
Smart City

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PEDSIM simulation

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Smart City – Future Work
 Multiple floors
 Multiple buildings
 Different evacuation
areas

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Research topic in collaboration with

43. An IoT Software Architecture
for an Evacuable Building
Architecture
Henry Muccini1, Claudio Arbib1, Paul Davidsson2,
Mahyar T. Moghaddam1
1 University of L’Aquila
2 Malmo University, Sweden
Slides available on my SlideShare account