This presentation is associated to a paper presented at HICSS 2019 (http://hicss.hawaii.edu/program-hicss52/).

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

13. 13
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

23. 23
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

24. 24
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

32. 32
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