event-driven, role-based mobility in disaster recovery networks
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Event-driven, Role-based Mobility in Disaster Recovery Networks. The Phoenix Project Robin Kravets Department of Computer Science University of Illinois. Consider the aftermath of a natural disaster No power Damaged communication infrastructure Cell towers Switching stations - PowerPoint PPT PresentationTRANSCRIPT
Event-driven, Role-based Mobility in Disaster Recovery Networks
The Phoenix Project
Robin KravetsDepartment of Computer ScienceUniversity of Illinois
Robin Kravets, UIUC - January 2007
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Day After Networks
Consider the aftermath of a natural disaster No power Damaged communication
infrastructure Cell towers Switching stations Emergency response
Goal Survivable communication
and networking in post disaster scenarios Support disaster
recovery efforts Provide connectivity to
survivors
Robin Kravets, UIUC - January 2007
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Communication in DANs
Emergency personnel Police, Fire, EMS, FEMA, … Intra-agency communication
Dedicated pre-configured networks
Inter-agency communication Civilians
Survivors Communication with
emergency personnel Communication with family
Police
Fire
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Post-disaster Communications
Challenge Disconnected operation Service oriented More resources may not help No fixed infrastructures
Approach Hop-by-hop communication paradigm Group based communication Take advantage of all available resources
Personal wireless devices Cell-phones in P2P mode Car-battery-operated WiFi mesh nodes Traditional Radio
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DAN Network Model
Internet
WLAN
2/3 G
MESH
DAN Cluster
WLAN
2/3 G
MESH
DAN Cluster
BT
DAN Cluster
WLAN
2/3 G
MESH
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Mapping the Network
Network nodes Static and dynamic Locally roaming
relief workers assigned a location
Globally roaming Patrolling police
vehicles A notion of recurrence
Workers tend to perform repetitive tasks
Chain of command entails fixed reporting hierarchies
Research challenges Mobility and
connectivity patterns Take advantage of
recurrence predictions to maximize delivery ratio
Provide resource sharing incentives
Detect and protect against malicious behavior
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Challenges
Network Topology Inherently partitioned
Response personnel are close to event People stand around far from event
Node Behavior Role-based
Responders act differently then civilians Vehicles may oscillate between events and bases
Event-based Behavior may change based on specific events
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Mobility Models
Fixed Models Random Movement
Walk in a randomly chosen direction Object Avoidance
Walk around objects, buildings, etc. Flocking
Walk with others in your group
Challenge Current models require all nodes to follow the same
behavior Although mobility is random, there is no support for
reaction to events
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Event-driven, Role-based Mobility
Observation Object movement is heavily dependent on
events Event characteristics Proximity to event
Object reactions are completely dependent on the current role of the object Civilians flee from events Police gravitate towards them Ambulances move between events and hospitals
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Disaster Events
Events are stimuli for object reaction
Event
Damage RadiusObjects become immobile
Event HorizonCivilians stop here heavy clustering
Immediate Reaction area becomes sparse, partitioning the network
Radio Contactreact only after radio contact occurs
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Objects and Roles
Roles define movement patterns for similar objects
Event
C
C
C
C
P
P
A
HospitalPolice and Ambulances outside of EH do not react before radio contact
C: CiviliansP: PoliceA: Ambulance
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Gravity-Based Reaction
Use gravity to model flee and approach F = I / d2 where I is the event intensity Sum of all forces affects velocity vectors
Can quickly and dynamically handle any number of events acting on objects
Event Event
C
Resulting velocity vector
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Topological Metrics
Common metrics Average node density Average path length
Problem These do not capture the characteristics of disaster
networks Better metrics
Time based Is graph partitioned at a given time? How clustered is the graph at a given time? Average, maximum, and variance of node density over time
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Simulation Tools
Two tools help generate ns2 mobility trace files from simple input parameters
paramGen > paramFile Input: size, #civilians, #police, #ambulance Output: Structured list of randomized and deterministic
parameters
disasterSim [-d] < paramFile > nsMobilityTrace Input: paramFile Output: runs complete simulation with our disaster mobility
model and produces nsMobilityTrace file
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Simulation Setup
1500 seconds long on 1000m2 grid 4 events, 75 civilians, 10 police, and 15 ambulances
randomly placed 10 sets of simulations, each set containing 2
simulations: One with events (simulating our disaster mobility model) One without events (simulating Random Walk) Same parameters used within a set
Events & radio contact occurs between 100 and 355 seconds
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Network Snapshots
0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 14000 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400
Events Occur
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Average Node Density
Connected components are “more connected” in the disaster mobility model
5
5.5
6
6.5
7
7.5
8
8.5
0 250 500 750 1000 1250 1500
Time (s)
Ave
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Disaster Mobility Model Random Walk
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Clustering Coefficient
How well a node’s neighbors know each other. Disaster mobility model topology is more clustered,
and becomes clustered quickly after events.
0.6
0.65
0.7
0.75
0.8
0.85
0 250 500 750 1000 1250 1500
Time (s)
Clu
ste
rin
g C
oe
ffic
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t
Disaster Mobility Model Random Walk
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Network Partitioning
1 = partitioned, 0 = not partitioned Indicates DTN-style routing may be necessary, with
ambulances acting as bridges.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 250 500 750 1000 1250 1500
Time (s)
Par
titi
on
Random Walk Disaster Mobility Model
Event-driven, Role-based Mobility in Disaster Recovery Networks
Robin KravetsDepartment of Computer ScienceUniversity of Illinoishttp://mobius.cs.uiuc.edu/
The Phoenix Projecthttp://mobius.cs.uiuc.edu/phoenix.htm