Event-driven, Role-based Mobility in Disaster Recovery Networks

1. Event-driven, Role-based Mobility in Disaster Recovery Networks The Phoenix Project Robin Kravets Department of Computer Science University of Illinois

2. 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 Day After Networks The-Day-After Networks

3. 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 The-Day-After Networks Fire

4. 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 The-Day-After Networks

5. DAN Network Model Internet MESH WLAN 2/3 G DAN Cluster The-Day-After Networks MESH MESH WLAN WLAN BT 2/3 G 2/3 G DAN Cluster DAN Cluster

6. 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 Mapping the Network The-Day-After Networks

7. 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 The-Day-After Networks

8. 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 The-Day-After Networks

9. 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 The-Day-After Networks

10. Disaster Events • Events are stimuli for object reaction Immediate Reaction area becomes sparse, partitioning the network Event Horizon Civilians stop here heavy clustering Event The-Day-After Networks Radio Contact react only after radio contact occurs Damage Radius Objects become immobile

11. Objects and Roles • Roles define movement patterns for similar objects C: Civilians P: Police A: Ambulance C C P The-Day-After Networks C Event A Hospital P C Police and Ambulances outside of EH do not react before radio contact

12. Gravity-Based Reaction • Use gravity to model flee and approach • F = I / d2where I is the event intensity • Sum of all forces affects velocity vectors • Can quickly and dynamically handle any number of events acting on objects The-Day-After Networks Event Event C Resulting velocity vector

13. 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 The-Day-After Networks

14. 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 The-Day-After Networks

15. 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 The-Day-After Networks

16. Network Snapshots The-Day-After Networks 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 0 50 100 150 200 250 300 350 400 500 600 800 1000 1200 1400 Events Occur

17. Average Node Density Disaster Mobility Model Random Walk 8.5 8 7.5 7 Average Node Density 6.5 6 The-Day-After Networks • Connected components are “more connected” in the disaster mobility model 5.5 5 0 250 500 750 1000 1250 1500 Time (s)

18. Disaster Mobility Model Random Walk 0.85 0.8 Clustering Coefficient 0.75 0.7 0.65 0.6 0 250 500 750 1000 1250 1500 Time (s) Clustering Coefficient The-Day-After Networks • How well a node’s neighbors know each other. • Disaster mobility model topology is more clustered, and becomes clustered quickly after events.

19. Random Walk Disaster Mobility Model 1 0.9 0.8 0.7 Partition 0.6 0.5 0.4 0.3 0.2 0.1 0 0 250 500 750 1000 1250 1500 Time (s) Network Partitioning The-Day-After Networks • 1 = partitioned, 0 = not partitioned • Indicates DTN-style routing may be necessary, with ambulances acting as bridges.

20. Event-driven, Role-based Mobility in Disaster Recovery Networks Robin Kravets Department of Computer Science University of Illinois http://mobius.cs.uiuc.edu/ The Phoenix Project http://mobius.cs.uiuc.edu/phoenix.htm