Mazlan Abbas (MIMOS Berhad), Nur Husna Md Yusof
(Universiti Teknologi Malaysia) and Norsheila Fisal (Universiti
Teknologi ...
Outline
•  Motivation
•  Routing Challenges in Emergency
Scenario
•  Simulation Result
•  Summary
•  Future Work
Issues of Communications During Disasters and Emergency
Terrorist'A7ack' Hurricane'
Tsunami' Earthquake'
Lack'of'
adequate...
Opportunistic Networks (OppNets)
To'enable'communicaLon'between'source'and'desLnaLon'
without'the'support'of'a'fixed'networ...
Smartphones – The Enabler
Camera'GPS'
WiFi'
Big'Storage'
CPU'Power'
History of Delay Tolerant Network
•  Interplanetary'Internet'(IPN)'is'a'NASA'research'project'led'by'Vint'Cerf'in'
1998.
•...
Delay Tolerant Networking (DTN)
•  DTN'is'a'set'of'protocols'that'
act'together'to'enable'a'
standardized'method'of'
perfo...
8'
3G'
WiFi'
WiFi'
WiFi'
3G'
3G'
3G'Base''
staLon'
OppNet'in'Emergency'Response'Scenario'
Public Safety
9'
3G'
WiFi'
WiFi'
WiFi'
3G'
3G'
Base''
StaLon'down'
X' X'
X'X'WiFi' WiFi'
OppNet'in'Emergency'Response'Scenario'
Public S...
10'
3G'
WiFi'
WiFi'
WiFi'
3G'
3G'
Base''
StaLon'down'
X' X'
X'X'WiFi' WiFi'
Internet'
Internet'
Ability'to'Connect'to'Inte...
11'
3G'
WiFi'
WiFi'
WiFi'
3G'
3G'
Base''
StaLon'down'
X' X'
X'X'WiFi' WiFi'
Internet'
Internet'
DTN'Gateway'
DTN'Gateway'
C1'
C6'
C3'
C2'
C3'
C5'
Node'Mobility'
Source'
DesLnaLon'
Routing Challenges
Example:$Disaster'relief'efforts,'mining'opera...
N1'
Factors That Impact Performance
N2'
Mobility'Pa7ern'
Node'Speed'
Type'of'CommunicaLon'
Transmission'Range'
Buffer'Size'...
1
4
Routing Protocols – Related Works (1)
•  The Direct Delivery does not start any further transactions after
exchanging ...
1
5
Routing Protocols – Related Works (1)
•  Predictive protocols such as PRoPHET [Lindgren et al 2004] use past
encounter...
1
6
Spray & Wait - A Better Choice
16'
Spray'&'Wait'–'High'delivery'probability' Spray'&'Wait'–'Low'overhead'(First'contac...
17'
Spray and Wait Routing Protocol
•  In Spray and Wait, message is delivered in two phases;
the spray phase and the wait...
18'
Performance Metrics
•  Delivery probability: It is a ratio between the
number of messages arrives at destination and t...
1
9
SimulaLon'tool:'ONE'(OpportunisLc'Networking'Environment)'
Helsinki'Downtown'Map'(4.5km'x'3.4'km)'
Assumptions
19'
20'
Parameter' Value'
SimulaLon'Time'(s)' 43200'
SimulaLon'Area'(sq.m)' 4500x3400'
RouLng'Protocol' Binary'Spray'and'Wait'...
21'
Experiment' Group' Number'of'Nodes' Speed'(m/s)'
1' Pedestrians' 20' 0.51.5''
2' Cars' 20' 2.7'–'13.9'
3' Pedestrians'...
22'
Varying the Buffer Size
Delivery'Probability'vs.'Buffer'Size''
Impact'on'Delivery'Probability'
Speed'of'nodes'does'not'...
23'
Varying the Buffer Size
Number'of'Messages'Dropped'vs.'Buffer'Size''
Impact'on'Number'of'Messages'Dropped'
Beyond'30'MB...
24'
Varying the Buffer Size
Latency'Average'vs.'Buffer'Size''
Impact'on'Latency'Average'
Smaller'number'of'
nodes'has'highe...
25'
Varying the Buffer Size
Hop'Count'vs.'Buffer'Size''
Impact'on'Average'Hop'Count'
26'
Experiment' Group' Number'of'Nodes' Speed'(m/s)'
1' Pedestrians' 20' 0.51.5''
2' Cars' 20' 2.7'–'13.9'
3' Pedestrians'...
27'
Varying the Message Size
Delivery'Probability'vs'Message'Size'
Impact'on'Delivery'Probability'
Slower'nodes'able'to'ca...
28'
Varying the Message Size
Average'Latency'vs'Message'Size'
Impact'on'Average'Latency'
29'
Summary
•  Opportunistic Networks (OppNets) are very
useful in the context of emergency scenarios
•  Binary Spray and ...
Future Work
•  Requires actual datasets (emergency scenarios)
for mobility model
•  Mixed mobility scenarios (pedestrians ...
THANK YOU
Contact: mazlan.abbas@mimos.my
@mazlan_abbas
h7ps://www.facebook.com/drmazlanabbas'
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Performance Evaluation of Binary Spray and Wait OppNet Protocol in the Context of Emergency Scenario

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Paper presented at PerNEM 2013, San Diego, USA (18-22 May, 2013)

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Performance Evaluation of Binary Spray and Wait OppNet Protocol in the Context of Emergency Scenario

  1. 1. Mazlan Abbas (MIMOS Berhad), Nur Husna Md Yusof (Universiti Teknologi Malaysia) and Norsheila Fisal (Universiti Teknologi Malaysia) Performance Evaluation of Binary Spray and Wait OppNet Protocol in the Context of Emergency Scenario PerNEM'2013,'San'Diego,'USA' Contact: mazlan.abbas@mimos.my @mazlan_abbas h7ps://www.facebook.com/drmazlanabbas'
  2. 2. Outline •  Motivation •  Routing Challenges in Emergency Scenario •  Simulation Result •  Summary •  Future Work
  3. 3. Issues of Communications During Disasters and Emergency Terrorist'A7ack' Hurricane' Tsunami' Earthquake' Lack'of' adequate' communic aLons'
  4. 4. Opportunistic Networks (OppNets) To'enable'communicaLon'between'source'and'desLnaLon' without'the'support'of'a'fixed'network'infrastructure'
  5. 5. Smartphones – The Enabler Camera'GPS' WiFi' Big'Storage' CPU'Power'
  6. 6. History of Delay Tolerant Network •  Interplanetary'Internet'(IPN)'is'a'NASA'research'project'led'by'Vint'Cerf'in' 1998. •  The'basic'idea'is'to'try'to'make'data'communicaLons'in'space/'between' planets. •  IPN'became'the'most'fundamental'basis'for'DTN'architecture'and'protocol' suite.' •  The'Interplanetary'Internet'is'a'disconnected,'storeand'forward' network'of'Internets 'based'on'a'wireless'backbone'with'huge' delays'(The'delay'in'sending'or'receiving'data'from'Mars'takes' between'3.5'to'20'minutes'at'the'speed'of'light)'and'error'prone' links •  Failing'of'IP/TCP'in'space'missions –  Endtoend'path'exist –  Small'delays'
  7. 7. Delay Tolerant Networking (DTN) •  DTN'is'a'set'of'protocols'that' act'together'to'enable'a' standardized'method'of' performing'storecarryand forward'communicaLons. •  CharacterisLcs'of'DTN: i.  Intermi7ent'connecLvity –  No'endtoend'path'between'source' and'desLnaLon ii.  Long'variable'delay –  Long'propagaLon'delays'between' nodes' A' B' B' C' C' D' Source' Store' Carry' Forward' Store' Carry' Forward' Delay'Tolerant'Network'(DTN)'='Mobile'OpportunisLc'Network'(OppNet)'
  8. 8. 8' 3G' WiFi' WiFi' WiFi' 3G' 3G' 3G'Base'' staLon' OppNet'in'Emergency'Response'Scenario' Public Safety
  9. 9. 9' 3G' WiFi' WiFi' WiFi' 3G' 3G' Base'' StaLon'down' X' X' X'X'WiFi' WiFi' OppNet'in'Emergency'Response'Scenario' Public Safety Smartphones'(Nodes)'can'be' carried'by' Pedestrians 'or' Vehicles ' Send'“SOS”'messages' Send'photos'of'vicLms'or'self' Can$we$send$videos?$ What$kind$of$file$size?$
  10. 10. 10' 3G' WiFi' WiFi' WiFi' 3G' 3G' Base'' StaLon'down' X' X' X'X'WiFi' WiFi' Internet' Internet' Ability'to'Connect'to'Internet'at' Remote'Ends' Public Safety Ques7on:$Can$smartphones$help$us$ during$Emergency$Situa7on?$
  11. 11. 11' 3G' WiFi' WiFi' WiFi' 3G' 3G' Base'' StaLon'down' X' X' X'X'WiFi' WiFi' Internet' Internet' DTN'Gateway' DTN'Gateway'
  12. 12. C1' C6' C3' C2' C3' C5' Node'Mobility' Source' DesLnaLon' Routing Challenges Example:$Disaster'relief'efforts,'mining'operaLons,'health'campaigns' In'emergency'situaLons,'enLLes'with'any'sensing'capabiliLes''such'as'cellphones'with'GPS'or'desktops' equipped'with'surveillance'cameras,'can'be'especially'valuable'for'the'OppNet.' C4'
  13. 13. N1' Factors That Impact Performance N2' Mobility'Pa7ern' Node'Speed' Type'of'CommunicaLon' Transmission'Range' Buffer'Size' Ba7ery'Life'TimetoLive'Message'Size' RouLng'Mechanism' Number'of'Nodes' Size'of'Area'
  14. 14. 1 4 Routing Protocols – Related Works (1) •  The Direct Delivery does not start any further transactions after exchanging the deliverable messages since it will send messages only if it is in contact with the final recipient. •  While in the First Contact routing, it sends as many messages to the other node as it has time; it removes the local copy of the message after a successful transfer. This results in only a single copy of every message in the network. •  Epidemic routing [Vahdat et al 2000] spreads an unlimited number of message copies by having nodes replicate them to all other nodes they connect. This includes the messages they create and the messages they have received from other nodes. 14'
  15. 15. 1 5 Routing Protocols – Related Works (1) •  Predictive protocols such as PRoPHET [Lindgren et al 2004] use past encounters of nodes to predict their future suitability to deliver messages to a certain target. It uses a metric called delivery predictability that is based upon how often two nodes meet each other. The more frequently and the more recently these nodes have met, the better a forwarder one is for messages directed to the other. •  While PRoPHET checks if another node is more likely to meet the final recipient, MaxProp [Burgess et al 2006] uses Dijkstra s algorithm to calculate whole paths from node to node using the meeting probabilities. •  The Spray and Wait [Spyropoulos et al 2005] works a bit like the Epidemic but it restricts the amount of copies that are spread in the network. Letting each created message to replicate only a certain amount of times. A node that has more than one copy of the message left, can give either a one copy to another node (the normal mode) or half of the copies (the binary mode). 15'
  16. 16. 1 6 Spray & Wait - A Better Choice 16' Spray'&'Wait'–'High'delivery'probability' Spray'&'Wait'–'Low'overhead'(First'contact'don’t'have' good'delivery'probability)' Spray'&'Wait'–'Low'latency' [Source:'“performance'of'Challenged'Internet'of'Things'in'Emergency'Response”,'M.'Abbas'and'N.H.Md.'Yusuf,'Technical'Report,'2012]' Shortest$Path$MapDBased$Movement$Model$–'Assuming'in' most'emergency'situaLon,'people'and'vehicle'will'move' along'the'roads.'' !
  17. 17. 17' Spray and Wait Routing Protocol •  In Spray and Wait, message is delivered in two phases; the spray phase and the wait phase. •  In the spray phase, source node spread a small number of copies to only a few relays. A node that has more than one copy of the message left can give either a copy to another node (the normal mode) or half of the copies (the binary mode) and keeps the rest to itself. •  In the wait phase, if the node has only a single copy of the message left, it is directly transmitted only to the destination. •  We use Spray-and-Wait in binary mode: a node carrying k copies of a message forwards k/2 of them to the next nodes it meets until the k =1. Then, a node waits till it meets the destination.
  18. 18. 18' Performance Metrics •  Delivery probability: It is a ratio between the number of messages arrives at destination and the number of messages sent. •  Message dropped: It is the number of messages dropped from nodes' buffers during transmission. Messages are dropped once the buffer is full. •  Latency average: The latency average is an average time taken for a message to reach destination. •  Hop count average: It is an average number of hops between source and destination nodes.
  19. 19. 1 9 SimulaLon'tool:'ONE'(OpportunisLc'Networking'Environment)' Helsinki'Downtown'Map'(4.5km'x'3.4'km)' Assumptions 19'
  20. 20. 20' Parameter' Value' SimulaLon'Time'(s)' 43200' SimulaLon'Area'(sq.m)' 4500x3400' RouLng'Protocol' Binary'Spray'and'Wait' Number'of'Copies' 6' Mobility'Model' Shortest'Path'Map'Based'Movement' Transmission'Range'(m)' 10' Transmit'Speed'(Mb/s)' 10' TTL'(minutes)' 300' Main Simulation Parameters
  21. 21. 21' Experiment' Group' Number'of'Nodes' Speed'(m/s)' 1' Pedestrians' 20' 0.51.5'' 2' Cars' 20' 2.7'–'13.9' 3' Pedestrians' 100' 0.5'–'1.5' 4' Cars' 100' 2.7'–'13.9'' Experiments – Varying Buffer Size Parameter' Value' Buffer'size'(MB)' 5100' Message'size'(B)' 500k'–'1M' Message'generaLon' Every'30'seconds'
  22. 22. 22' Varying the Buffer Size Delivery'Probability'vs.'Buffer'Size'' Impact'on'Delivery'Probability' Speed'of'nodes'does'not' ma7er'with'buffer'size'but' number'of'nodes'does' ma7er' Higher'loss'of'messages'when'buffer'size' is'too'small.'OpLmum'at'30'MB.' Messages'eventually'have'to'be'dropped' if'it'exceeded'the'TTL.'
  23. 23. 23' Varying the Buffer Size Number'of'Messages'Dropped'vs.'Buffer'Size'' Impact'on'Number'of'Messages'Dropped' Beyond'30'MB'buffer'size,'the'number'of'messages'being'dropped'remained'the'same.'
  24. 24. 24' Varying the Buffer Size Latency'Average'vs.'Buffer'Size'' Impact'on'Latency'Average' Smaller'number'of' nodes'has'higher' latency.'
  25. 25. 25' Varying the Buffer Size Hop'Count'vs.'Buffer'Size'' Impact'on'Average'Hop'Count'
  26. 26. 26' Experiment' Group' Number'of'Nodes' Speed'(m/s)' 1' Pedestrians' 20' 0.51.5'' 2' Cars' 20' 2.7'–'13.9' 3' Pedestrians' 100' 0.5'–'1.5' 4' Cars' 100' 2.7'–'13.9'' Experiments – Varying Message Size Parameter' Value' Message'size'' 500'kB''to''5'MB' Buffer'size' 30'MB'
  27. 27. 27' Varying the Message Size Delivery'Probability'vs'Message'Size' Impact'on'Delivery'Probability' Slower'nodes'able'to'carry'bigger'message'sizes'
  28. 28. 28' Varying the Message Size Average'Latency'vs'Message'Size' Impact'on'Average'Latency'
  29. 29. 29' Summary •  Opportunistic Networks (OppNets) are very useful in the context of emergency scenarios •  Binary Spray and Wait Protocol is one good option for routing •  Smartphones seems to be a good potential candidate communications tool in emergency scenarios •  Speedier nodes (cars) require smaller Message size (images rather than videos).
  30. 30. Future Work •  Requires actual datasets (emergency scenarios) for mobility model •  Mixed mobility scenarios (pedestrians plus vehicles) •  Find better battery efficient methods •  Further refinements to Spray & Wait Protocol with other parameters such as Contact Time etc. •  Implementation on smartphones – e.g. WiFi Direct 802.11ac or Bluetooth
  31. 31. THANK YOU Contact: mazlan.abbas@mimos.my @mazlan_abbas h7ps://www.facebook.com/drmazlanabbas'

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