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Cooperative and Reliable Packet-Forwarding on Top of AODV. Tal Anker, Danny Dolev, Bracha Hod The Hebrew University of Jerusalem. Outline. Introduction Background: Trust and Reputation Motivation and Contribution Solution Highlight Simulation Framework and Results Conclusions.

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cooperative and reliable packet forwarding on top of aodv

Cooperative and Reliable Packet-Forwarding on Top of AODV

Tal Anker, Danny Dolev, Bracha Hod

The Hebrew University of Jerusalem

outline
Outline
  • Introduction
  • Background: Trust and Reputation
  • Motivation and Contribution
  • Solution Highlight
  • Simulation Framework and Results
  • Conclusions
introduction
Introduction
  • Mobile ad-hoc networks are vulnerable to many attacks by selfish or malicious nodes
  • The adversary model – data packet dropping
    • Black hole node drops all the data packets
    • Gray hole node adversary selectively drops some data packets but not other
    • Other malicious attacks are beyond the scope of this work
  • Recent approaches
    • Watchdog and Pathrater [Marti, Giuli, Lai and Baker 2000]
    • CONFIDANT [Buchegger and Le Boudec 2002]
    • CORE [Michiardi and Molva 2002]
    • OCEAN [Bansal and Baker 2003]
trust and reputation
Trust and Reputation
  • Trust and reputation use past behavior to predict current behavior
    • Trust is based on direct experience
    • Reputation is derived from both direct and indirect information
    • In a reputation system, nodes compute and advertise rating values
  • Direct information and indirect information can also be referred as first-hand observation and second-hand observation, respectively
motivation and contribution
Motivation and Contribution
  • AODV is one of the leading routing protocol for MANET
  • Most solutions have focus on DSR
    • DSR nodes access greater amount of information which enable their more rapid recovery from misbehaving nodes
      • Full-path routing
      • Multiple paths
    • AODV is much more scalable
  • Our work is the first reputation system on top of AODV
first hand observations
First-hand Observations
  • Neighbors’ monitoring to detect misbehavior using passive acknowledgment (a.k.a. watchdog)
    • Each node overhears its neighbors and records their positive and negative actions
    • Inherent weaknesses, such as collisions
    • Weaknesses in AODV
      • Need for “next hop” information
      • Mistakes in several situations, e.g., dropping during local repair
    • Associated with less overhead and delay
second hand observations
Second-hand Observations
  • Reputation system based on the Beta distribution function
    • Direct rating together with positive and negative actions are derived from the direct observations
    • Rating exchange between neighbors periodically
    • Total rating is an incorporation of the direct and indirect rating. It is used to classify the nodes
    • Trust is used to defend against liars
    • Local model as a result of MANET constrains
misbehavior reaction
Misbehavior Reaction
  • Nodes’ classification
    • Total rating value with positive and negative actions
      • Positive actions estimate load
      • Negative actions estimate misbehavior
    • Two nodes with the same total rating, but with different history are classified differently
  • Path selection
    • Greedy selection of the next hop
    • Path maintenance for partial dropping
  • Misbehaving nodes’ punishment
    • Second chance when the rating is faded
simulation model
Simulation Model
  • Simulation in GloMoSim
  • Standard parameters of the channel and radio model
  • IEEE 802.11 as the medium access protocol
  • Nodes are placed randomly in the area
    • Area of 1000x1000, 1500x1500 and 5000x5000 meters for 50, 100 and 500 nodes respectively
  • Movement by Random waypoint model
    • Speed range of 5-20 m/s
    • Pause time range of 0-500s
  • Data packets transmission at CBR on routes above 1-hop length
throughput of well behaving nodes
Throughput of Well-behaving Nodes

50 Nodes 100 Nodes

15 Sources, 15 Black-holes 20 Sources, 30 Black-holes

punishment of misbehaving nodes
Punishment of Misbehaving Nodes

Data Packets Transmitted Data Packets for

by Misbehaving Nodes Misbehaving Nodes That

were not Transmitted

50 Nodes, 15 Sources, 15 Black-holes

partial dropping gray holes
Partial Dropping (Gray holes)

Data Packets Dropped

Dropping percentage of 50% Different Dropping

(32% of the total rating) Percentages

50 Nodes, 15 Sources, 15 Gray-holes

robustness against advanced liars
Robustness against Advanced Liars

Data Packets Received False Positives

50 Nodes, 15 Sources, 10 Black-holes

scalability over aodv
Scalability over AODV

Throughput Data Packets Dropped

500 Nodes, 250 static and the remainder walk

on speed of 5-10 m/s. 30 Sources, 50 black holes.

conclusions
Conclusions
  • A reputation system on top of AODV is effective for both partial and complete dropping
  • The reputation system remained robust against advanced liars, when a majority of the nodes are trustworthy
  • In large networks, it is better to rely on self-observations because the network conditions have greater effect than the reputation system benefits
direct rating
Direct Rating
  • Direct rating of a node j by its neighbor i
total rating
Total Rating

past actions current actions indirect info.

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