Cooperative and reliable packet forwarding on top of aodv
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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

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Cooperative and Reliable Packet-Forwarding on Top of AODV

Tal Anker, Danny Dolev, Bracha Hod

The Hebrew University of Jerusalem


  • Introduction

  • Background: Trust and Reputation

  • Motivation and Contribution

  • Solution Highlight

  • Simulation Framework and Results

  • Conclusions


  • 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 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

  • 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

  • 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

  • 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

  • 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 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

50 Nodes 100 Nodes

15 Sources, 15 Black-holes20 Sources, 30 Black-holes

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)

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

Data Packets Received False Positives

50 Nodes, 15 Sources, 10 Black-holes

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.


  • 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


The Beta Distribution Function

Direct Rating

  • Direct rating of a node j by its neighbor i

Total Rating

past actions current actions indirect info.

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