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Fuzzy Trust Recommendation Based on Collaborative Filtering for Mobile Ad-hoc Networks. Junhai Luo 1,2 , Xue Liu 1 , Yi Zhang 3 ,Danxia Ye 2 ,Zhong Xu 1 1 McGill University 2 University of Electronic Science and Technology of China 3 University of California September 2008. Outline. - PowerPoint PPT Presentation
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Fuzzy Trust Recommendation Based on Fuzzy Trust Recommendation Based on Collaborative Filtering for Mobile Ad-hoc Collaborative Filtering for Mobile Ad-hoc NetworksNetworks
Junhai Luo1,2, Xue Liu1 , Yi Zhang 3 ,Danxia Ye2 ,Zhong Xu1
1McGill University 2University of Electronic Science and Technology of China 3University of California
September 2008
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OutlineOutlineMotivationsRelated WorkArchitectureAlgorithm RealizationPerformance EvaluationConclusion and Future Work
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MotivationsMotivationsMANETs characteristics:
◦Cooperative
◦Autonomous
◦Self-organized
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SS
DD
ij
Motivations(cont.)Motivations(cont.)
◦Low power
◦Multi-hop
◦Vulnerable to various attacks
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PDA
Pen computer
Laptop computerLaptop computer
PDA
Motivations(cont.)Motivations(cont.)
1) High trust value = ? High or correct recommendation to other nodes.
2) Uncertain
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Why?
Motivations(cont.)Motivations(cont.)Methods
◦ Collaborative filtering
◦ Fuzzy logic
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Related WorkRelated WorkCONFIDANT [1]
◦ DSR (Dynamic Source Routing) with reputation systemNUGLETs [2]
◦ Virtual currencySORI [3]
◦ Secure and objective reputation schemeCORE [4]
◦ Collaborative observations and reputation mechanism
[1] S. Buchegger and J.-Y. L. Boudec, Performance analysis of the confidant protocol,” in MobiHoc ’02: Proceedings of the 3rd ACM international symposium on Mobile ad-hoc networking & computing. New York, NY, USA: ACM, 2002, pp. 226–236
[2] L. Buttyan and J.-P. Hubaux, “Nuglets: a Virtual Currency to Stimulate Cooperation in Self-Organized Mobile Ad Hoc Networks,” Tech. Rep., 2001
[3] Q. He, D. Wu, and P. Khosla, “Sori: a secure and objective reputation based incentive scheme for ad-hoc networks,” Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE, vol. 2, pp. 825–830 Vol.2, 21-25 March 2004.
[4] P. Michiardi and R. Molva, “Core: a collaborative reputation mechanism to enforce node cooperation in mobile ad-hoc networks,” in Proceedings of the IFIP TC6/TC11 Sixth Joint Working Conference on Communications and Multimedia Security. Deventer, The Netherlands, The Netherlands: Kluwer, B.V., 2002, pp. 107–121.
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ArchitectureArchitecture
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i j2
jK
j1
…
K
Ri Rjk,m Rj
Ri,m
cos( , )i j
m
AlgorithmAlgorithm RealizationRealizationLocal trust Value
Collaborative filtering
Fuzzy trust recommendation
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Algorithm Realization(cont.)Algorithm Realization(cont.)Local Trust Value
◦Neighbor monitoring[3]
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,
( )
( )j
j mj
HF mR
RF m
( )jHF m Number of packets forwarded by node m
( )jRF m Number of packets Requested for Forwarding by node j
Algorithm Realization(cont.)Collaborative FilteringCollaborative Filtering
◦Similarity Functions
Cosine-Based Similarity
Correlation-Based Similarity
Adjusted Cosine Similarity
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Algorithm Realization(cont.)Algorithm Realization(cont.)Fuzzy Method
◦Fuzzy Membership Function
◦Fuzzy Levels
◦Fuzzy Inference
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Algorithm Realization(cont.)Algorithm Realization(cont.)Fuzzy Membership Function
◦ Trapezoid Membership Function (TMF)2 3
1 4
11 2
2 1
43 4
3 4
1 a x a
0 x=a or x=a
a <x<a( )
a <x<a
x ax
a a
x a
a a
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a1a2 a3 a4
1
0
Trust Levels
Degree
Algorithm Realization(cont.)Algorithm Realization(cont.)Fuzzy Levels
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Trust level Description Trapezoid Membership Function
HD High Distrust [-1, -0.8, -0.6]
D Distrust [-0.8, -0.6, -0.4,-0.2]
UD Undistrust [-0.4, -0.2, 0]
UT Untrust [0, 0.2, 0.4]
T Trust [0.2, 0.4, 0.6,0.8]
HT High Trust [0.6, 0.8, 1]
U Unknown [0,0,0,0]
Algorithm Realization(cont.)Algorithm Realization(cont.)Fuzzy Inference
◦Inference rule :
IF …THEN rule
For example:
IF temperature is very cold THEN turn off fan
IF temperature is very hot THEN speed up fan
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Algorithm Realization(cont.)Algorithm Realization(cont.)
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Start
Set node-nearest-neighbors
Retrieve node'sevaluation
Calculate thecorrelation coefficient
Calculate similarity based on fuzzy reference
Compute the trustrecommendation
End
K
Performance EvaluationPerformance EvaluationEvaluation Metrics
◦Mean Absolute Error (MAE):
Tri value of trust recommendation
Rri value of real evaluation
◦ Average Packet Drop Ratio (APDR):
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1
N
i iiTr Rr
MAEN
1
1
N
DropediN
Originatedi
PacketsAPDR
Packets
Performance Evaluation(cont.)Performance Evaluation(cont.)Evaluation Setup
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Parameter Value
MAC 802.11/b
Area
Speed [5,20]
Radio range 250
Placement Uniform
Movement Random waypoint
Application CBR
Sending capacity 2Mbps
Packet size 64B
Simulation time 900s
1000 1000m m
m
Performance Evaluation(cont.)Performance Evaluation(cont.)Mean Absolute Error (MAE)
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NN SM Cosine Correlation Adjusted cosine
5 1.332 1.335 1.283
10 1.313 1.322 1.302
15 1.286 1.280 1.278
20 1.302 1.300 1.279
25 1.288 1.302 1.288
30 1.294 1.295 1.293
35 1.331 1.332 1.300
40 1.279 1.299 1.279
45 1.336 1.299 1.289
50 1.291 1.333 1.290
Performance Evaluation(cont.)Performance Evaluation(cont.)
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Performance Evaluation(cont.)Performance Evaluation(cont.)Average Packet Drop Ratio(APDR)
04/21/23 21
Conclusion and Future WorkConclusion and Future WorkA fuzzy trust recommendation based on collaborative
filtering for MANETs.
Combining local trust and trust recommendation information based on collaborative filtering to allow nodes to represent and reason with uncertainty and imprecise information regarding other nodes' trust.
Some attack models will be done in the paper in the future.
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QuestionsQuestions
?
04/21/23 24