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LDTS: A Lightweight and Dependable Trust System for Clustered Wireless Sensor Networks. Author s: Xiaoyong Li, Feng Zhou, and Junping Du. Presented by: Ting Hua. Outline. Motivation C lustered WSN M odel Lightweight Scheme for Trust Decision-Making Theoretical analysis and evaluation

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slide1

LDTS: A Lightweight and Dependable Trust System

for Clustered Wireless Sensor Networks

Authors: Xiaoyong Li, Feng Zhou, and Junping Du

Presented by: Ting Hua

outline
Outline
  • Motivation
  • Clustered WSN Model
  • Lightweight Scheme for Trust Decision-Making
  • Theoretical analysis and evaluation
  • Simulation-based analysis and evaluation
  • Conclusion
motivation
Motivation
  • Limited work focus on
    • Resource efficiency of clustered WSNs
      • fail to consider the problem of resource constraints of nodes
      • used complex algorithms to calculate nodes’ trustworthiness
    • Dependability of the trust system itself
      • Current: collect remote feedback and then aggregate s such feedback to yield the global reputation for the nodes
      • Problem: How about open or hostile WSN environment contains a large number of undependable (or malicious) nodes?
outline1
Outline
  • Motivation
  • ClusteredWSNModel
  • Lightweight Scheme for Trust Decision-Making
  • Theoretical analysis and evaluation
  • Simulation-based analysis and evaluation
  • Conclusion
c lustered wsn m odel
Clustered WSN Model
  • Nodes
    • CH: cluster head
    • CM: cluster member
    • BS: base station
  • Communications
    • Inter-cluster: A CM can communicate with their CH directly.
    • Intra-cluster: A CH can forward the aggregated data to the central BS through other CHs.
outline2
Outline
  • Motivation
  • Clustered WSN Model
  • Lightweight Scheme for Trust Decision-Making
  • Theoretical analysis and evaluation
  • Simulation-based analysis and evaluation
  • Conclusion
trust decision making at cm level
Trust Decision-Making at CM Level
  • Decision making: past interaction records?
    • Yes: CM-to-CM Direct trust degree (DTD)
      • # of successful and unsuccessful interactions
      • Interaction: cooperation of two CMs, e.g., node x sends a message to CH ivia node y
        • Successful: node y forwarded such message to CH
        • Unsuccessful:
          • No retransmission of the packet within a threshold time
          • Overheard packet is illegally fabricated
    • No: CH-to-CM Indirect trust degree (ITD)
      • send a feedback request to CH
cm to cm direct trust calculation
CM-to-CM Direct Trust Calculation

a window of time

# of successful interactions of node x with y

# of unsuccessful interactions of node x with y

strict punishment for unsuccessful interactions

ch to cm feedback trust calculation
CH-to-CM Feedback Trust Calculation

# of positive feedback

# of negative

feedback

Assumption: CH is trustworthy within its cluster!

trust decision making at ch level
Trust Decision-Making at CH Level
  • Decision making: calculate for direct trust and feedback trust simultaneously
  • CH-to-CH direct trust
    • # of successful and unsuccessful interactions
  • BS-to-CH feedback trust
    • BS periodically asks all CHs for their trust ratings on their neighbors.
    • CH send a feedback request to BS
ch to ch direct trust calculation
CH-to-CH Direct Trust Calculation

a window of time

# of successful interactions of CH i with CH j

# of unsuccessful interactions of CH i with CH j

strict punishment for unsuccessful interactions

bs to ch feedback trust calculation
BS-to-CH Feedback Trust Calculation

feedback of CH k toward CH j

quality of feedback

# of positive

feedback

# of negative

feedback

self adaptive global trust aggregation at chs
Self-Adaptive Global Trust Aggregation at CHs

BS-to-CH feedback trust

CH-to-CH Direct Trust

# of positive feedbacks

# of successful interactions

increasing α,

Φ(x) quickly approaches 1

outline3
Outline
  • Motivation
  • Clustered WSN Model
  • Lightweight Scheme for Trust Decision-Making
  • Theoretical analysis and evaluation
  • Simulation-based analysis and evaluation
  • Conclusion
communication overhead analysis and comparison
Communication Overhead Analysis and Comparison

Assume: Network consists of m clusters (including the BS)

average size of clusters is n (including the CH of the cluster)

communication overhead of one node

# of CM

send n requests and receive

n responses

outline4
Outline
  • Motivation
  • Clustered WSN Model
  • Lightweight Scheme for Trust Decision-Making
  • Theoretical analysis and evaluation
  • Simulation-based analysis and evaluation
  • Conclusion
outline5
Outline
  • Motivation
  • Clustered WSN Model
  • Lightweight Scheme for Trust Decision-Making
  • Theoretical analysis and evaluation
  • Simulation-based analysis and evaluation
  • Conclusion
conclusion
Conclusion
  • Lightweight trust evaluating scheme
    • cooperations between CMs
    • cooperations between CHs
  • Dependability-enhanced trust evaluating approach
    • cooperations between CHs
  • Self-adaptive weighting method
    • CH’s trust aggregation