for Clustered Wireless Sensor Networks 1 Presented by Ting Hua Author s Xiaoyong Li Feng Zhou and Junping Du Outline 2 Motivation C lustered WSN M odel Lightweight Scheme for Trust DecisionMaking ID: 170498
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Slide1
LDTS: A Lightweight and Dependable Trust Systemfor Clustered Wireless Sensor Networks
1
Presented by: Ting Hua
Author
s:
Xiaoyong Li, Feng Zhou, and Junping DuSlide2
Outline
2
Motivation
C
lustered WSN
M
odel
Lightweight Scheme for Trust Decision-Making
Theoretical analysis and evaluation
Simulation-based analysis and evaluation
ConclusionSlide3
Motivation
3
Limited work focus on
R
esource 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?Slide4
Outline
4
Motivation
C
lustered
WSN
M
odel
Lightweight Scheme for Trust
Decision-Making
Theoretical analysis and evaluation
Simulation-based analysis
and
evaluation
ConclusionSlide5
Clustered WSN Model
5
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.Slide6
Outline
6
Motivation
C
lustered WSN
M
odel
Lightweight Scheme for Trust Decision-Making
Theoretical analysis and evaluation
Simulation-based analysis
and
evaluation
ConclusionSlide7
Trust Decision-Making at CM Level
7
Decision
making:
p
ast
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
i
via
node
y
Successful:
node y forwarded such message to CH
Unsuccessful:
No retransmission
of the packet within a
threshold time
O
verheard packet is illegally fabricated
No: CH-to-CM Indirect
trust
degree (ITD)
send a feedback request to CHSlide8
CM-to-CM Direct Trust Calculation
8
a window of time
# of successful interactions of node x with y
# of unsuccessful interactions of node x with y
strict punishment for unsuccessful interactionsSlide9
CH-to-CM Feedback Trust Calculation
9
# of positive feedback
# of negative
feedback
Assumption:
CH is trustworthy within its cluster!Slide10
Trust Decision-Making at CH Level
10
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 BSSlide11
CH-to-CH Direct Trust Calculation
11
# of unsuccessful interactions of CH i with CH j
strict punishment for unsuccessful interactions
a window of time
# of successful interactions of CH i with CH jSlide12
BS-to-CH Feedback Trust Calculation
12
feedback of CH k toward CH j
# of positive
feedback
# of negative
feedback
quality of feedbackSlide13
Self-Adaptive Global Trust Aggregation at CHs
13
# of successful interactions
BS-to-CH feedback trust
CH-to-CH Direct Trust
# of positive feedbacks
increasing
α
,
Φ
(x) quickly approaches 1Slide14
Outline
14
Motivation
C
lustered WSN
M
odel
Lightweight Scheme for Trust Decision-Making
Theoretical analysis and evaluation
Simulation-based analysis
and
evaluation
ConclusionSlide15
Dependability Analysis Against Malicious AttacksSlide16
Dependability Analysis Against Malicious AttacksSlide17
Dependability Analysis Against Malicious AttacksSlide18
Dependability Analysis Against Malicious AttacksSlide19
Dependability Analysis Against Malicious AttacksSlide20
Dependability Analysis Against Malicious AttacksSlide21
Dependability Analysis Against Malicious AttacksSlide22
Dependability Analysis Against Malicious AttacksSlide23
Dependability Analysis Against Malicious AttacksSlide24
Dependability Analysis Against Malicious AttacksSlide25
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)
# of CM
send n requests and receive
n responses
communication overhead of one nodeSlide26
Storage Overhead Analysis and ComparisonSlide27
Outline
27
Motivation
C
lustered WSN
M
odel
Lightweight Scheme for Trust Decision-Making
Theoretical analysis and evaluation
Simulation-based analysis
and
evaluation
ConclusionSlide28
LDTS Simulator and EnvironmentSlide29
Overhead Evaluation and ComparisonSlide30
Overhead Evaluation and ComparisonSlide31
Dependability Evaluation and ComparisonSlide32
Dependability Evaluation and ComparisonSlide33
Outline
33
Motivation
C
lustered WSN
M
odel
Lightweight Scheme for Trust Decision-Making
Theoretical analysis and evaluation
Simulation-based analysis and evaluation
ConclusionSlide34
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