integrated social and quality of service trust management of mobile groups in ad hoc networks
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Integrated Social and Quality of Service Trust Management of Mobile Groups in Ad Hoc Networks . Ing -Ray Chen, Jia Guo , Fenye Bao , Jin- Hee Cho Communications Surveys & Tutorials , IEEE Β 13.4 (2011): 562-583. Speaker: Liang Zhao. Outline. 1.Background 2.Trust Management Protocol - PowerPoint PPT PresentationTRANSCRIPT
Integrated Social and Quality of Service Trust Management of Mobile Groups in Ad Hoc Networks Ing-Ray Chen, Jia Guo, Fenye Bao, Jin-Hee ChoCommunications Surveys & Tutorials, IEEE 13.4 (2011): 562-583
Speaker: Liang Zhao
Outlineβ’ 1.Backgroundβ’ 2.Trust Management Protocolβ’ 3.Model-based Evaluation Technique.β’ 4.Evaluation Resultsβ’ 5.Conclusion and Future Works
BackgroundTrust Management:
A mobile ad hoc network (MANET), sometimes called a mobile mesh network, is a self-configuring network of mobile devices connected by wireless links.
Mobile Ad Hoc Network (MANETs):
1. Abstract system that processes symbolic representations of social trust2. Aid automated decision-making process.
Problems in MANET trust Managementβ’ 1. Traditional QoS Trust Metrics did not
consider Social Trust as metric.β’ 2. Existing trust Metrics lack good aggregation
parameter settings.β’ 3. Effectiveness of Trust Management Protocol
is hard to be evaluated due to difficulty of getting labels based on ground truth.
Contributionsβ’ 1. Consider social metrics: i.e. intimacy (social ties)
and honesty (healthiness).β’ 2. Identify best trust aggregation parameter settings
for each trust metric.
β’ 3. For validating proposed trust management protocol, a novel model-based evaluation technique is leveraged to generate ground truth.
SQTrust
Model-based Evaluation
SQTrust: A New Trust Management Protocol
SQTrust: Preliminary
1. Social Ties (Intimacy)2. Honesty (Healthiness)
measure the social trust level of a node as these social properties are considered critical for trustworthy mission execution
Most important metrics to measure the QoS trust level of a node
3. Competence (Energy)4. Protocol compliance (Cooperativeness)
Trust Metrics (trust components) taken into account:
SQTrust: A New Trust Management Protocolβ’ What is it for?
β’ How to infer it?By collecting all the observations from other nodes
π ππ π’π (π‘ )=
βπππ πβ π
β
π π , π(π‘)
πβ1
Subjective trust
For inferring the trust belief of each node in the network
Trust Observations of node j by node i
SQTrust: A New Trust Management Protocolβ’ How to get ?
Consider the following trust metrics (namely trust components):1. Intimacy2. Healthiness3. Energy4. Cooperativeness
π π , πβ (π‘)=β
ππ€πΓπ π , π
π (π‘)
Social Metrics
Qos Metrics
Weight of each trust component Each trust component
How to determine them?
SQTrust: A New Trust Management Protocolβ’ How to infer each trust component ?
Consider both direct trust and indirect trust.
π π , ππ (π‘ )=π½1π π , π
ππππππ‘ , π (π‘)+π½2π π , πππππππππ‘ ,π (π‘)
How to determine them?
Directly collected by Node i toward node j.
Indirect evidences given to node i by a subset of 1-hop neighbors selected.
Trade-off:
SQTrust: Direct Trust β’ How to infer the Direct Trust of a node?
Well, it depends.
- If Node i is 1-hop neighbor of node j
π π , πππππππ‘ ,π (π‘ )=π π , π
1βhππ , π (π‘ )
-Otherwise,
π π , πππππππ‘ ,π (π‘ )=πβππ β π‘Γπ π , π
ππππππ‘ ,π (π‘ββ π‘ )
exponential trust decay over time.
SQTrust: indirect trust Inferring Indirect Trust is a little more complex.
1. Selection of Subset of 1-hop neighbors.
Threshold-based filtering: only consider trustworthy recommenders
Relevance-based trust: only consider trustworthy nodes under current trust component
<threshold
<threshold
<threshold
compromised
Low trust in healthiness
Trust decay over space
Trust decay over time
SQTrust: Indirect Trust 2.Calculation of indirect trust
-If there is at least one qualified neighbor:
π π , πππππππππ‘ , π (π‘ )=
βπβπ
(π π ,ππ (π‘ )Γππ , π
π (π‘ ) )ππ
-Otherwise,
π π , πππππππππ‘ , π (π‘ )=πβππ β π‘Γπ π , π
ππππππππ‘ ,π (π‘ββ π‘ )
Node iβs trust in node m Node mβs trust in node j
Model-based Evaluation
Model-based Evaluation
β’ Schema:β’ 1. Leverage SPN to build a semi-Markov chain
to generate the nodesβ status.β’ 2. Reward Assignment for each status.β’ 3. Objective trust calculation.
Purpose: To get the objective trust as an exact global knowledge to evaluate subjective trust :
v.s.
a semi-Markov chain for node statusβ’ Node Status is of 5 status representations:
β’ 1. Location.(int)β’ 2. Member.(boolean)β’ 3. Energy.(boolean)β’ 4. Healthiness .(boolean)β’ 5. Cooperativeness.(boolean)
trust components
To tell the position proximity of nodes
Location
Is fired when node moves to another region.
What is it for?1. Enable the underlying semi-Markov model to give the probability that each node is in a certain region.
2. Thus to tell whether a node is 1-hop neighbor of another.
# of tokens depends on the region a node moving into
Transition rate:
Initial speed
Wireless radio range
IntimacyConsider both direct trust and indirect trust.For direct trust:
1. utilize location probability of a node to infer if nodes i and j are 1-hop neighbors.2. If they are, utilize the equation:
Based on the probability node i and node j are in the same region.
EnergyTo get the probability of current energy level of a node.
initialize different value to differentnodes to emphasize the heterogeneity.
-lower when node becomes uncooperative to save energy-higher when being compromised
Initial # of tokens: depends on the initial value
Transition rate:
Healthiness (CN)
A node is compromised when T_COMPRO fires
Transition rate: _π πππ
A token goes to CN when a node is compromised
Then, either of below can happen:1. Good-mouth a bad node with a high trust recommendation
2. Bad-mouth a good node with a low trust recommendation
Cooperativeness (UNCOOP)
πππ‘π (ππππΆπππ )=π π (πΈ ππππππ) π π (ππππππππ’ππ‘π¦ ) π π (πππππππ )
π ππ
A token goes to UNCOOP when a node is uncooperative.
depends on energy, mission difficulty and neighborhood uncooperativeness degree:
Lower energy, less cooperative
Harder the mission, more cooperative
Less cooperative 1-hop neighbors, more cooperative
Group communication interval
π (π₯ )=πΌ π₯β π
Reward Assignment for each status
Objective Trust CalculationObjective trust :
π ππππ (π‘ )=β
ππ€π βπ π
πππ , π (π‘ )
(1) For healthiness, energy or cooperativeness:
π ππππ , π (π‘ )=
β«π‘ βπβ π‘
π‘
βπ β π
(π π βπ π (π‘β² ))ππ‘ β²
πβ π‘
(2) For intimacy:
by aggregating all the trust components calculated as:
Probability the system is at status s at time t
Evaluation Results
Evaluation ResultsParameter Settings
Total 150 nodes, initially all are not compromised in MANETs. Initially all are trustworthyBased on ns3 simulation
Evaluation ResultsOverall trust values from subjective trust v.s. objective trust
The value around 85% is the best trade-off
Conclusions and Future Works1. Purpose of this paper: A protocol which minimizes the trust bias and maximize application performance.2. Applicability: Based on the optimal protocol settings we get, we apply it for dynamic trust management with considering the environment changes.Future Works:Consider more sophisticated attacker behaviors, i.e. opportunistic, random and insidious attacks.
Thanks