Integrated Social and Quality of Service Trust Management of Mobile Groups in Ad Hoc Networks...

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 13.4 (2011): 562-583

Speaker: Liang Zhao

Outline• 1.Background• 2.Trust Management Protocol• 3.Model-based Evaluation Technique.• 4.Evaluation Results• 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 Management

• 1. Traditional QoS Trust Metrics did not consider Social Trust as metric.

• 2. Existing trust Metrics lack good aggregation parameter settings.

• 3. Effectiveness of Trust Management Protocol is hard to be evaluated due to difficulty of getting labels based on ground truth.

Contributions• 1. Consider social metrics: i.e. intimacy (social ties)

and honesty (healthiness).• 2. Identify best trust aggregation parameter settings

for each trust metric.

• 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 Protocol

• What is it for?

• How to infer it?By collecting all the observations from other nodes

𝑇 𝑗𝑠𝑢𝑏 (𝑡)=

∑𝑎𝑙𝑙 𝑖≠ 𝑗

❑

𝑇 𝑖 , 𝑗(𝑡)

𝑁−1

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 Protocol

• How to get ?

Consider the following trust metrics (namely trust components):

1. Intimacy

2. Healthiness

3. Energy

4. Cooperativeness

𝑇 𝑖 , 𝑗❑ (𝑡)=∑

𝑋

𝑤𝑋×𝑇 𝑖 , 𝑗𝑋 (𝑡)

Social Metrics

Qos Metrics

Weight of each trust component Each trust component

How to determine them?

SQTrust: A New Trust Management Protocol

• How to infer each trust component ?

Consider both direct trust and indirect trust.

𝑇 𝑖 , 𝑗𝑋 (𝑡 )=𝛽1𝑇 𝑖 , 𝑗

𝑑𝑖𝑟𝑒𝑐𝑡 , 𝑋 (𝑡)+𝛽2𝑇 𝑖 , 𝑗𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 ,𝑋 (𝑡)

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

• How to infer the Direct Trust of a node? Well, it depends.

- If Node i is 1-hop neighbor of node j

𝑇 𝑖 , 𝑗𝑑𝑖𝑟𝑒𝑐𝑡 ,𝑋 (𝑡 )=𝑇 𝑖 , 𝑗

1−h𝑜𝑝 , 𝑋 (𝑡 )

-Otherwise,

𝑇 𝑖 , 𝑗𝑑𝑖𝑟𝑒𝑐𝑡 ,𝑋 (𝑡 )=𝑒−𝜆𝑑 ∆ 𝑡×𝑇 𝑖 , 𝑗

𝑑𝑖𝑟𝑒𝑐𝑡 ,𝑋 (𝑡−∆ 𝑡 )

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:

𝑇 𝑖 , 𝑗𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 , 𝑋 (𝑡 )=

∑𝑚∈𝑉

(𝑇 𝑖 ,𝑚𝑋 (𝑡 )×𝑇𝑚 , 𝑗

𝑋 (𝑡 ) )𝑛𝑟

-Otherwise,

𝑇 𝑖 , 𝑗𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 , 𝑋 (𝑡 )=𝑒−𝜆𝑑 ∆ 𝑡×𝑇 𝑖 , 𝑗

𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 ,𝑋 (𝑡−∆ 𝑡 )

Node i’s trust in node m Node m’s trust in node j

Model-based Evaluation

Model-based Evaluation

• Schema:• 1. Leverage SPN to build a semi-Markov chain

to generate the nodes’ status.• 2. Reward Assignment for each status.• 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 status

• Node Status is of 5 status representations:

• 1. Location.(int)• 2. Member.(boolean)• 3. Energy.(boolean)• 4. Healthiness .(boolean)• 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 different

nodes 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: _𝜆 𝑐𝑜𝑚

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)

𝑟𝑎𝑡𝑒 (𝑇𝑈𝑁𝐶𝑂𝑂𝑃 )=𝑓 𝑒 (𝐸𝑟𝑒𝑚𝑎𝑖𝑛) 𝑓 𝑒 (𝑀𝑑𝑖𝑓𝑓𝑖𝑐𝑢𝑙𝑡𝑦 ) 𝑓 𝑒 (𝑆𝑑𝑒𝑔𝑟𝑒𝑒 )

𝑇 𝑔𝑐

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

𝑓 (𝑥 )=𝛼 𝑥−𝜀

Reward Assignment for each status

Objective Trust CalculationObjective trust :

𝑇 𝑗𝑜𝑏𝑗 (𝑡 )=∑

𝑋

𝑤𝑋 ∙𝑇 𝑗𝑜𝑏𝑗 , 𝑋 (𝑡 )

(1) For healthiness, energy or cooperativeness:

𝑇 𝑗𝑜𝑏𝑗 , 𝑋 (𝑡 )=

∫𝑡 −𝑑∆𝑡

𝑡

∑𝑠∈ 𝑆

(𝑟 𝑠 ∙𝑃 𝑠 (𝑡′ ))𝑑𝑡 ′

𝑑∆ 𝑡

(2) For intimacy:

by aggregating all the trust components calculated as:

Probability the system is at status s at time t

Evaluation Results

Evaluation Results

Parameter SettingsTotal 150 nodes, initially all are not compromised in MANETs.

Initially all are trustworthy

Based on ns3 simulation

Evaluation Results

Overall trust values from subjective trust v.s. objective trust

The value around 85% is the best trade-off

Conclusions and Future Works

1. 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