Hierarchical Trust Management for Wireless Sensor Networks and Its Application to Trust-

Based Routing

Fenye Bao, Ing-Ray Chen, Moonjeong Chang

Presented by: Scott Hackman03 November 2011

1Scott Hackman – CS5214 – Modeling and Analysis

Hierarchical Trust Management

Introduction

Cluster-based approach to creating a system for wireless routing better than shortest-distance and flood-based routing.

Utilizes Social Networking and Quality of Service (QoS) techniques to model the behaviors of nodes to determine their reliability.

Highly scalable due to being a cluster-based model.

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Wireless Sensor Network

A Wireless Sensor Network (WSN) refers to a distributed network of autonomous sensors, each operating independently for the greater good of the network.

A WSN is inherently unstable due to the independence of the Sensor Nodes (SN) and their different operating characteristics, including malicious and selfish activity.

The WSN must take input from its SNs, evaluate their input, and determine the overall picture for what is happening across its network.

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Sensor Node

A SN monitors physical or environmental conditions, such as temperature, sound, vibration, pressure, motion, or pollutants.

A SN is can transmit, or forward information through multi-hop routing.

SNs have very limited resources: Energy Memory Computational Power

May be susceptible to malicious attacks when their energy is low.

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Cluster Head

A Cluster Head (CH) is a node that has been elected to take charge of a group of SNs.

A CH receives direct input from each of its SNs. A CH is responsible for reporting to all of the other CHs in the

system. CHs use more energy than SNs.

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Abnormal Node Behavior

Malicious Node A node may be captured by the enemy at any point and start passing erroneous

information or drop packets. A node is more likely to become malicious if it has low energy or if it is

surrounded by malicious nodes.

Selfish Node A node may become selfish if its energy becomes low relative to its neighbors’. “Selfish” can be thought of as “efficient”. If a node recognizes that its battery

level is low and its neighbors have sufficient energy, it may start dropping packets so its neighbors pick up more of the burden.

The challenge becomes: How do we create a model such that malicious and selfish nodes can be identified and the WSN can adjust to these conditions to achieve a near-optimal performance?

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System Model

First, how do we determine which nodes are SNs and which nodes are CHs?

HEED (Hybrid energy-efficient, distributed) – The CH’s must have higher energy and have relative proximity. This will allow for higher energy consumption as well as optimal communications.

SNs will collect data and evaluate their peers. That information will be passed to their respective CHs.

The CHs will collect the SNs data and collect their own peer-to-peer (P2P) data from other CHs.

CHs will pass their data to a “CH Commander” for evaluation.

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How Does Trust Factor In?

Once the hierarchy is established, the evaluations completed by each node follow a trust scheme that allows for direct and indirect trust-based reporting.

Trust is established by evaluating directly, and indirectly, four different factors:

Energy Measures competence Less susceptible to malicious attacks

Unselfishness Measures cooperativeness

Honesty Whether or not the node is compromised based on intrusion detection

capabilities in the system based on software-based code attestation Intimacy

Relative degree of interaction experiences between two nodes

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Evaluation Process

A weighted evaluation is performed and all four categories are factored into one, overall trust score:

Tij(t) denotes the trust that node i has toward node j at time t.

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Peer-to-Peer Trust Evaluation

P2P Trust Evaluation is performed between SNs and between CHs.

When node i evaluates its trust toward node j, it snoops, or overhears enough data to provide direct observation. (It is assumed, notationally, that i and j are direct neighbors.)

When i evaluates a node that is beyond its communication range, we refer to the node as node k.

Node i cannot directly evaluate k, so it must rely on the information passed to it by some node j and multiply that evaluation by a weight that correlates to i’s trust toward j.

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Peer-to-Peer Trust Evaluation

This relationship is represented as follows:

γ and α represent weights associated with trust decay. X represents one of the trust components.

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Peer-to-Peer Trust Factors

- This measures the level of interaction experiences. It is computed by the number of interactions between node i and j over the maximum number of interactions between node i and any neighbor node over the time period [0, t].

- This refers to the belief of node i that node j is not compromised base on node i’s direct observations toward node j. It can be a binary quantity, 0 or 1, based on the result of Intrusion Detection System (IDS) deployed on node i about whether or not node j is compromised at time t.

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Peer-to-Peer Trust Factors

- This indicates the percentage of node j’s remaining energy that node i directly observes at time t. Node i can overhear or even monitor node j’s packet transmission activities over the time period [0, t] to estimate this value.

- This provides the degree of unselfishness of node j as evaluated by node i based on direct observations over [0, t]. Node i can apply overhearing and snooping techniques to detect selfish behaviors from node j.

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Other Parameters Defined

α - Weight that represents a more instantaneous evaluation, since the higher α, the more weight is given to time t.

β – Represents the impact of “indirect recommendations”.

γ

These parameters are used to adjust the trust decay over time. Lower factors cause a dampening effect that puts more weight on past events. This reduced high rates of change and may stabilize a system that receives sporadic, erroneous readings.

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CH-to-SN Trust Evaluation

Once all calculations are complete for a given time period t, the CH applies statistical analysis principles to all Tij(t) values received to perform CH-to-SN trust evaluation toward node j.

CH can also detect any outliers in the cluster to see if any good-mouthing or bad-mouthing is occurring.

The CH can exclude a sensor or reroute with the information it obtains.

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Performance Model

To create an objective model for comparison, a stochastic Petri net model is used.

The Petri new model essentially computes the same values, but takes away the trust aspect. All values are known by the model at all times and routing data is computed accordingly.

The underlying data of this model is used by the trust-based simulation, but each component can only see the data as defined by the initial conditions. Hence, best-case scenario, the trust-based approach can only perform as well as the objective Petri-net model.

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Petri Net Model

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Petri Net Model - Energy

Energy represents the remaining energy in a node. A token will be expended from Energy when T_ENERGY

triggers. Energy consumption rates:

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Petri Net Model - Selfishness

A node may become selfish to save energy. An unselfish node may decide whether it will be selfish or not upon every time

interval Ts according to its remaining energy and the number of unselfish neighbors.

A selfish node may become redeemed based on trust evaluation.

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Petri Net Model - Honesty

A node becomes compromised when T_COMPRO fires and places a token in CN.

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Subjective Trust Evaluation

If j is a selfish node (a/c), compromised node (b/c) or normal node (c/c)

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Objective Trust Evaluation

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Trust Evaluation

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Trust Evaluation

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Geographic Routing

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Geographic Routing

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Geographic Routing

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Geographic Routing

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Conclusion

This model presents a very practical framework that allows for highly reliable transmissions with reduced overhead.

Social networking and QoS methods allow peers to quantitatively rate their peers, drastically reducing the work needed to be done by the cluster head.

This model remains highly scalable because of its hierarchical nature.

Possible Future Work: Apply a genetic algorithm to this model and train it off of real-world data to achieve optimal weighting factors.

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