Hierarchical Trust
Management for Wireless
Sensor Networks and Its
Application to Trust-Based
Routing
Fenye Bao, Ing-Ray Chen, Moonjeong Chang
Presented by: Scott Hackman
03 November 2011
Scott Hackman – CS5214 – Modeling and Analysis
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Hierarchical Trust Management
Introduction
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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.
Scott Hackman – CS5214 – Modeling and Analysis
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Hierarchical Trust Management
Wireless Sensor Network
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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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Hierarchical Trust Management
Sensor Node
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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 multihop routing.
SNs have very limited resources:
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Energy
Memory
Computational Power
May be susceptible to malicious attacks when their energy is
low.
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Hierarchical Trust Management
Cluster Head
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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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Hierarchical Trust Management
Abnormal Node Behavior
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Malicious Node
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Selfish Node
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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.
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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Hierarchical Trust Management
System Model
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First, how do we determine which nodes are SNs and which
nodes are CHs?
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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 peerto-peer (P2P) data from other CHs.
CHs will pass their data to a “CH Commander” for evaluation.
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Hierarchical Trust Management
How Does Trust Factor In?
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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:
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Energy
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Unselfishness
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Measures cooperativeness
Honesty
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Measures competence
Less susceptible to malicious attacks
Whether or not the node is compromised based on intrusion detection
capabilities in the system based on software-based code attestation
Intimacy
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Relative degree of interaction experiences between two nodes
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Hierarchical Trust Management
Evaluation Process
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A weighted evaluation is performed and all four categories are
factored into one, overall trust score:
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Tij(t) denotes the trust that node i has toward node j at time t.
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Hierarchical Trust Management
Peer-to-Peer Trust Evaluation
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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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Hierarchical Trust Management
Peer-to-Peer Trust Evaluation
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This relationship is represented as follows:
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γ and α represent weights associated with trust decay. X
represents one of the trust components.
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Hierarchical Trust Management
Peer-to-Peer Trust Factors
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- 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].
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- 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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Hierarchical Trust Management
Peer-to-Peer Trust Factors
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- 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.
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- 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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Hierarchical Trust Management
Other Parameters Defined
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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”.
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γ
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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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Hierarchical Trust Management
CH-to-SN Trust Evaluation
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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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Hierarchical Trust Management
Performance Model
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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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Hierarchical Trust Management
Petri Net Model
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Hierarchical Trust Management
Petri Net Model - Energy
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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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Hierarchical Trust Management
Petri Net Model - Selfishness
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A node may become selfish to save energy.
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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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Hierarchical Trust Management
Petri Net Model - Honesty
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A node becomes compromised when T_COMPRO fires and
places a token in CN.
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Hierarchical Trust Management
Subjective Trust Evaluation
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If j is a selfish node (a/c), compromised node (b/c) or normal
node (c/c)
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Hierarchical Trust Management
Objective Trust Evaluation
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Hierarchical Trust Management
Trust Evaluation
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Hierarchical Trust Management
Trust Evaluation
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Hierarchical Trust Management
Geographic Routing
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Hierarchical Trust Management
Geographic Routing
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Hierarchical Trust Management
Geographic Routing
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Hierarchical Trust Management
Geographic Routing
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Hierarchical Trust Management
Conclusion
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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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