Bayesian methods for modelling and management of trust in wireless sensor networks
- Publication Type:
- Thesis
- Issue Date:
- 2008
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Security and trust are two interdependent concepts and are often used
interchangeably when defining a secure wireless sensor network (WSN) system.
However, security is different from trust in that, it assumes no node is trustworthy
and requires ongoing authentication using sophisticated protocols leading to high
communication and computation overheads. This makes the traditional
cryptographic security tools hard, if not impossible, to be used in wireless sensor
networks that are severely resource constrained. Trust on the other hand is the
exact opposite of security in that any node can interact with any other and requires
no authentication and unwrapping of hidden keys to carry on with their business
and hence carries zero overhead. However, this leads to the miss-use and abuse of
networks causing loss and damage to the owners of the networks. This thesis
focuses on developing novel methods for modelling and managing trust that
enable WSN to be secure while significantly reducing computing and
communication overheads.
Although researchers have been studying the problem of trust modelling and
management in wireless sensor networks for over a decade, their focus was on the
trust associated with routing messages between nodes (communication trust).
However, wireless sensor networks are mainly deployed to sense the world and
report data, both continuous and discrete. However, there are no methods in the
literature that focus on the trust associated with misreporting data (data trust). In
this thesis, we model the trust associated with the integrity of the data, and
propose methods to combine the data trust with the communication trust to infer
the total trust.
Bayesian probabilistic approach is used to model and manage trust. A new risk
assessment algorithm for establishing trust in wireless sensor networks based on
the quality of services characteristics of sensor nodes, using the traditional
weighting approach is introduced. Then a Beta distribution is used to model
communication trust (due to its binary nature) and determine the weights in terms
of the Beta distribution parameters to probabilistically combine direct and indirect
trust.
The thesis extends the Bayesian probabilistic approach to model data trust for
cases when the sensed data is continuous. It introduces the Gaussian trust and
reputation system to that accounts for uncertain characteristics of sensor data.
Finally we introduce a Bayesian fusion algorithm to combine the data trust and
communication trust to infer the overall trust between nodes. Simulation results
are presented to demonstrate how the models accurately classify different nodes as
being trustworthy or not based on their reliability in sensor reporting and routing
functions.
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