Networked and Embedded Systems Laboratory

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Sensor network technology promises a vast increase in automatic data collection capabilities
through efficient deployment of tiny sensing devices. The technology will allow users to mea-
sure phenomena of interest at unprecedented spatial and temporal densities. However, as with
almost every data-driven technology, the many benefits come with a significant challenge in data
reliability. If wireless sensor networks are really going to provide data for the scientific community,
citizen-driven activism, or organizations which test that companies are upholding environmental
laws, then an important question arises: How can a user trust the accuracy of information pro-
vided by the sensor network? Data integrity is vulnerable to both node and system failures. In
data collection systems, faults are indicators that sensor nodes are not providing useful informa-
tion. In data fusion systems the consequences are more dire; the final outcome is easily affected
by corrupted sensor measurements, and the problems are no longer visibly obvious.
In this paper, we investigate a generalized and unified approach for providing information about
the data accuracy in sensor networks. Our approach is to allow the sensor nodes to develop a
community of trust. We propose a framework where each sensor node maintains reputation metrics
which both represent past behavior of other nodes and are used as an inherent aspect in predicting
their future behavior. We employ a Bayesian formulation, specifically a beta reputation system,
for the algorithm steps of reputation representation, updates, integration and trust evolution.
This framework is available as a middleware service on motes and has been ported to two sensor
network operating systems, TinyOS and SOS. We evaluate the efficacy of this framework using
multiple contexts: (1) a lab-scale testbed of Mica2 motes, (2) Avrora simulations, and (3) real
data sets collected from sensor network deployments in James Reserve.

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