| dc.description.abstract | In many applications, data from different sensors
are aggregated in order to obtain more reliable information
about the process that the sensors are monitoring. However, the
quality of the aggregated information is intricately dependent
on the reliability of the individual sensors. In fact, unreliable
sensors will tend to report erroneous values of the ground truth,
and thus degrade the quality of the fused information. Finding
strategies to identify unreliable sensors can assist in having a
counter-effect on their respective detrimental influences on the
fusion process, and this has has been a focal concern in the
literature. The purpose of this paper is to propose a solution
to an extremely pertinent problem, namely, that of identifying
which sensors are unreliable
without any knowledge of the ground
truth
. This fascinating paradox can be formulated in simple
terms as trying to identify
stochastic
liars without any additional
information about the truth. Though apparently impossible, we
will show that it is feasible to solve the problem, a claim that
is
counter-intuitive in and of itself
. To the best of our knowledge,
this is the first reported solution to the aforementioned paradox.
Legacy work and the reported literature have merely addressed
assessing the reliability of a sensor by comparing its reading
to the ground truth either in an online or an offline manner.
The informed reader will observe that the so-called Weighted
Majority Algorithm is a representative example of a large class
of such legacy algorithms. The essence of our approach involves
studying the agreement of each sensor with the rest of the sensors,
and not comparing the reading of the individual sensors with
the ground truth – as advocated in the literature. Under some
mild conditions on the reliability of the sensors, we can prove
that we can, indeed, filter out the unreliable ones. Our approach
leverages the power of the theory of Learning Automata (LA) so
as to gradually learn the identity of the reliable and unreliable
sensors. To achieve this, we resort to a team of
LA
, where a
distinct automaton is associated with each sensor. The solution
provided here has been subjected to rigorous experimental tests,
and the results presented are, in our opinion, both novel and
conclusive. | language |
