2 research outputs found
Optimal Sensing and Data Estimation in a Large Sensor Network
An energy efficient use of large scale sensor networks necessitates
activating a subset of possible sensors for estimation at a fusion center. The
problem is inherently combinatorial; to this end, a set of iterative,
randomized algorithms are developed for sensor subset selection by exploiting
the underlying statistics. Gibbs sampling-based methods are designed to
optimize the estimation error and the mean number of activated sensors. The
optimality of the proposed strategy is proven, along with guarantees on their
convergence speeds. Also, another new algorithm exploiting stochastic
approximation in conjunction with Gibbs sampling is derived for a constrained
version of the sensor selection problem. The methodology is extended to the
scenario where the fusion center has access to only a parametric form of the
joint statistics, but not the true underlying distribution. Therein,
expectation-maximization is effectively employed to learn the distribution.
Strategies for iid time-varying data are also outlined. Numerical results show
that the proposed methods converge very fast to the respective optimal
solutions, and therefore can be employed for optimal sensor subset selection in
practical sensor networks.Comment: 9 page
Optimal Dynamic Sensor Subset Selection for Tracking a Time-Varying Stochastic Process
Motivated by the Internet-of-things and sensor networks for cyberphysical
systems, the problem of dynamic sensor activation for the tracking of a
time-varying process is examined. The tradeoff is between energy efficiency,
which decreases with the number of active sensors, and fidelity, which
increases with the number of active sensors. The problem of minimizing the
time-averaged mean-squared error over infinite horizon is examined under the
constraint of the mean number of active sensors. The proposed methods artfully
combine three key ingredients: Gibbs sampling, stochastic approximation for
learning, and modifications to consensus algorithms to create a high
performance, energy efficient tracking mechanisms with active sensor selection.
The following progression of scenarios are considered: centralized tracking of
an i.i.d. process; distributed tracking of an i.i.d. process and finally
distributed tracking of a Markov chain. The challenge of the i.i.d. case is
that the process has a distribution parameterized by a known or unknown
parameter which must be learned. The key theoretical results prove that the
proposed algorithms converge to local optima for the two i.i.d process cases;
numerical results suggest that global optimality is in fact achieved. The
proposed distributed tracking algorithm for a Markov chain, based on
Kalman-consensus filtering and stochastic approximation, is seen to offer an
error performance comparable to that of a competetive centralized Kalman
filter.Comment: This is an intermediate version. This will be updated soo