485 research outputs found
On Distributed Linear Estimation With Observation Model Uncertainties
We consider distributed estimation of a Gaussian source in a heterogenous
bandwidth constrained sensor network, where the source is corrupted by
independent multiplicative and additive observation noises, with incomplete
statistical knowledge of the multiplicative noise. For multi-bit quantizers, we
derive the closed-form mean-square-error (MSE) expression for the linear
minimum MSE (LMMSE) estimator at the FC. For both error-free and erroneous
communication channels, we propose several rate allocation methods named as
longest root to leaf path, greedy and integer relaxation to (i) minimize the
MSE given a network bandwidth constraint, and (ii) minimize the required
network bandwidth given a target MSE. We also derive the Bayesian Cramer-Rao
lower bound (CRLB) and compare the MSE performance of our proposed methods
against the CRLB. Our results corroborate that, for low power multiplicative
observation noises and adequate network bandwidth, the gaps between the MSE of
our proposed methods and the CRLB are negligible, while the performance of
other methods like individual rate allocation and uniform is not satisfactory
Localization for Anchoritic Sensor Networks
We introduce a class of anchoritic sensor networks, where communications
between sensor nodes is undesirable or infeasible, e.g., due to harsh
environment, energy constraints, or security considerations
Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
Recent advances in electronics are enabling substantial processing to be
performed at each node (robots, sensors) of a networked system. Local
processing enables data compression and may mitigate measurement noise, but it
is still slower compared to a central computer (it entails a larger
computational delay). However, while nodes can process the data in parallel,
the centralized computational is sequential in nature. On the other hand, if a
node sends raw data to a central computer for processing, it incurs
communication delay. This leads to a fundamental communication-computation
trade-off, where each node has to decide on the optimal amount of preprocessing
in order to maximize the network performance. We consider a network in charge
of estimating the state of a dynamical system and provide three contributions.
First, we provide a rigorous problem formulation for optimal real-time
estimation in processing networks in the presence of delays. Second, we show
that, in the case of a homogeneous network (where all sensors have the same
computation) that monitors a continuous-time scalar linear system, the optimal
amount of local preprocessing maximizing the network estimation performance can
be computed analytically. Third, we consider the realistic case of a
heterogeneous network monitoring a discrete-time multi-variate linear system
and provide algorithms to decide on suitable preprocessing at each node, and to
select a sensor subset when computational constraints make using all sensors
suboptimal. Numerical simulations show that selecting the sensors is crucial.
Moreover, we show that if the nodes apply the preprocessing policy suggested by
our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio
Design of State-based Schedulers for a Network of Control Loops
For a closed-loop system, which has a contention-based multiple access
network on its sensor link, the Medium Access Controller (MAC) may discard some
packets when the traffic on the link is high. We use a local state-based
scheduler to select a few critical data packets to send to the MAC. In this
paper, we analyze the impact of such a scheduler on the closed-loop system in
the presence of traffic, and show that there is a dual effect with state-based
scheduling. In general, this makes the optimal scheduler and controller hard to
find. However, by removing past controls from the scheduling criterion, we find
that certainty equivalence holds. This condition is related to the classical
result of Bar-Shalom and Tse, and it leads to the design of a scheduler with a
certainty equivalent controller. This design, however, does not result in an
equivalent system to the original problem, in the sense of Witsenhausen.
Computing the estimate is difficult, but can be simplified by introducing a
symmetry constraint on the scheduler. Based on these findings, we propose a
dual predictor architecture for the closed-loop system, which ensures
separation between scheduler, observer and controller. We present an example of
this architecture, which illustrates a network-aware event-triggering
mechanism.Comment: 17 pages, technical repor
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