255 research outputs found
Sensor selection via convex optimization,”
Abstract-We consider the problem of choosing a set of sensor measurements, from a set of possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the possible choices of sensor measurements is not practical unless and are small. In this paper, we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of 3 operations; for = 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2-GHz personal computer
Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World
We address the two fundamental problems of spatial field reconstruction and
sensor selection in heterogeneous sensor networks: (i) how to efficiently
perform spatial field reconstruction based on measurements obtained
simultaneously from networks with both high and low quality sensors; and (ii)
how to perform query based sensor set selection with predictive MSE performance
guarantee. For the first problem, we developed a low complexity algorithm based
on the spatial best linear unbiased estimator (S-BLUE). Next, building on the
S-BLUE, we address the second problem, and develop an efficient algorithm for
query based sensor set selection with performance guarantee. Our algorithm is
based on the Cross Entropy method which solves the combinatorial optimization
problem in an efficient manner.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the
Developing Worl
On Multi-Step Sensor Scheduling via Convex Optimization
Effective sensor scheduling requires the consideration of long-term effects
and thus optimization over long time horizons. Determining the optimal sensor
schedule, however, is equivalent to solving a binary integer program, which is
computationally demanding for long time horizons and many sensors. For linear
Gaussian systems, two efficient multi-step sensor scheduling approaches are
proposed in this paper. The first approach determines approximate but close to
optimal sensor schedules via convex optimization. The second approach combines
convex optimization with a \BB search for efficiently determining the optimal
sensor schedule.Comment: 6 pages, appeared in the proceedings of the 2nd International
Workshop on Cognitive Information Processing (CIP), Elba, Italy, June 201
Dynamic Sensor Placement Based on Graph Sampling Theory
In this paper, we consider a dynamic sensor placement problem where sensors
can move within a network over time. Sensor placement problem aims to select M
sensor positions from N candidates where M < N. Most existing methods assume
that sensors are static, i.e., they do not move, however, many mobile sensors
like drones, robots, and vehicles can change their positions over time.
Moreover, underlying measurement conditions could also be changed that are
difficult to cover the statically placed sensors. We tackle the problem by
allowing the sensors to change their positions in their neighbors on the
network. Based on a perspective of dictionary learning, we sequentially learn
the dictionary from a pool of observed signals on the network based on graph
sampling theory. Using the learned dictionary, we dynamically determine the
sensor positions such that the non-observed signals on the network can be best
recovered from the observations. Furthermore, sensor positions in each time
slot can be optimized in a decentralized manner to reduce the calculation cost.
In experiments, we validate the effectiveness of the proposed method via the
mean squared error (MSE) of the reconstructed signals. The proposed dynamic
sensor placement outperforms the existing static ones both in synthetic and
real data
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