TY - JOUR
T1 - Optimal Decision-Making in an Opportunistic Sensing Problem
AU - Mikesell, Derek
AU - Griffin, Christopher
N1 - Funding Information:
Manuscript received May 15, 2015; revised August 10, 2015; accepted November 11, 2015. Date of publication December 3, 2015; date of current version November 15, 2016. This work was supported by the Office of Naval Research under Naval Sea Systems Command Contract N00024-12-D-6404 DO0160. This paper was recommended by Associate Editor R. Selmic.
Publisher Copyright:
© 2013 IEEE.
PY - 2016/12
Y1 - 2016/12
N2 - In this paper, we consider the problem of sensing a finite set of (moving) objects over a finite planning horizon using a set of sensors in prefixed locations that vary with respect to time over a discretized space. Control in this situation is limited and the problem considered is one of opportunistic sensing. We formulate an integer program that maximizes the quality of sensor return given either deterministic or probabilistic (i.e., forecasted) object routes. We examine the computational complexity of the problem and show it is non-deterministic polynomial-hard. We theoretically and numerically illustrate subclasses of the problem that are computationally simpler, ultimately deriving a heuristic that is strongly polynomial. Real-world and constructed data sets are used in our analysis.
AB - In this paper, we consider the problem of sensing a finite set of (moving) objects over a finite planning horizon using a set of sensors in prefixed locations that vary with respect to time over a discretized space. Control in this situation is limited and the problem considered is one of opportunistic sensing. We formulate an integer program that maximizes the quality of sensor return given either deterministic or probabilistic (i.e., forecasted) object routes. We examine the computational complexity of the problem and show it is non-deterministic polynomial-hard. We theoretically and numerically illustrate subclasses of the problem that are computationally simpler, ultimately deriving a heuristic that is strongly polynomial. Real-world and constructed data sets are used in our analysis.
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U2 - 10.1109/TCYB.2015.2502421
DO - 10.1109/TCYB.2015.2502421
M3 - Article
AN - SCOPUS:85027706494
SN - 2168-2267
VL - 46
SP - 3285
EP - 3293
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 12
ER -