DocumentCode :
1452631
Title :
Ordering for Estimation and Optimization in Energy Efficient Sensor Networks
Author :
Blum, Rick S.
Author_Institution :
Electr. & Comput. Eng. Dept., Lehigh Univ., Bethlehem, PA, USA
Volume :
59
Issue :
6
fYear :
2011
fDate :
6/1/2011 12:00:00 AM
Firstpage :
2847
Lastpage :
2856
Abstract :
A discretized version of a continuous optimization problem is considered for the case where data is obtained from a set of dispersed sensor nodes and the overall metric is a sum of individual metrics computed at each sensor. An example of such a problem is maximum-likelihood estimation based on statistically independent sensor observations. By ordering transmissions from the sensor nodes, a method for achieving a saving in the average number of sensor transmissions is described. While the average number of sensor transmissions is reduced, the approach always yields the same solution as the optimum approach where all sensor transmissions occur. The approach is described first for a general optimization problem. A maximum-likelihood target location and velocity estimation example for a multiple node noncoherent multiple-input multiple-output (MIMO) radar system is later described. In particular, for cases with N good quality sensors with ideal signals and sufficiently large signal-to-interference-plus-noise ratio (SINR), the average percentage of transmissions saved approaches 100% as the number of discrete grid points in the optimization problem Q becomes significantly large. In these same cases, the average percentage of transmissions saved approaches (Q-1)/ Q × 100 % as the number of sensors N in the network becomes significantly large. Similar savings are illustrated for general optimization (or estimation) problems with some sufficiently well-designed sensors. Savings can be even larger in some cases for systems with some poor quality sensors.
Keywords :
MIMO radar; maximum likelihood estimation; wireless sensor networks; continuous optimization problem; discrete grid point; dispersed sensor node; energy efficient sensor network; maximum likelihood estimation; maximum likelihood target location; multiple node noncoherent multiple input multiple output radar system; sensor observation; sensor transmission; signal to interference plus noise ratio; velocity estimation; MIMO radar; Maximum likelihood estimation; Measurement; Optimization; Signal to noise ratio; Transmitters; Energy efficient; estimation; maximum-likelihood estimation; optimization; ordering; sensor networks;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2011.2116015
Filename :
5714758
Link To Document :
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