MGA: A Solution Strategy to the Sensor Selection Problem in WSNs

2013 ◽  
Author(s):  
Luhutyit P Damuut ◽  
Dongbing Gu
Keyword(s):  
Sensor Selection ◽  
Selection Problem ◽  
Solution Strategy

scholarly journals New gradient methods for sensor selection problems

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983964
Author(s):  
De Zhang ◽  
Mingqiang Li ◽  
Feng Zhang ◽  
Maojun Fan
Keyword(s):  
Optimization Problem ◽  
Gradient Methods ◽  
Sensor Selection ◽  
Selection Problem ◽  
Projected Gradient Method ◽  
Step Size ◽  
Convex Optimization Problem ◽  
Selection Problems ◽  
Combinational Optimization ◽  
Relaxed Problem

In this article, we consider the sensor selection problem of choosing [Formula: see text] sensors from a set of [Formula: see text] possible sensor measurements. The sensor selection problem is a combinational optimization problem. Evaluating the performance for each possible combination is impractical unless [Formula: see text] and [Formula: see text] are small. We relax the original selection problem to be a convex optimization problem and describe a projected gradient method with Barzilai–Borwein step size to solve the proposed relaxed problem. Numerical results demonstrate that the proposed algorithm converges faster than some classical algorithms. The solution obtained by the proposed algorithm is closer to the truth.

scholarly journals Multimodal Sensor Selection for Multiple Spatial Field Reconstruction

2021 ◽  
Author(s):  
Linh Nguyen ◽  
Karthick Thiyagarajan ◽  
Nalika Ulapane ◽  
sarath kodagoda
Keyword(s):  
Random Fields ◽  
Real Life ◽  
Covariance Matrices ◽  
Sensor Selection ◽  
Sensor Nodes ◽  
Approximate Algorithm ◽  
Selection Problem ◽  
Cross Covariance ◽  
Efficient Prediction ◽  
Selection For

The paper addresses the multimodal sensor selection problem where selected collocated sensor nodes are employed to effectively monitor and efficiently predict multiple spatial random fields. It is first proposed to exploit multivariate Gaussian processes (MGP) to model multiple spatial phenomena jointly. By the use of the Matern cross-covariance function, cross covariance matrices in the MGP model are sufficiently positive semi-definite, concomitantly providing efficient prediction of all multivariate processes at unmeasured locations. The multimodal sensor selection problem is then formulated and solved by an approximate algorithm with an aim to select the most informative sensor nodes so that prediction uncertainties at all the fields are minimized. The proposed approach was validated in the real-life experiments with promising results.

SENSOR (GROUP FEATURE) SELECTION WITH CONTROLLED REDUNDANCY IN A CONNECTIONIST FRAMEWORK

2014 ◽  
Vol 24 (06) ◽  
pp. 1450021 ◽  
Author(s):  
RUDRASIS CHAKRABORTY ◽  
CHIN-TENG LIN ◽  
NIKHIL R. PAL
Keyword(s):  
Decision Making ◽  
Feature Selection ◽  
Processing Time ◽  
Sensor Selection ◽  
Selection Problem ◽  
Data Sets ◽  
Feature Selection Problem ◽  
Selection Scheme ◽  
Learning Scheme ◽  
The Cost

For many applications, to reduce the processing time and the cost of decision making, we need to reduce the number of sensors, where each sensor produces a set of features. This sensor selection problem is a generalized feature selection problem. Here, we first present a sensor (group-feature) selection scheme based on Multi-Layered Perceptron Networks. This scheme sometimes selects redundant groups of features. So, we propose a selection scheme which can control the level of redundancy between the selected groups. The idea is general and can be used with any learning scheme. We have demonstrated the effectiveness of our scheme on several data sets. In this context, we define different measures of sensor dependency (dependency between groups of features). We have also presented an alternative learning scheme which is more effective than our old scheme. The proposed scheme is also adapted to radial basis function (RBS) network. The advantages of our scheme are threefold. It looks at all the groups together and hence can exploit nonlinear interaction between groups, if any. Our scheme can simultaneously select useful groups as well as learn the underlying system. The level of redundancy among groups can also be controlled.

scholarly journals Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem

2021 ◽  
Vol 28 ◽  
pp. 284-288 ◽  
Author(s):  
Taku Nonomura ◽  
Shunsuke Ono ◽  
Kumi Nakai ◽  
Yuji Saito
Keyword(s):  
Sensor Selection ◽  
Selection Problem ◽  
Data Driven

scholarly journals Time Difference of Arrival Passive Localization Sensor Selection Method Based on Tabu Search

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6547
Author(s):  
Qian Li ◽  
Baixiao Chen ◽  
Minglei Yang
Keyword(s):  
Tabu Search ◽  
Covariance Matrix ◽  
Selection Method ◽  
Search Method ◽  
Sensor Selection ◽  
Time Difference ◽  
Selection Problem ◽  
Positioning Error ◽  
Time Difference Of Arrival ◽  
Position Errors

This paper proposes a time difference of arrival (TDOA) passive positioning sensor selection method based on tabu search to balance the relationship between the positioning accuracy of the sensor network and system consumption. First, the passive time difference positioning model, taking into account the sensor position errors, is considered. Then, an approximate closed-form constrained total least-squares (CTLS) solution and a covariance matrix of the positioning error are provided. By introducing a Boolean selection vector, the sensor selection problem is transformed into an optimization problem that minimizes the trace of the positioning error covariance matrix. Thereafter, the tabu search method is employed to solve the transformed sensor selection problem. The simulation results show that the performance of the proposed sensor optimization method considerably approximates that of the exhaustive search method. Moreover, it can significantly reduce the running time and improve the timeliness of the algorithm.

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