Optimal Sensor Selection in Binary Heterogeneous Sensor Networks

Lázaro, Marcelino; Sanchez-Fernandez, Matilde; Artés‐Rodríguez, Antonio

doi:10.1109/tsp.2009.2012902

Cited by 17 publications

(20 citation statements)

References 40 publications

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“…Optimal sensor design has been studied in the form of quantizers or local encoders; for instance [54] and [55]. The problem of sensor selection as an estimation problem is considered in [63], and under given sensor performance and costs as a detection problem in [64]. The tradeoff between performance and total bit rate with a special emphasis on quantizer bit rates is studied in [57] and [58], where the estimation of a single parameter is considered.…”

Section: Discussionmentioning

confidence: 99%

Signal Recovery With Cost-Constrained Measurements

Özçelikkale

Özaktaş

Arıkan

2010

IEEE Trans. Signal Process.

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Abstract-We are concerned with the problem of optimally measuring an accessible signal under a total cost constraint, in order to estimate a signal which is not directly accessible. An important aspect of our formulation is the inclusion of a measurement device model where each device has a cost depending on the number of amplitude levels that the device can reliably distinguish. We also assume that there is a cost budget so that it is not possible to make a high amplitude resolution measurement at every point. We investigate the optimal allocation of cost budget to the measurement devices so as to minimize estimation error. This problem differs from standard estimation problems in that we are allowed to "design" the number and noise levels of the measurement devices subject to the cost constraint. Our main results are presented in the form of tradeoff curves between the estimation error and the cost budget. Although our primary motivation and numerical examples come from wave propagation problems, our formulation is also valid for other measurement problems with similar budget limitations where the observed variables are related to the unknown variables through a linear relation. We discuss the effects of signal-to-noise ratio, distance of propagation, and the degree of coherence (correlation) of the waves on these tradeoffs and the optimum cost allocation. Our conclusions not only yield practical strategies for designing optimal measurement systems under cost constraints, but also provide insights into measurement aspects of certain inverse problems.

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Section: Discussionmentioning

confidence: 99%

Signal Recovery With Cost-Constrained Measurements

Özçelikkale

Özaktaş

Arıkan

2010

IEEE Trans. Signal Process.

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“…Detection and estimation problems considering system resource constraints have extensively been studied in the literature [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In [4], measurement cost minimization is performed under various estimation accuracy constraints.…”

Section: Introductionmentioning

confidence: 99%

“…In [11], the aim is to obtain a network configuration that satisfies the optimum detection performance under a given cost constraint. The cost constraint depends on the number of sensors employed in the network.…”

Section: Introductionmentioning

confidence: 99%

Centralized and decentralized detection with cost-constrained measurements

Laz

Gezici

2017

Signal Processing

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In this thesis, optimal detection performance of centralized and decentralized detection systems is investigated in the presence of cost constrained measurements. For the evaluation of detection performance, Bayesian, Neyman-Pearson and Jdivergence criteria are considered. The main goal for the Bayesian criterion is to minimize the probability of error (more generally, the Bayes risk) under a constraint on the total cost of the measurement devices. In the Neyman-Pearson framework, the probability of detection is to be maximized under a given cost constraint. In the distance based criterion, the J-divergence between the distributions of the decision statistics under different hypotheses is maximized subject to a total cost constraint. The probability of error expressions are obtained for both centralized and decentralized detection systems, and the optimization problems are proposed for the Bayesian criterion. The probability of detection and probability of false alarm expressions are obtained for the Neyman-Pearson strategy and the optimization problems are presented. In addition, J-divergences for both centralized and decentralized detection systems are calculated and the corresponding optimization problems are formulated. The solutions of these problems indicate how to allocate the cost budget among the measurement devices in order to achieve the optimum performance. Numerical examples are presented to discuss the results. Gezici for his guidance, suggestions and encouragement throughout this work. KeywordsHe is a modest, kind and considerate academician having a great personality.Working with him was a great privilege and honor for me.

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“…One type of binary sensor network tries to use the geometry of the network [3] and considers the network as a connected graph to aid detection. The work in [12] considers heterogeneous binary sensors with different performance and cost. The purpose is to analyze the best proportion of sensors in each class to tradeoff performance with cost.…”

Section: Introductionmentioning

confidence: 99%

Trackingwith binary sensors

Bai

Baidoo-Williams

Mudumbai

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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We present a novel approach to the tracking of piecewise linear trajectories using a network of cheap "binary sensors" i.e. devices that can only detect whether an object is in their sensing range. We show that an object traveling on almost all straight lines, even with an unknown speed, can be tracked by generically placed three binary sensors whose sensing ranges need not intersect with each other, but do intersect the trajectory. We then discuss the implication of these results to pice-wise linear trajectories with potentially unknown speeds. We also present efficient algorithms that simulations and theory verify, achieve good tracking performance, and robustness to sensing errors.

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Optimal Sensor Selection in Binary Heterogeneous Sensor Networks

Cited by 17 publications

References 40 publications

Signal Recovery With Cost-Constrained Measurements

Signal Recovery With Cost-Constrained Measurements

Centralized and decentralized detection with cost-constrained measurements

Trackingwith binary sensors

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