Energy-Aware Sensor Selection in Field Reconstruction

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks. We consider the case where two types of sensors are deployed: the first consists of expensive, high quality sensors; and the second, of cheap low quality sensors, which are activated only if the intensity of the spatial field exceeds a pre-defined activation threshold (eg. wind sensors). In addition, these sensors are powered by means of energy harvesting and their time varying energy status impacts on the accuracy of the measurement that may be obtained. We account for this phenomenon by encoding the energy harvesting process into the second moment properties of the additive noise, resulting in a spatial heteroscedastic process. We then address the following two important problems: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from both networks; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. We first show that the resulting predictive posterior distribution, which is key in fusing such disparate observations, involves solving intractable integrals. To overcome this problem, we solve the first problem by developing 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. We present a comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using both synthetic and real insurance storm surge database known as the Extreme Wind Storms Catalogue.

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“…In [28], [43], a sparsity-promoting penalty function to discourage repeated selection of any sensor node was proposed.…”

Section: A Related Work On Spatial Field Reconstruction In Sensor Nementioning

confidence: 99%

Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

Zhang

Nevat²,

Peters³

et al. 2018

IEEE Trans. Signal Process.

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“…Another popular greedy sensor selection method, called FrameSense [6], initially activates all the sensors and then removes one at each step based on a "worst-out" principle to optimize its submodular objective function. Convex optimization techniques have also been proven useful for experimental design [7,Chapter 7.5] with 1 [8], [9] and reweighted 1 norm minimization [10] approaches such as [11], [12], [13], [14]. Earlier work used information theoretic approaches like mutual information maximization [15], [16] and cross entropy optimization [17] or other search heuristics like genetic algorithms [18], tabu search [19] and branch-and-bound methods [20] to solve the sensor placement problems.…”

Section: Introductionmentioning

confidence: 99%

Sensor Scheduling With Time, Energy, and Communication Constraints

Rusu

Thompson

Robertson

2018

IEEE Trans. Signal Process.

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In this paper we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications constraints. The proposed algorithms, which deal directly with minimizing the mean squared error (MSE), are based on the convex relaxation approach to address the binary optimization scheduling problems that are formulated in sensor network scenarios. We propose to balance the energy and communications demands of operating a network of sensors over time while we still guarantee a minimum level of estimation accuracy. We measure this accuracy by the MSE for which we provide average case and lower bounds analyses that hold in general, irrespective of the scheduling algorithm used. We show experimentally how the proposed algorithms perform against state-of-the-art methods previously described in the literature.

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“…Several variations of sensor selection/scheduling problems have been studied in the literature [2]- [8] according to the type of quantity to be estimated (parameter or random process), measurement models (linear or nonlinear) and cost functions (estimation performance or energy consumption). In the aforementioned literature, a posterior Cramer-Rao lower bound (PCRLB) is commonly used as the op timization criterion for sensor selection in target tracking [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…In [2]- [8], the sensor selection problem was studied by assuming the measurement noise to be uncorrelated, which implies that each measurement contributes to the Fisher information in an additive manner. By exploiting this structure, sensor selection problems with uncorrelated measurements can be efficiently solved via convex relaxations.…”

Section: Introductionmentioning

confidence: 99%

Sensor selection with correlated measurements for target tracking in wireless sensor networks

Liu

Maşazade

Fardad

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We study the problem of adaptive sensor management for target tracking, where at every instant we search for the best sensors to be activated at the next time step. In our problem formulation, the measurements may be corrupted by correlated noises, and the impact of correlated measurements on sensor selection is studied. Specifically, we adopt an alteruative conditional posterior Cramer-Rao lower bound (C-PCRLB) as the optimization criterion for sensor selection, where the trace of the conditional Fisher information matrix is maximized subject to an energy constraint. We demonstrate that the proposed sensor selection problem can be transformed into the problem of maximizing a convex quadratic function over a bounded polyhedron. This optimization problem is NP-hard in nature, and thus we employ a linearization method and a bilinear programming approach to obtain locally optimal sensor schedules in a computationally efficient manner.

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Energy-Aware Sensor Selection in Field Reconstruction

Cited by 38 publications

References 13 publications

Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

Sensor Scheduling With Time, Energy, and Communication Constraints

Sensor selection with correlated measurements for target tracking in wireless sensor networks

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