Sparsity-Aware Sensor Selection: Centralized and Distributed Algorithms

Jamali-Rad, Hadi; Simonetto, Andrea; Leus, Geert

doi:10.1109/lsp.2013.2297419

Cited by 51 publications

(53 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig. 4 shows the simulation results where we compare with FrameSense [6], convex relaxation ( 1 followed by rounding, using the log determinant approach to minimize VCE) [8], SparSenSe [13] and MPME [4]. All measurement matrices used here are α−tight with α = 100.…”

Section: B Comparisons With Previous Sensor Selection Algorithmsmentioning

confidence: 99%

“…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%

“…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. Several recent works have also considered nonlinear sensor networks [13], tracking applications [21], [22], distributed sensing scenarios [23], [24], correlated noise models [25], estimation of continuous variables [26]. Additional scenarios where further limitations are added to the network sensing problem include: energy budget constraints [27] and ways to maximize the lifetime per unit cost in wireless sensor networks [28], 2 regularization terms that discourage the selection of the same sensors over a period of time [12], [29] and scheduling [30], [31] over the network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensor Scheduling With Time, Energy, and Communication Constraints

Rusu

Thompson

Robertson

2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

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.

show abstract

Section: B Comparisons With Previous Sensor Selection Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensor Scheduling With Time, Energy, and Communication Constraints

Rusu

Thompson

Robertson

2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Other more recent approaches leverage on the inherent sparsity of the problem. For instance, the authors in [16] investigate-both from centralized and distributed standpoints-strategies aimed to minimize the number of selected sensors subject to a given Mean Square Error (MSE) target. Non-linear measurement models (such as those in source localization and tracking problems) have been considered in [17], also in a sparsitypromoting framework.…”

Section: Introductionmentioning

confidence: 99%

Sensor Selection and Power Allocation Strategies for Energy Harvesting Wireless Sensor Networks

Calvo-Fullana

Matamoros

Antón-Haro

2016

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

Abstract-In this paper, we investigate the problem of jointly selecting a predefined number of energy-harvesting (EH) sensors and computing the optimal power allocation. The ultimate goal is to minimize the reconstruction distortion at the fusion center. This optimization problem is, unfortunately, non-convex. To circumvent that, we propose two suboptimal strategies: (i) a joint sensor selection and power allocation (JSS-EH) scheme that, we prove, is capable of iteratively finding a stationary solution of the original problem from a sequence of surrogate convex problems; and (ii) a separate sensor selection and power allocation (SS-EH) scheme, on which basis we can identify a sensible sensor selection and analytically find a power allocation policy by solving a convex problem. We also discuss the interplay between the two strategies. Performance in terms of reconstruction distortion, impact of initialization, actual subsets of selected sensors and computed power allocation policies, etc., is assessed by means of computer simulations. To that aim, an EH-agnostic sensor selection strategy, a lower bound on distortion, and an online version of the SS-EH and JSS-EH schemes are derived and used for benchmarking.

show abstract

“…Sensor resource management [2][3][4][5][6][7][8]: Approaches dealing with the dynamic activation of observation nodes to optimally schedule the sensing activities are referred to…”

Section: Introductionmentioning

confidence: 99%