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Sparsitypromoting sensor selection for nonlinear measurement models
 IEEE Trans. Signal Process. (Submitted
, 2013
"... Abstract—The problem of choosing the best subset of sensors that guarantees a certain estimation performance is referred to as sensor selection. In this paper, we focus on observations that are related to a general nonlinear model. The proposed framework is valid as long as the observations are ind ..."
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Abstract—The problem of choosing the best subset of sensors that guarantees a certain estimation performance is referred to as sensor selection. In this paper, we focus on observations that are related to a general nonlinear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cramér–Rao bound (CRB) as a performance measure. We formulate the sensor selection problem as the design of a sparse vector, which in its original form is a nonconvex(quasi) norm optimization problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. The proposed solvers result in sparse sensing techniques. We also propose a projected subgradient algorithm that is attractive for largescale problems. The developed theory is applied to sensor placement for localization. Index Terms—Convex optimization, Cramér–Rao bound, nonlinear models, projected subgradient algorithm, sensor networks,
Continuous Sensor Placement
"... Abstract—Existing solutions to the sensor placement problem are based on sensor selection, in which the best subset of available sampling locations is chosen such that a desired estimation accuracy is achieved. However, the achievable estimation accuracy of sensor placement via sensor selection is ..."
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Abstract—Existing solutions to the sensor placement problem are based on sensor selection, in which the best subset of available sampling locations is chosen such that a desired estimation accuracy is achieved. However, the achievable estimation accuracy of sensor placement via sensor selection is limited to the initial set of sampling locations, which are typically obtained by gridding the continuous sampling domain. To circumvent this issue, we propose a framework of continuous sensor placement. A continuous variable is augmented to the gridbased model, which allows for offthegrid sensor placement. The proposed offline design problem can be solved using readily available convex optimization solvers. Index Terms—Convex optimization, joint sparsity, sensor placement, sensor selection, sparse sensing, sparsity. I. PROBLEM STATEMENT
Sensor Selection for Estimation, Filtering, and Detection
"... Abstract—Sensor selection is a crucial aspect in sensor network design. Due to the limitations on the hardware costs, availability of storage or physical space, and to minimize the processing and communication burden, the limited number of available sensors has to be smartly deployed. The node deplo ..."
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Abstract—Sensor selection is a crucial aspect in sensor network design. Due to the limitations on the hardware costs, availability of storage or physical space, and to minimize the processing and communication burden, the limited number of available sensors has to be smartly deployed. The node deployment should be such that a certain performance is ensured. Optimizing the sensors ’ spatial constellation or their temporal sampling patterns can be casted as a sensor selection problem. Sensor selection is essentially a combinatorial problem involving a performance evaluation over all possible choices, and it is intractable even for problems of modest scale. Nevertheless, using convex relaxation techniques, the sensor selection problem can be solved efficiently. In this paper, we present a brief overview and recent advances on the sensor selection problem from a statistical signal processing perspective. In particular, we focus on some of the important statistical inference problems like estimation, tracking, and detection. Index Terms—Sensor placement, sensor selection, sparsity, convex optimization, sensor networks, statistical inference. I.
CORRELATIONAWARE SPARSITYENFORCING SENSOR PLACEMENT FOR SPATIOTEMPORAL FIELD ESTIMATION
"... In this work, we propose a generalized framework for designing optimal sensor constellations for spatiotemporally correlated field estimation using wireless sensor networks. The accuracy of the field intensity estimate in every point of a given service area strongly depends upon the number and t ..."
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In this work, we propose a generalized framework for designing optimal sensor constellations for spatiotemporally correlated field estimation using wireless sensor networks. The accuracy of the field intensity estimate in every point of a given service area strongly depends upon the number and the constellation of the sensors along with the spatiotemporal statistics of the field. We formulate and solve a sparsityenforcing optimization problem to select the best sensor locations that achieve some desired estimation performance. The sparsityenforcing iterative selection algorithm is aware of the nonseparable spacetime covariance structure of the field. Index Terms — Wireless sensor network, field estimation, Bayesian framework, convex optimization, sparsity. 1.
NearOptimal Thermal Monitoring Framework for ManyCore SystemsonChip
"... Abstract—Chip designers place onchip thermal sensors to measure local temperatures, thus preventing thermal runaway situations in manycore processing architectures. However, the quality of the thermal reconstruction is directly dependent on the number of placed sensors, which should be minimized, ..."
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Abstract—Chip designers place onchip thermal sensors to measure local temperatures, thus preventing thermal runaway situations in manycore processing architectures. However, the quality of the thermal reconstruction is directly dependent on the number of placed sensors, which should be minimized, while guaranteeing full detection of all the worst case temperature gradient. In this paper, we present an entire framework for the thermal management of complex manycore architectures, such that we can precisely recover the thermal distribution from a minimal number of sensors. The proposed sensor placement algorithm is guaranteed to reduce the impact of noisy measurements on the reconstructed thermal distribution. We achieve significant improvements compared to the state of the art, in terms of both computational complexity and reconstruction precision. For example, if we consider a 64 cores systemsonchip with 64 noisy sensors (s2 4), we achieve an average reconstruction error of 1:5C, that is less than half of what previous stateoftheart methods achieve. We also study the practical limits of the proposed method and show that we do not need realistic workloads to learn the model and efficiently place the sensors. In fact, we show that the reconstruction error is not significantly increased if we randomly generate the powertraces of the components or if we have just a part of the correct workload. Index Terms—Sensor placement, thermal management, thermal monitoring Ç 1
ROBUST MICROPHONE PLACEMENT FOR SOURCE LOCALIZATION FROM NOISY DISTANCE MEASUREMENTS
"... We propose a novel algorithm to design an optimum array geometry for source localization inside an enclosure. We assume a squarelaw decay propagation model for the sound acquisition so that the additive noise on the measured sourcemicrophone distances is proportional to the distances regardless of ..."
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We propose a novel algorithm to design an optimum array geometry for source localization inside an enclosure. We assume a squarelaw decay propagation model for the sound acquisition so that the additive noise on the measured sourcemicrophone distances is proportional to the distances regardless of the noise distribution. We formulate the source localization as an instance of the “Generalized Trust Region Subproblem ” (GTRS) whose solution gives the location of the source. We show that by suitable selection of the microphone locations, one can tremendously decrease the noisesensitivity of the resulting solution. In particular, by minimizing the noisesensitivity of the source location in terms of sensor positions, we find the optimal noiserobust array geometry for the enclosure. Simulation results are provided to show the efficiency of the proposed algorithm. Index Terms – Robust microphone placement, Source localization, Generalized Trust Region Subproblem (GTRS). 1.
1DASS: Distributed Adaptive Sparse Sensing
"... Abstract—Wireless sensor networks are often designed to perform two tasks: sensing a physical field and transmitting the data to endusers. A crucial aspect of the design of a WSN is the minimization of the overall energy consumption. Previous researchers aim at optimizing the energy spent for the c ..."
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Abstract—Wireless sensor networks are often designed to perform two tasks: sensing a physical field and transmitting the data to endusers. A crucial aspect of the design of a WSN is the minimization of the overall energy consumption. Previous researchers aim at optimizing the energy spent for the communication, while mostly ignoring the energy cost due to sensing. Recently, it has been shown that considering the sensing energy cost can be beneficial for further improving the overall energy efficiency. More precisely, sparse sensing techniques were proposed to reduce the amount of collected samples and recover the missing data by using data statistics. While the majority of these techniques use fixed or random sampling patterns, we propose to adaptively learn the signal model from the measurements and use the model to schedule when and where to sample the physical field. The proposed method requires minimal onboard computation, no internode communications and still achieves appealing reconstruction performance. With experiments on realworld datasets, we demonstrate significant improvements over both traditional sensing schemes and the stateoftheart sparse sensing schemes, particularly when the measured data is characterized by a strong intrasensor (temporal) or intersensors (spatial) correlation. Index Terms—Wireless sensor networks, sparse sensing, adaptive sampling scheduling, compressive sensing, energy efficiency I.