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73
Sensor Selection via Convex Optimization
 IEEE Transactions on Signal Processing
, 2009
"... We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the(m k possible choices of sensor measurements is not ..."
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Cited by 96 (2 self)
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We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the(m k possible choices of sensor measurements is not practical unless m and k are small. In this paper we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m3 operations; for m = 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2 GHz personal computer. 1
Multimodal fusion for multimedia analysis: a survey
, 2010
"... This survey aims at providing multimedia researchers with a stateoftheart overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several c ..."
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Cited by 58 (1 self)
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This survey aims at providing multimedia researchers with a stateoftheart overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.
A survey of sensor selection schemes in wireless sensor networks
 In SPIE Defense and Security Symposium Conference on Unattended Ground, Sea, and Air Sensor Technologies and Applications IX
, 2007
"... One of the main goals of sensor networks is to provide accurate information about a sensing field for an extended period of time. This requires collecting measurements from as many sensors as possible to have a better view of the sensor surroundings. However, due to energy limitations and to prolong ..."
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Cited by 29 (4 self)
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One of the main goals of sensor networks is to provide accurate information about a sensing field for an extended period of time. This requires collecting measurements from as many sensors as possible to have a better view of the sensor surroundings. However, due to energy limitations and to prolong the network lifetime, the number of active sensors should be kept to a minimum. To resolve this conflict of interest, sensor selection schemes are used. In this paper, we survey different schemes that are used to select sensors. Based on the purpose of selection, we classify the schemes into (1) coverage schemes, (2) target tracking and localization schemes, (3) single mission assignment schemes and (4) multiple missions assignment schemes. We also look at solutions to relevant problems from other areas and consider their applicability to sensor networks. Finally, we take a look at the open research problems in this field. 1.
Optimal Motion Strategies for Rangeonly Constrained Multisensor Target Tracking
, 2006
"... Abstract—In this paper, we study the problem of optimal trajectory generation for a team of mobile sensors tracking a moving target using distanceonly measurements. This problem is shown to be NPHard, in general, when constraints are imposed on the speed of the sensors. We propose two algorithms, ..."
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Cited by 26 (8 self)
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Abstract—In this paper, we study the problem of optimal trajectory generation for a team of mobile sensors tracking a moving target using distanceonly measurements. This problem is shown to be NPHard, in general, when constraints are imposed on the speed of the sensors. We propose two algorithms, modified GaussSeidelrelaxation and LPrelaxation, for determining the set of feasible locations that each sensor should move to in order to collect the most informative measurements; i.e., distance measurements that minimize the uncertainty about the position of the target. Furthermore, we prove that the motion strategy that minimizes the trace of the position error covariance matrix is equivalent to the one that maximizes the minimum eigenvalue of its inverse. The two proposed algorithms are applicable regardless of the process model that is employed for describing the motion of the target, while the computational complexity of both methods is linear in the number of sensors. Extensive simulation results are presented demonstrating that the performance attained with the proposed methods is comparable to that obtained with gridbased exhaustive search, whose computational cost is exponential in the number of sensors, and significantly better than that of a random, towards the target, motion strategy.
Passive mobile robot localization within a fixed beacon field
 in Proceedings of the International Workshop on the Algorithmic Foundations of Robotics
, 2006
"... This thesis describes a geometric algorithm for the localization of mobile nodes in networks of sensors and robots using bounded regions, in particular we explore the rangeonly and angleonly measurement cases. The algorithm is a minimalistic approach to localization and tracking when dead reckonin ..."
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Cited by 23 (9 self)
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This thesis describes a geometric algorithm for the localization of mobile nodes in networks of sensors and robots using bounded regions, in particular we explore the rangeonly and angleonly measurement cases. The algorithm is a minimalistic approach to localization and tracking when dead reckoning is too inaccurate to be useful. The only knowledge required about the mobile node is its maximum speed. Geometric regions are formed and grown to account for the motion of the mobile node. New measurements introduce new constraints which are propagated back in time to refine previous localization regions. The mobile robots are passive listeners while the sensor nodes actively broadcast making the algorithm scalable to many mobile nodes while maintaining the privacy of individual nodes. We prove that the localization regions found are optimal–that is, they are the smallest regions which must contain the mobile node at that time. We prove that each new measurement requires quadratic time in the number of measurements to update the system, however, we demonstrate experimentally that this can be reduced to constant time. Numerous
A lookup table based approach for solving the camera selection problem in large camera networks
 in Proceedings of the International Workshop on Distributed Smart Cameras
, 2006
"... Modern navigation, mapping and surveillance systems require numerous cameras mounted at random locations over a geographically large area. In order to efficiently extract any locationspecific intelligence from such a large network of cameras, we propose to create distributed lookup tables that ran ..."
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Cited by 22 (0 self)
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Modern navigation, mapping and surveillance systems require numerous cameras mounted at random locations over a geographically large area. In order to efficiently extract any locationspecific intelligence from such a large network of cameras, we propose to create distributed lookup tables that rank the cameras according to how well they can image a specific location. The lookup table covers all possible locations within the corresponding camera’s viewing frustum 1 by optimally dividing the volume of the viewing frustum. The process of updating lookup tables is purely incremental, thus it can easily be used in a dynamic network. The foremost advantages of our approach are a significant reduction of runtime computation for selecting a set of favorable cameras using a simple lookup operation and a significant reduction of network traffic that would otherwise be required to carry out the process of camera selection. The proposed algorithm can be applied to various applications involving distributed cooperative processing in a large camera network. 1
Utilitybased Sensor Selection
, 2006
"... Sensor networks consist of many small sensing devices that monitor an environment and communicate using wireless links. The lifetime of these networks is severely curtailed by the limited battery power of the sensors. One line of research in sensor network lifetime management has examined sensor sel ..."
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Cited by 19 (1 self)
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Sensor networks consist of many small sensing devices that monitor an environment and communicate using wireless links. The lifetime of these networks is severely curtailed by the limited battery power of the sensors. One line of research in sensor network lifetime management has examined sensor selection techniques, in which applications judiciously choose which sensors ’ data should be retrieved and are worth the expended energy. In the past, many adhoc approaches for sensor selection have been proposed. In this paper, we argue that sensor selection should be based upon a tradeoff between applicationperceived benefit and energy consumption of the selected sensor set. We propose a framework wherein the application can specify the utility of measuring data (nearly) concurrently at each set of sensors. The goal is then to select a sequence of sets to measure whose total utility is maximized, while not exceeding the available energy.
Camera network node selection for target localization in the presence of occlusions
 In SenSys Workshop on Distributed Cameras
, 2006
"... A camera network node subset selection methodology for target localization in the presence of static and moving occluders is described. It is assumed that the locations of the static occluders are known, but that only prior statistics for the positions of the object and the moving occluders are avai ..."
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Cited by 14 (3 self)
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A camera network node subset selection methodology for target localization in the presence of static and moving occluders is described. It is assumed that the locations of the static occluders are known, but that only prior statistics for the positions of the object and the moving occluders are available. This occluder information is captured in the camera measurement via an indicator random variable that takes the value 1 if the camera can see the object and 0, otherwise. The minimum MSE of the best linear estimate of object position based on camera measurements is then used as a metric for selection. It is shown through simulations and experimentally that a greedy selection heuristic performs close to optimal and outperforms other heuristics.
On efficient sensor scheduling for linear dynamical systems,”
 Automatica,
, 2012
"... Abstract Consider a set of sensors estimating the state of a process in which only one of these sensors can operate at each timestep due to constraints on the overall system. The problem addressed here is to choose which sensor should operate at each timestep to minimize a weighted function of th ..."
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Cited by 13 (2 self)
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Abstract Consider a set of sensors estimating the state of a process in which only one of these sensors can operate at each timestep due to constraints on the overall system. The problem addressed here is to choose which sensor should operate at each timestep to minimize a weighted function of the error covariances of the state estimations. This work investigates the development of tractable algorithms to solve for the optimal and suboptimal sensor schedules. A condition on the nonoptimality of an initialization of the schedule is developed. Using this condition, both an optimal and a suboptimal algorithm are devised to prune the search tree of all possible sensor schedules. The suboptimal algorithm trades off the quality of the solution and the complexity of the problem through a tuning parameter. The performance of the suboptimal algorithm is also investigated and an analytical error bound is provided. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms, and the application of the algorithms in active robotic mapping is explored.
Randomized Sensor Selection in Sequential Hypothesis Testing
, 2009
"... We consider the problem of sensor selection for timeoptimal detection of a hypothesis. We consider a group of sensors transmitting their observations to a fusion center. The fusion center considers the output of only one randomly chosen sensor at the time, and performs a sequential hypothesis test. ..."
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Cited by 11 (7 self)
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We consider the problem of sensor selection for timeoptimal detection of a hypothesis. We consider a group of sensors transmitting their observations to a fusion center. The fusion center considers the output of only one randomly chosen sensor at the time, and performs a sequential hypothesis test. We consider the class of sequential tests which are easy to implement, asymptotically optimal, and computationally amenable. For three distinct performance metrics, we show that, for a generic set of sensors and binary hypothesis, the fusion center needs to consider at most two sensors. We also show that for the case of multiple hypothesis, the optimal policy needs at most as many sensors to be observed as the number of underlying hypotheses.