A survey of sensor selection schemes in wireless sensor networks

Rowaihy, Hosam; Eswaran, Sharanya; Johnson, Matthew; Verma, Dinesh; Bar-Noy, Amotz; Brown, Theodore J.; Porta, Thomas La

doi:10.1117/12.723514

Cited by 136 publications

(79 citation statements)

References 37 publications

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“…Our sensor assignment can be seen as a coverage scheme in the categorisation given in [3]. Similar methods for static sensor coverage can be seen in [2] and [4], where they look not only at the selection of subsets of sensors, but also consider the case of node failure.…”

Section: Related and Future Workmentioning

confidence: 99%

Sensor Assignment In Virtual Environments Using Constraint Programming

Pizzocaro

Chalmers

Preece

2008

Applications and Innovations in Intelligent Systems XV

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This paper describes a method for assignment and deployment of sensors in a virtual environment using constraint programming. We extend an existing model (multiple knapsack problem) to implement this assignment and placement, according to a given set of requirements (modelled as a utility extension). Sensor Assignment & DeploymentIn military/rescue operations the first step in gathering intelligence is the deployment of sensor devices to acquire knowledge of the domain and information to aid in pre-mission planning 1 . The assignment of sensors to areas where this information could be found, therefore, becomes of vital importance. The optimal assignment of sensors given a pre-defined commanders intent means that we can allocate (possibly limited) resources and use these to the best possible extent. In this paper we describe a method of sensor assignment and deployment using an extension to the multiple knapsack problem and how this can be deployed in a virtual environment as a web service.In assigning sensors in a virtual environment, we are trying to aid in the placement and best usage of detectors which can scan the field of battle for information to help in the formation of plans or deployment of troops. Generally in such scenarios we have the following resources, requirements and methods: a finite number of sensors with various capabilities, a given set of areas to be covered by various sensor capabilities and a set of methods directing the placement of the sensors (optimality, maximum coverage etc.).Our main aim in this paper is to describe how we have modelled the assignment of sensors, given these criteria, using a variation on the multiple knapsack model, and how we have formulated the problem in terms of this model.

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Section: Related and Future Workmentioning

confidence: 99%

Sensor Assignment In Virtual Environments Using Constraint Programming

Pizzocaro

Chalmers

Preece

2008

Applications and Innovations in Intelligent Systems XV

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“…The calculation ofŴ k −q , using the definition given in (8), requires the inverse of only a portion of the sensor signal correlation matrix, namely R −1 y −q y −q , which is currently unknown. In order to calculate the inverse without having to first remove the corresponding rows and columns that pertain to the node q's sensors and calculate R …”

Section: Utility For Node Removalmentioning

confidence: 99%

“…Assuming the DANSE algorithm has converged 8 we define the following quantity for node k with respect to its node-specific estimation problem…”

Section: Node Removal : Utility Upper Boundmentioning

confidence: 99%

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Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

et al. 2014

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A wireless sensor network is envisaged that performs signal estimation by means of the distributed adaptive nodespecific signal estimation (DANSE) algorithm. This wireless sensor network has constraints such that only a subset of the nodes are used for the estimation of a signal. While an optimal node selection strategy is NP-hard due to its combinatorial nature, we propose a greedy procedure that can add or remove nodes in an iterative fashion until the constraints are satisfied based on their utility. With the proposed definition of utility, a centralized algorithm can efficiently compute each nodes's utility at hardly any additional computational cost. Unfortunately, in a distributed scenario this approach becomes intractable. However by using the convergence and optimality properties of the DANSE algorithm, it is shown that for node removal, each node can efficiently compute a utility upper bound such that the MMSE increase after removal will never exceed this value. In the case of node addition, each node can determine a utility lower bound such that the MMSE decrease will always exceed this value once added. The greedy node selection procedure can then use these upper and lower bounds to facilitate distributed node selection.

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“…In [3], the authors propose an information-driven dynamic sensor selection for target tracking tasks, using information utility measures such as entropy, Mahalanobis and Kullback-Leibler distances. A survey of sensor selection schemes in WSN can be found in [4].…”

Section: Introductionmentioning

confidence: 99%

Sensor selection for hypothesis testing in wireless sensor networks: a Kullback-Leibler based approach

Bajović

Sinopoli

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Abstract-We consider the problem of selecting a subset of p out of n sensors for the purpose of event detection, in a wireless sensor network (WSN). Occurrence or not of the event of interest is modeled as a binary Gaussian hypothesis test. In this case sensor selection consists of finding, among all n p combinations, the one maximizing the Kullback-Leibler (KL) distance between the induced p-dimensional distributions under the two hypotheses. An exhaustive search is impractical if n and p are large, as the resulting optimization problem is combinatorial. We propose a suboptimal approach with computational complexity of order O(n 3 p). This consists of relaxing the 0/1 constraint on the entries of the selection matrices to let the optimization problem search over the set of Stiefel matrices. Although finding the Stiefel matrix is a nonconvex problem, we provide an algorithm that guarantees to produce a global optimum for p = 1, through a series of judicious problem reformulations. The case p > 1 is tackled by an incremental, greedy approach. The obtained Stiefel matrix is then used to determine the sensor selection matrix which best approximates its range space. Extensive simulations are used to assess near optimality of the proposed approach. They also show how the proposed approach performs better than exhaustive searches once an upper bound on the computation time is set.

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A survey of sensor selection schemes in wireless sensor networks

Cited by 136 publications

References 37 publications

Sensor Assignment In Virtual Environments Using Constraint Programming

Sensor Assignment In Virtual Environments Using Constraint Programming

Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

Sensor selection for hypothesis testing in wireless sensor networks: a Kullback-Leibler based approach

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