Energy Aware Iterative Source Localization for Wireless Sensor Networks

Maşazade, Engin; Niu, Ruixin; Varshney, Pramod K.; Keskinoz, Mehmet

doi:10.1109/tsp.2010.2051433

Cited by 103 publications

(65 citation statements)

References 27 publications

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“…This was accomplished by taking ratios of the energy reading of a pair of sensors in the noise-free case. In [12], the authors proposed an energy aware source localization method to reduce the energy consumption in localization.…”

Section: Single-target/source Localization In Wireless Sensormentioning

confidence: 99%

A Survey of Localization in Wireless Sensor Network

Cheng

Zhang

et al. 2012

International Journal of Distributed Sensor Networks

228

154

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Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network.

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Section: Single-target/source Localization In Wireless Sensormentioning

confidence: 99%

A Survey of Localization in Wireless Sensor Network

Cheng

Zhang

et al. 2012

International Journal of Distributed Sensor Networks

228

154

View full text Add to dashboard Cite

show abstract

“…In (10), the FIM is decomposed into two parts, where, J D t represents the FIM corresponding to the sensor measurements, and J P t represents the FIM corresponding to the a priori information. The FIM corresponding to the sensor measurements, J D t , can be further written as the summation of each sensor's individual FIM [18], [21] as,…”

Section: B Fisher Information With Quantized Measurementsmentioning

confidence: 99%

“…Most past work focusses on sensor management problems without addressing selfish concerns of participants [15], [16], [17], [18], [19]. Information based measures have been used for sensor management in [15], [16] which maximize the mutual information between the sensor measurements and target state.…”

Section: Introductionmentioning

confidence: 99%

“…Information based measures have been used for sensor management in [15], [16] which maximize the mutual information between the sensor measurements and target state. Sensor selection strategies that minimize the bound on the estimation error, which is the inverse of Fisher Information [17], [18] are computationally more efficient than the information based sensor selection methods [18]. In [19], the Nondominating Sorting Genetic Algorithm-II method is employed for the multi-objective optimization based sensor selection problem.…”

Section: Introductionmentioning

confidence: 99%

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Target Tracking via Crowdsourcing: A Mechanism Design Approach

Cao

Brahma

Varshney

2015

IEEE Trans. Signal Process.

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Abstract-In this paper, we propose a crowdsourcing based framework for myopic target tracking by designing an incentivecompatible mechanism based optimal auction in a wireless sensor network (WSN) containing sensors that are selfish and profitmotivated. For typical WSNs which have limited bandwidth, the fusion center (FC) has to distribute the total number of bits that can be transmitted from the sensors to the FC among the sensors. To accomplish the task, the FC conducts an auction by soliciting bids from the selfish sensors, which reflect how much they value their energy cost. Furthermore, the rationality and truthfulness of the sensors are guaranteed in our model. The final problem is formulated as a multiple-choice knapsack problem (MCKP), which is solved by the dynamic programming method in pseudopolynomial time. Simulation results show the effectiveness of our proposed approach in terms of both the tracking performance and lifetime of the sensor network.

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“…In particular, it has been shown [126] that the complexity of mutual information-based approaches is exponential in N , whereas the complexity of PCRLB or conditional PCRLB-based approaches is linear in N . A recursive approach to calculate the sequential PCRLB for a general multi-dimensional discrete-time nonlinear filtering problem has been derived [127].…”

Section: (I) Reduced Complexity Metrics For Trackingmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

109

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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Energy Aware Iterative Source Localization for Wireless Sensor Networks

Cited by 103 publications

References 27 publications

A Survey of Localization in Wireless Sensor Network

A Survey of Localization in Wireless Sensor Network

Target Tracking via Crowdsourcing: A Mechanism Design Approach

Distributed inference in wireless sensor networks

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