Distributed Detection and Data Fusion

Varshney, Pramod K.

doi:10.1007/978-1-4612-1904-0

Cited by 1,279 publications

(1,110 citation statements)

References 51 publications

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“…When the DS scheme is employed, every sensor node goes to sleep based on the probability p(x) as expressed in (2). Similar to (3), the expected energy consumption per second of a sensor node that is a distance x away from the cluster head is:…”

Section: B Coefficient Of Variation Of Energy Consumptionmentioning

confidence: 99%

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Balanced-energy sleep scheduling scheme for high-density cluster-based sensor networks

Deng

Han

Heinzelman

et al. 2005

Computer Communications

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114

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Abstract-In order to conserve battery power in very dense sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks. However, determining which of the sensor nodes should be put into the sleep state is non-trivial. As the goal of allowing nodes to sleep is to extend network lifetime, we propose and analyze a Balanced-energy Scheduling (BS) scheme in the context of cluster-based sensor networks. The BS scheme aims to evenly distribute the energy load of the sensing and communication tasks among all the nodes in the cluster, thereby extending the time until the cluster can no longer provide adequate sensing coverage. Two related sleep scheduling schemes, the Distance-based Scheduling (DS) scheme and the Randomized Scheduling (RS) scheme are also studied in terms of the coefficient of variation of their energy consumption. Analytical and simulation results are presented to evaluate the proposed BS scheme. It is shown that the BS scheme extends the cluster's overall network lifetime significantly while maintaining a similar sensing coverage compared with the DS and the RS schemes for sensor clusters.

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Section: B Coefficient Of Variation Of Energy Consumptionmentioning

confidence: 99%

“…by Varshney [2] and Heinzelman et al [3], such distributed management provides a convenient framework for data fusion, local decision making and local control, and energy savings. A fixed or adaptive approach may be used for cluster maintenance.…”

mentioning

confidence: 99%

Balanced-energy sleep scheduling scheme for high-density cluster-based sensor networks

Deng

Han

Heinzelman

et al. 2005

Computer Communications

Self Cite

114

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“…In order to optimize the detection performance, the decision rules at the local sensors and at the fusion centre are designed under criteria such as the Bayesian or Neyman-Pearson criterion (e.g. [7,16]). The decision rules at the fusion centre and at local sensors are intertwined with each other and need to be jointly determined to optimize the overall system performance.…”

Section: (B) Distributed Detection Basicsmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

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110

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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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“…The SPARS system uses the Bayesian method [8] for data ignal Activity Pattern (SAP) reconstruction. The sparse modeling has been widely used in the literature to solve various problems in science and [11].…”

Section: Introductionmentioning

confidence: 99%

Detection of PUE Attack by SPARS Model using WSPRT

Sairam¹,

Ramakrishnaiah²

2015

IJSEA

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Cognitive radio is a system which improves the utilization of the spectrum by sensing the white spaces in its vicinity. This sensed information will be utilized by the Secondary User (SU) to transmit the data. But some of the malicious users attacks th system by generating the signal same as that of the primary transmitter. The attack caused by generating the signal same as t primary transmitter is called as Primary User Emulation Attack (PUEA). In this paper the Signal Activity Pattern Acquisition Reconstruction System (SPARS) is used to detect the attack. But this system suffers from low True Positive Rate. To incr positive rate or sensitivity a new technique was proposed called as Weighted Sequential Probability Ratio Test (WSPRT). By improving the true positive rate or sensitivity, the detection capability of the system will be improved.

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Distributed Detection and Data Fusion

Cited by 1,279 publications

References 51 publications

Balanced-energy sleep scheduling scheme for high-density cluster-based sensor networks

Balanced-energy sleep scheduling scheme for high-density cluster-based sensor networks

Distributed inference in wireless sensor networks

Detection of PUE Attack by SPARS Model using WSPRT

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