Channel Aware Target Localization With Quantized Data in Wireless Sensor Networks

Özdemir, Onur; Niu, Ruixin; Varshney, Pramod K.

doi:10.1109/tsp.2008.2009893

Cited by 95 publications

(87 citation statements)

References 18 publications

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“…Suppose that the channels between sensors and fusion center may be not error free [12] or sensors suffer from attack [13,14]. For example, if the channel coefficient between sensors and fusion center is Rayleigh distributed, and hard decision is adopted at the fusion center, then the channel can be described by a probability transition matrix [12].…”

Section: B Binary Digital Communicationmentioning

confidence: 99%

Parameter Estimation via Unlabeled Sensing Using Distributed Sensors

et al. 2017

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In this letter, the problem of estimating an unknown deterministic parameter is studied, where each sensor acquires a noisy version of the signal and the data at fusion center are unlabeled. Two scenarios are studied, one is that each sensor uses analog communication to transmit its observations with different channel coefficients. Another is that each sensor uses a different threshold to quantize the noisy signal with a transition matrix describing the channel. Sufficient conditions are provided under which maximum likelihood (ML) estimators can be found in polynomial time, and numerical simulations are conducted to evaluate performances of ML estimators.

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Section: B Binary Digital Communicationmentioning

confidence: 99%

Parameter Estimation via Unlabeled Sensing Using Distributed Sensors

et al. 2017

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“…Initialize parameters: the number of clustering C=K, the number of initial iteration t=1, the biggest number of iteration is t max , and the stopping threshold Step 2. Calculate probable subdivision values according to (6).…”

Section: Multi-source Localization Algorithmmentioning

confidence: 99%

“…However, the energy of sensor nodes and the communication bandwidth are extremely limited and the transmission of original data consumes relatively much energy, so the measured original data can be quantized so that the nodes only need to transmit several bits of information after quantization, hence significantly lowering the transmission quantity of data and lowering the energy consumption of nodes. Ozdemir [6] propose a target localization method based on quantitative data, which establishes the objective function through MLE-based method, obtains the estimated localization of targets through optimizing the objective function, and integrates the channel uncertainty into it, so this method has certain fault tolerance for the channel disturbance. Masazade [7] put forward cyclic source localization algorithm against heterogeneous sensor networks, which firstly obtains the posterior probability density function through the Monte-Carlo method, then proposes two node selection methods, and finally estimates the location of signal sources according to the selected nodes and objective function.…”

Section: Introductionmentioning

confidence: 99%

Study on Multi-Source Localization of Wireless Sensor Network Based on Quantitative Information

Yun-ting

Hui

Jing

et al. 2016

IJFGCN

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“…There has been extensive research in similar topics, especially in the realm of wireless sensor network where stringent power and bandwidth constraint is always imposed. In order to save power and bandwidth, one of the approaches is to use quantized data for the state estimation [7]- [11]. This method is inherent with the discrete nature of the communication between the sensors and the fusion center (FC) in the FC-based network or between sensors in the ad hoc network.…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation With Dimension Reduction Preprocessing

Yang

Chen

et al. 2014

IEEE Trans. Signal Process.

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Abstract-System state estimation relies heavily on the measurements. With the advance of sensing technology, the ability to measure is no longer a bottleneck in many systems, and more and more researchers now focus on the rich-information setting, i.e., big data. However, although information never hurts, it does not help unconditionally. How to make the most of it depends on whether we can process the data efficiently. In some systems, the inherent constraint such as the bandwidth and cost makes it necessary to reduce the dimension of the measurement before further processing. The problem that the raw measurements are first preprocessed to reduce size and then used for estimation is addressed in this paper. It is shown that there is a lower bound on the size of the preprocessed data such that if the size is beyond the bound, there exists a closedform estimator design that the linear minimum mean-square estimation can be obtained. Moreover, we propose an algorithm that is guaranteed to converge to a stationary point to design an estimator in the conditions that the lower bound cannot be reached. Besides convergence, the proposed algorithm guarantees bounded performance loss compared with the global optimal solution under some additional conditions. Finally, simulation results in three different applications are shown to demonstrate the effectiveness of the proposed algorithm.

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Channel Aware Target Localization With Quantized Data in Wireless Sensor Networks

Cited by 95 publications

References 18 publications

Parameter Estimation via Unlabeled Sensing Using Distributed Sensors

Parameter Estimation via Unlabeled Sensing Using Distributed Sensors

Study on Multi-Source Localization of Wireless Sensor Network Based on Quantitative Information

Distributed State Estimation With Dimension Reduction Preprocessing

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