A maximum-likelihood parametric approach to source localizations

Chen, J.C.; Hudson, Robert S.; Yao, Kung

doi:10.1109/icassp.2001.940284

Cited by 40 publications

(33 citation statements)

References 2 publications

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“…Based on direction of arrival (DOA) for narrow-band sources or time-delay of arrival (TDOA), many techniques for acoustic pulse localization in sensor networks or sensor arrays are available in the literature [4] [5]. But due to the requirement of accurate matching of timings, inexpensive sensors cannot be employed as a lot of processing is required for location estimation.…”

Section: Introductionmentioning

confidence: 99%

Target localization in Wireless Sensor Networks with quantized data in the presence of Byzantine attacks

Agrawal

Vempaty

Chen

et al. 2011

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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Wireless Sensor Networks (WSNs) are vulnerable to Byzantine attacks in which malicious sensors send wrong data to the fusion center leading to an increase in the probability of incorrect inference. This paper considers Byzantine attacks for the location estimation task in wireless sensor networks where each sensor uses a binary quantization scheme to send binary data to the fusion center. Posterior Cramer-Rao lower Bound (PCRLB) metric and Fisher Information Matrix (FIM) are used to analyze the performance of the network in the presence of Byzantine attacks. We have considered two kinds of attack strategies, independent attacks (all the malicious sensors attack a WSN independently of each other) and collaborative attacks (all the malicious sensors communicate with each other and attack the WSN in a coordinated manner). We determine the fraction of Byzantine attackers in the network above which the fusion center becomes incapable of finding the location of the target. Optimal attacking strategy for given attacking resources is also proposed.

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Section: Introductionmentioning

confidence: 99%

Target localization in Wireless Sensor Networks with quantized data in the presence of Byzantine attacks

Agrawal

Vempaty

Chen

et al. 2011

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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“…Recently, maximum likelihood estimation is a common method used for event localization [6][7][8]. In [1], it proposed SNAP(Subtract on Negative Add on Positive) algorithm, which can identify the event location using binary data from the sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

MSNAP: Fault Tolerant Event Localization in Wireless Sensor Networks

Gao

Wan

2010

2010 IEEE Asia-Pacific Services Computing Conference

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This paper investigates event localization in wireless sensor networks. We improve the SNAP (Subtract on Negative Add on Positive) localization algorithm and propose the MSNAP (Modified Subtract on Negative Add on Positive) localization algorithm with higher localization accuracy and better performance of fault tolerance. First, every sensor node obverses the event signal and compares its observed reading with a threshold. If the reading is above the threshold, the node will send it to the sink station. Otherwise, it remains silent. Based on the observed readings which the nodes report, the sink station constructs the likelihood matrix by simply adding ± 1 contributions in the area around the nodes, whose maximum value points to the event location. Compared with the SNAP algorithm, when constructing the likelihood matrix, MSNAP dynamically adjusts the size of estimated region depending on the observed readings the nodes reported. Experimental results show that the algorithm effectively improves the localization accuracy and fault tolerance.

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“…& Abel, 1987) use time delays between sensors location to estimate the source position. However, though they are computationally less expensive than maximum-likelihood parametric algorithms (Chen et al, 2001a), they cannot handle efficiently multiple sources (Chen et al, 2001b). Maximum-likelihood (ML) algorithms are inspired by the fact that source location information is contained in the linear phase shift of the sensor data spectrum obtained through a discrete Fourier Transform applied to the wideband data.…”

Section: Previous Workmentioning

confidence: 99%