Real-Time Estimation of Sensor Node's Position Using Particle Swarm Optimization With Log-Barrier Constraint

Nguyen, Hong-Quang; Guo, Hao; Low, Kay Soon

doi:10.1109/tim.2011.2135030

Cited by 68 publications

(30 citation statements)

References 32 publications

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“…the three alignment parameters and the deblurring filter). These optimization methods were tested, as they are usually applied to solve difficult optimization problems and have been used on solving many instrument design problems [1,6,18,26,31,48]. Based on these comparisons, the effective of the proposed Taguchi method can be further evaluated.…”

Section: Comparison With Commonly-used Optimization Methodsmentioning

confidence: 99%

Alignment parameter calibration for IMU using the Taguchi method for image deblurring

et al. 2015

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Section: Comparison With Commonly-used Optimization Methodsmentioning

confidence: 99%

Alignment parameter calibration for IMU using the Taguchi method for image deblurring

et al. 2015

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“…For each single element such as in feature vector VD , the probability of it can be modeled into the Gaussian distribution with mean and variance Σ according to [30], which is analyzed from the point of error measurement:…”

Section: Network Assumptions and Statistic Based Feature Modelmentioning

confidence: 99%

Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

Suzhe

Cheng

2016

Journal of Sensors

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Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario.

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“…For reasons of implementation ease, the total pool of possible AP locations is chosen at the locations as determined by the receiver locations grid. The PL will be modeled according to two-slope model proposed by the IEEE 802.11 TGn channel models group [43]:…”

Section: Configurationmentioning

confidence: 99%

A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization

et al. 2015

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Real-Time Estimation of Sensor Node's Position Using Particle Swarm Optimization With Log-Barrier Constraint

Cited by 68 publications

References 32 publications

Alignment parameter calibration for IMU using the Taguchi method for image deblurring

Alignment parameter calibration for IMU using the Taguchi method for image deblurring

Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization

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