A distributed averaging-based evidential Expectation-Maximization algorithm for density estimation in unreliable sensor networks

Measurement ◽  
2020 ◽  
Vol 165 ◽  
pp. 108162
Author(s):  
Behrouz Safarinejadian ◽  
Mohiyeddin Mozaffari
Keyword(s):  
Sensor Networks ◽  
Density Estimation ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Distributed Averaging

scholarly journals Distributed parameter estimation in wireless sensor networks in the presence of fading channels and unknown noise variance

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880330
Author(s):  
Shoujun Liu ◽  
Kezhong Liu ◽  
Jie Ma ◽  
Wei Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fading Channels ◽  
Expectation Maximization ◽  
Multiple Access ◽  
Expectation Maximization Algorithm ◽  
Wireless Sensor ◽  
Fusion Center ◽  
Noise Variance ◽  
Access Scheme

Parameter estimation is one of the most important research areas in wireless sensor networks. In this study, we consider the problem of estimating a deterministic parameter over fading channels with unknown noise variance. Owing to the bandwidth constraints in wireless sensor networks, sensor observations are quantized and subsequently transmitted to the fusion center. Two types of communication channels are considered, namely, parallel-access channels and multiple-access channels. Based on the knowledge of channel statistics, the power of the received signals at the fusion center can be described by the mode of the exponential mixture distribution. The expectation maximization algorithm is used to determine maximum likelihood solutions for this mixture model. A new estimator based on the expectation maximization algorithm is subsequently proposed. Simulation results show that this estimator exhibits superior performance compared to the method of moments estimator in both parallel- and multiple-access schemes. In addition, we determine that the parallel-access scheme outperforms the multiple-access scheme when the noise variance is small and it loses its superiority when the noise variance is large.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Su-Zhe Wang ◽  
Yong Li ◽  
Wei Cheng
Keyword(s):  
Feature Extraction ◽  
Sensor Networks ◽  
Expectation Maximization ◽  
Recognition Rate ◽  
Splitting Method ◽  
Expectation Maximization Algorithm ◽  
Classification Algorithm ◽  
Support Vector ◽  
Malicious Nodes ◽  
Distribution Features

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.

Ordered subset expectation maximization algorithm for image reconstruction applied to zero-flow or low-flow mimicking brain phantom

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S678-S678
Author(s):  
Yasuhiro Akazawa ◽  
Yasuhiro Katsura ◽  
Ryohei Matsuura ◽  
Piao Rishu ◽  
Ansar M D Ashik ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Low Flow ◽  
Brain Phantom ◽  
Zero Flow
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