A key predistribution scheme for deployable sensor networks using the node deployment probability density function

2007 ◽  
Vol 90 (10) ◽  
pp. 73-83
Author(s):  
Takashi Ito ◽  
Hidenori Ohta ◽  
Nori Matsuda ◽  
Takeshi Yoneda
Keyword(s):  
Sensor Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Key Predistribution ◽  
Node Deployment ◽  
Key Predistribution Scheme

A novel mobile agent-based distributed evidential expectation maximization algorithm for uncertain sensor networks

2020 ◽  
pp. 014233122096958
Author(s):  
Mohiyeddin Mozaffari ◽  
Behrouz Safarinejadian ◽  
Mokhtar Shasadeghi
Keyword(s):  
Sensor Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Convergence Analysis ◽  
Density Function ◽  
Mobile Agent ◽  
Expectation Maximization ◽  
Convergence Criterion ◽  
Sufficient Statistics ◽  
Agent Based

In this paper, a novel mobile agent-based distributed evidential expectation maximization (MADEEM) algorithm is presented for sensor networks. The proposed algorithm is used for probability density function estimation and data clustering in the presence of uncertainties in sensor measurements. It is assumed that the sensor measurements are statistically modeled by a common Gaussian mixture model. The proposed algorithm maximizes a new generalized likelihood criterion in an iterative and distributed manner. For this purpose, mobile agents compute some local sufficient statistics by using local measurements of each sensor node. After the local computations, the global sufficient statistics are updated. At the end of iterations, the parameters of the probability density function are updated by using the global sufficient statistics. The mentioned process will be continued until the convergence criterion is satisfied. Convergence analysis of the proposed algorithm is also presented in this paper. After the convergence analysis, the simulation results show the promising performance of the proposed algorithm. Finally, the last part of the paper is devoted to the concluding remarks.

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