Metoda Sederhana Distribusi Node Jaringan Sensor Nirkabel untuk Mengatasi Kehilangan Data Pada Saat Pengiriman

2018 ◽  
Vol 9 (2) ◽  
pp. 67-72
Author(s):  
Rifki Muhendra
Keyword(s):  
Bit Error Rate ◽  
Mesh Networks ◽  
Wireless Sensor ◽  
Distribution Model ◽  
Network Node ◽  
Data Loss ◽  
Simple Method ◽  
Node Distribution ◽  
Address Data ◽  
Index Terms

A simple method of wireless sensor network node distribution was developed to address data loss during transmission. A node consists of a microcontroller, radio frequency, and battery. The adjacent nodes are planted with programs that can build mesh networks. This network consists of multiple child networks and parent networks connected through a frequency router node. Communication between nodes in the network can take two ways. The average ping time measured for the node distribution model is small from 1 second and the bit error rate (BER) is 0%. Index Terms—Wireless sensor networks, mesh, frequency router node

Distribution-free models for latent mixed population responses in a longitudinal setting with missing data

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3273-3285 ◽  
Author(s):  
Hui Zhang ◽  
Li Tang ◽  
Yuanyuan Kong ◽  
Tian Chen ◽  
Xueyan Liu ◽  
...  
Keyword(s):  
Simulated Data ◽  
Distribution Model ◽  
Distribution Free ◽  
Response Models ◽  
Psychosocial Studies ◽  
Address Data ◽  
Latent Subgroups ◽  
Model Treatment ◽  
Functional Response Models ◽  
Inverse Probability Weighted

Many biomedical and psychosocial studies involve population mixtures, which consist of multiple latent subpopulations. Because group membership cannot be observed, standard methods do not apply when differential treatment effects need to be studied across subgroups. We consider a two-group mixture in which membership of latent subgroups is determined by structural zeroes of a zero-inflated count variable and propose a new approach to model treatment differences between latent subgroups in a longitudinal setting. It has also been incorporated with the inverse probability weighted method to address data missingness. As the approach builds on the distribution-free functional response models, it requires no parametric distribution model and thereby provides a robust inference. We illustrate the approach with both real and simulated data.

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