scholarly journals Zigbee wireless sensor network localization evaluation schemewith weighted centroid method

2018 ◽  
Vol 192 ◽  
pp. 02070 ◽  
Author(s):  
Lonesy Thammavong ◽  
Khamphong Khongsomboon ◽  
Thanadol Tiengthong ◽  
Sathaporn Promwong
Keyword(s):  
Sensor Network ◽  
Cumulative Distribution Function ◽  
Indoor Localization ◽  
Cumulative Distribution ◽  
Transmission Model ◽  
Centroid Method ◽  
Distance Error ◽  
Network Localization ◽  
Optimal Value ◽  
Positioning Method

Using wireless communication system, appropriate and correct indoor localization with Zigbee sensor network and could provide interesting services and applications. In this study the Zigbee transmission model with positioning method by using the relative-span exponentially weighted centroid method for the indoor localization. The experimental results and analyze results are evaluated a distance error. The ZigBee transmission model in measurement consists of 121 positions with distance between positions to positions is 0.3 meter. The experimental setup at every position operated at frequency band from 2.3 GHz to 2.5 GHz. The accuracy of estimated position is considered in the term of distance error with the cumulative distribution function (CDF) of distance error is shown. The result presents optimal value for REWL is 0.2 and mean of distance error is 0.65 m.

Wireless Sensor Network Localization Algorithm Based on Hop-Count and Distributed Learning

2014 ◽  
Vol 940 ◽  
pp. 457-460
Author(s):  
Ying Zhang ◽  
Yi Wang ◽  
Ying Ze Ye
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Distributed Learning ◽  
Training Data ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Hop Count ◽  
Sensor Network Localization ◽  
Network Localization ◽  
Positioning Method

The wireless sensor network localization algorithm in this paper combines hop-count information and distributed learning. The network is classified into many classes based on sensors’ location, and then the class that each sensor falls into is specified. There are a certain number of beacon nodes with position coordinate in network, and they use their own locations as training data in performing above classification. This positioning method merely uses the partial hop-count information between target sensor and reference node in specifying the class of each node. The final simulation experiment will analyze the excellent performance of this method under different system parameters.

DISSONANCE — A MEASURE OF VARIABILITY FOR ORDINAL RANDOM VARIABLES

2001 ◽  
Vol 09 (01) ◽  
pp. 39-53 ◽  
Author(s):  
RONALD R. YAGER
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Probability Distributions ◽  
Random Variables ◽  
Cumulative Distribution ◽  
General Properties

We look at the issue of obtaining a variance like measure associated with probability distributions over ordinal sets. We call these dissonance measures. We specify some general properties desired in these dissonance measures. The centrality of the cumulative distribution function in formulating the concept of dissonance is pointed out. We introduce some specific examples of measures of dissonance.

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