Paper Info
Title
Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks.
Abstract
For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application.
Year
DOI
Venue
2018
10.3390/s18113851
SENSORS
Keywords
Field
DocType
wireless sensor networks,data fusion,support degree function,dynamic time warping,sensor-cloud,water quality monitoring
Aquaculture,Sensor fusion,Electronic engineering,Engineering,Wireless sensor network
Journal
Volume
Issue
ISSN
18
11.0
1424-8220
Citations 
PageRank 
References 
0
0.34
20
Authors
4
Name
Order
Citations
PageRank
Pei Shi100.68
Guanghui Li242.13
Yongming Yuan311.03
Liang Kuang401.01