scholarly journals A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2555 ◽  
Author(s):  
Tengyue Zou ◽  
Yuanxia Wang ◽  
Mengyi Wang ◽  
Shouying Lin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Real Time ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Weighted Data

PROBABILISTIC REAL-TIME DATA FUSION IN WIRELESS SENSOR NETWORKS WITH ZIGBEE

2007 ◽  
Vol 40 (22) ◽  
pp. 267-272 ◽  
Author(s):  
A.R. Pinto ◽  
Benedito R. Bitencort ◽  
Underlea C. Correa ◽  
M.A.R. Dantas ◽  
Carlos Montez
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Real Time ◽  
Wireless Sensor ◽  
Time Data ◽  
Real Time Data

scholarly journals A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 784 ◽  
Author(s):  
Jiayao Wang ◽  
Olamide Tawose ◽  
Linhua Jiang ◽  
Dongfang Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Base Station ◽  
Fuzzy Entropy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Time Performance

The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.

scholarly journals A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
ZiQi Hao ◽  
ZhenJiang Zhang ◽  
Han-Chieh Chao
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Data Fusion ◽  
Event Detection ◽  
Base Station ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Local Decision ◽  
Fuzzy Logic Method

As limited energy is one of the tough challenges in wireless sensor networks (WSN), energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We usek-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

Research on Data Fusion Technology in WSN

2011 ◽  
Vol 148-149 ◽  
pp. 75-81
Author(s):  
Wen Chao Zhang ◽  
Yu Zhen Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Information Acquisition ◽  
Evaluation Method ◽  
Fault Tolerant ◽  
Environmental Parameters ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
And Performance

Wireless Sensor Networks is a novel technology of information acquisition and processing. It integrates the technologies of sensor, wireless communication, and microelectronics. It can sense and collect environmental parameters in the range of the network, and then process the parameters cooperatively in real time. We discuss the design principle and performance evaluation method of the data fusion algorithm for wireless sensor networks in detail. We introduce the concept of fusion cost, and emphasize that the data fusion algorithm in wireless sensor networks must be combined with specific application background. Finally, we emphasize the proposed algorithm which combined reduced-rank filtering with adaptive weighted. The fact that it can effectively eliminate the effect of abnormal noise on fusion performance is illustrated through examples. The results showed that this algorithm occupies obviously the advantage in the aspect of fault-tolerant ability.

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