Packet Damage-Resistant Analysis Using a Data Mining Mechanism in Wireless Sensor Networks

Author(s):  
Jinsuk Baek ◽  
Paul S. Fisher
2007 ◽  
Vol 06 (02) ◽  
pp. 235-251 ◽  
Author(s):  
GUANGYAN HUANG ◽  
XIAOWEI LI ◽  
JING HE ◽  
XIN LI

Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively.


2013 ◽  
Vol 9 (7) ◽  
pp. 406316 ◽  
Author(s):  
Azhar Mahmood ◽  
Ke Shi ◽  
Shaheen Khatoon ◽  
Mi Xiao

Author(s):  
Shoban Babu Sriramoju

Data mining acquires its name from the resemblances between searching for useful company information in a large database for instance, locating connected products in gigabytes of store scanner data-- as well as mining a mountain for a capillary of beneficial ore. Both processes call for either sifting through an immense amount of product, or smartly penetrating it to discover specifically where the value resides. This paper provides the major problems of Data Mining as well as additionally discuss regarding security assimilation challenges in WSN.


2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096134
Author(s):  
Hongjian Ma

With the development of Internet of things technology, the combination of Internet of things technology and sports competition parameter collection technology, so as to carry out rapid and accurate retrieval and positioning of technology and tactics, has innovation in the current research field. In the high-level table tennis competition, the use of technology and tactics is closely related to the gain and loss of points. At present, the traditional table tennis video mining algorithm has some problems such as low efficiency and poor performance of optimization classification. Based on this, this article introduces the big data platform of the wireless sensor networks to construct the table tennis match database, realizing the real-time updating of table tennis match parameters and the call of historical data at any time. Then establishing a data mining model to realize the data and dynamic analysis of table tennis matches. Finally, based on this strategic analysis system, the data collected from two table tennis competitions are simulated, and the tactical recommendation of theoretical analysis is obtained, which provides a theoretical basis for the digitization of table tennis sports.


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