Detection of black hole in Wireless Sensor Network based on Data Mining

2014 ◽  
Author(s):  
Gursheen Kaur ◽  
Mandeep Singh
Keyword(s):  
Data Mining ◽  
Black Hole ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor

Performance Analysis on LEACH Protocol in Wireless Sensor Network (WSN) Under Black Hole Attack

2018 ◽  
Vol 24 (3) ◽  
pp. 1791-1794 ◽  
Author(s):  
Khairul Akram Zainol Ariffin ◽  
Rozita Mohd Mokhtar ◽  
Abdul Hadi Abd Rahman
Keyword(s):  
Black Hole ◽  
Wireless Sensor Network ◽  
Performance Analysis ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Black Hole Attack ◽  
Leach Protocol

scholarly journals Mining Wireless Sensor Network Data: an adaptive approach based on artificial neuralnetworks algorithm

2011 ◽  
pp. 129-135
Author(s):  
Lambodar Jena ◽  
Ramakrushna Swain ◽  
N.K. kamila
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Dimensionality Reduction ◽  
Sensor Network ◽  
Power Supply ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Limited Power ◽  
Limited Power Supply

This paper proposes a layered modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task . Here we show that some of the algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless sensor-network platforms and will meet several aspects of the constraints for data mining in sensor networks like: limited communication bandwidth, limited computing resources, limited power supply, and the need for fault-tolerance. The analysis of the dimensionality reduction obtained from the outputs of the neural-networks clustering algorithms shows that the communication costs of the proposed approach are significantly smaller, which is an important consideration in sensor-networks due to limited power supply. In this paper we will present two possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several nodes, equipped with several sensors each.

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