Intrusion detection of wireless sensor networks based on IPSO algorithm and BP neural network

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Qiuting Tian ◽  
Xue Lu ◽  
Letian Duan ◽  
Dezhi Han
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Wireless Sensor ◽  
Ipso Algorithm

Research on Wireless Sensor Networks Data Fusion Algorithm Based on COA-BP

2014 ◽  
Vol 539 ◽  
pp. 247-250
Author(s):  
Xiao Xiao Liang ◽  
Li Cao ◽  
Chong Gang Wei ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Network Lifetime ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Bp Algorithm ◽  
Wireless Sensor

To improve the wireless sensor networks data fusion efficiency and reduce network traffic and the energy consumption of sensor networks, combined with chaos optimization algorithm and BP algorithm designed a chaotic BP hybrid algorithm (COA-BP), and establish a WSNs data fusion model. This model overcomes shortcomings of the traditional BP neural network model. Using the optimized BP neural network to efficiently extract WSN data and fusion the features among a small number of original date, then sends the extracted features date to aggregation nodes, thus enhance the efficiency of data fusion and prolong the network lifetime. Simulation results show that, compared with LEACH algorithm, BP neural network and PSO-BP algorithm, this algorithm can effectively reduce network traffic, reducing 19% of the total energy consumption of nodes and prolong the network lifetime.

scholarly journals Random Neural Network Based Intelligent Intrusion Detection for Wireless Sensor Networks

2016 ◽  
Vol 80 ◽  
pp. 2372-2376 ◽  
Author(s):  
Ahmed Saeed ◽  
Ali Ahmadinia ◽  
Abbas Javed ◽  
Hadi Larijani
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Wireless Sensor ◽  
Random Neural Network
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