Localization algorithm for mobile nodes in wireless sensor networks based on subsection learning of double-layers BP neural network

2014 ◽  
Author(s):  
Mao Yuming
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Bp Neural Network ◽  
Wireless Sensor ◽  
Double Layers ◽  
Localization Algorithm ◽  
Mobile Nodes

scholarly journals An improved DV-Hop localization with minimum connected dominating set for mobile nodes in wireless sensor networks

2018 ◽  
Vol 14 (1) ◽  
pp. 155014771875563 ◽  
Author(s):  
Gulshan Kumar ◽  
Mritunjay Kumar Rai ◽  
Rahul Saha ◽  
Hye-jin Kim
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Dominating Set ◽  
Connected Dominating Set ◽  
Wireless Sensor ◽  
Network Simulator ◽  
Localization Algorithm ◽  
Mobile Nodes ◽  
Minimum Connected Dominating Set ◽  
Anchor Nodes

Localization is one of the key concepts in wireless sensor networks. Different techniques and measures to calculate the location of unknown nodes were introduced in recent past. But the issue of nodes’ mobility requires more attention. The algorithms introduced earlier to support mobility lack the utilization of the anchor nodes’ privileges. Therefore, in this article, an improved DV-Hop localization algorithm is introduced that supports the mobility of anchor nodes as well as unknown nodes. Coordination of anchor nodes creates a minimum connected dominating set that works as a backbone in the proposed algorithm. The focus of the research paper is to locate unknown nodes with the help of anchor nodes by utilizing the network resources efficiently. The simulated results in network simulator-2 and the statistical analysis of the data provide a clear impression that our novel algorithm improves the error rate and the time consumption.

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.

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