On the use of Unified And-Or fuzzy operator for distributed node exhaustion attack decision-making in wireless sensor networks

2010 ◽  
Author(s):  
Salman A. Khan ◽  
Zubair A. Baig
Keyword(s):  
Decision Making ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Fuzzy Operator

Applying fuzzy logic for decision-making on Wireless Sensor Networks

2007 ◽  
Author(s):  
Luci Pirmez ◽  
Flavia C. Delicato ◽  
Paulo F. Pires ◽  
Ana L. Mostardinha ◽  
Nelson S. de Rezende
Keyword(s):  
Decision Making ◽  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Wireless Sensor

scholarly journals Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Rongrong Yin ◽  
Xueliang Yin ◽  
Mengdi Cui ◽  
Yinghan Xu
Keyword(s):  
Decision Making ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evaluation Method ◽  
Structural Characteristics ◽  
Wireless Sensor ◽  
Computational Time ◽  
Node Degree ◽  
Node Importance ◽  
Multi Attribute Decision Making

Abstract Identifying important nodes is very crucial to design efficient communication networks or contain the spreading of information such as diseases and rumors. The problem is formulated as follows: given a network, which nodes are the more important? Most current studies did not incorporate the structure change as well as application features of a network. Aiming at the node importance evaluation in wireless sensor networks, a new method which ranks nodes according to their structural importance and performance impact is proposed. Namely, this method considers two aspects of the network, network structural characteristics and application requirements. This method integrates four indicators which reflect the node importance, namely, node degree, number of spanning trees, delay, and network energy consumption. Firstly, the changes in the four indicators are analyzed using the node deletion method. Then, the TOPSIS multi-attribute decision-making method is applied to merge these four evaluation indicators. On this basis, a more comprehensive evaluation method (MADME) for node importance is obtained. Theory study reveals MADME method saves computational time. And the simulation results show the superiority of the MADME method over various algorithms such as the N-Burt method, betweenness method, DEL-Node method, and IE-Matrix method. The accuracy of the evaluation can be improved, and the key nodes determined by the MADME method have a more obvious effect on the network performance. Our method can provide guidance on influential node identification in the network.

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