scholarly journals Correction to: Joint Optimization of Fusion Rule Threshold and Transmission Power for Energy Efficient CSS in Cognitive Wireless Sensor Networks

2021 ◽  
Author(s):  
Girraj Sharma ◽  
Ritu Sharma
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Fusion Rule ◽  
Wireless Sensor ◽  
Joint Optimization ◽  
Transmission Power ◽  
Cognitive Wireless Sensor Networks

scholarly journals A Transmission Power Optimization with a Minimum Node Degree for Energy-Efficient Wireless Sensor Networks with Full-Reachability

Sensors ◽  
2013 ◽  
Vol 13 (3) ◽  
pp. 3951-3974 ◽  
Author(s):  
Yi-Ting Chen ◽  
Mong-Fong Horng ◽  
Chih-Cheng Lo ◽  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Power Optimization ◽  
Wireless Sensor ◽  
Node Degree ◽  
Transmission Power

scholarly journals A Robust and Energy Efficient Cooperative Spectrum Sensing Scheme in Cognitive Wireless Sensor Networks

2015 ◽  
Vol 7 (3) ◽  
pp. 140 ◽  
Author(s):  
Shaoyang Men ◽  
Pascal Chargé ◽  
Sébastien Pillement
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Energy Efficient ◽  
Evaluation Method ◽  
Cooperative Spectrum Sensing ◽  
Wireless Sensor ◽  
Hidden Terminal ◽  
Cognitive Wireless Sensor Networks

Cooperative spectrum sensing (CSS) is able to effectively solve the hidden terminal, depth attenuation, multipath shadows and other issues which are not addressed by the single-user sensing. Therefore, it has attracted a large amount of interest and several CSS algorithms have been proposed. However, they are not specifically tailored for cognitive wireless sensor networks (CWSNs) where transmission reliability, power management and interference avoidance are critical issues. In this paper, we propose a robust and energy efficient CSS scheme in CWSNs. Firstly, taking into account the limited energy of sensor node, especially the mobile node, we introduce the nodes of the network into multiple clusters for the CSS in order to save energy consumed in reporting results and exchanging information and extend the lifetime of the network. Secondly, we consider that some cognitive nodes may not work as expected. Hence, facing the problem of faulty nodes in clusters, we propose an evaluation method which considers simultaneously the node reliability and the mutually supportive degree among different nodes to support adapted decisions. Finally, after removing the node of low credibility, the energy efficiency and reliability of each cluster are improved significantly. Simulation results allow to validate that the proposed method outperforms the state of the art in energy efficiency and detection reliability, even in presence of faulty nodes.

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