Fuzzy Logic Based Congestion Estimation for QoS in Wireless Sensor Network

2007 ◽  
Author(s):  
Saad A. Munir ◽  
Yu Wen Bin ◽  
Ren Biao ◽  
Ma Jian
Keyword(s):  
Fuzzy Logic ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor

Fuzzy logic-based jamming detection algorithm for cluster-based wireless sensor network

2018 ◽  
Vol 31 (10) ◽  
pp. e3567 ◽  
Author(s):  
K. P. Vijayakumar ◽  
P. Ganeshkumar ◽  
M. Anandaraj ◽  
K. Selvaraj ◽  
P. Sivakumar
Keyword(s):  
Fuzzy Logic ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Jamming Detection

scholarly journals Fuzzy logic for cluster head selection in wireless sensor network

2016 ◽  
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Rozita Jailani ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Cluster Head Selection

scholarly journals A Novel Clustering Approach in Wireless Sensor Network Using Genetic Algorithm and Fuzzy Logic

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Fatemehzahra Gholami Tirkolaei ◽  
Faramarz E. Seraji
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Main Challenge ◽  
Clustering Approach ◽  
Reduce Energy Consumption ◽  
Reduction Of Energy Consumption

<p>Wireless sensor network consists of hundred or thousand sensor nodes that are connected together and work simultaneously to perform some special tasks. The restricted energy of sensor nodes is the main challenge in wireless sensor network as node energy depletion causes node death. Therefore, some techniques should be exerted to reduce energy consumption in these networks. One of the techniques to reduce energy consumptions most effectively is the use of clustering in wireless sensor networks.</p><p>There are various methods for clustering process, among which LEACH is the most common and popular one. In this method, clusters are formed in a probabilistic manner. Among clustering strategies, applying evolutional algorithm and fuzzy logic simultaneously are rarely taken into account. The main attention of previous works was energy consumption and less attention was paid to delay.</p><p>In the present proposed method, clusters are constructed by an evolutional algorithm and a fuzzy system such that in addition to a reduction of energy consumption, considerable reduction of delay is also obtained. The simulation results clearly reveal the superiority of the proposed method over other reported approaches.</p>

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