Multiobjective fuzzy knowledge‐based bacterial foraging optimization for congestion control in clustered wireless sensor networks

2021 ◽  
Author(s):  
Elaheh Moharamkhani ◽  
Behrouz Zadmehr ◽  
Saeideh Memarian ◽  
Mohammad Javad Saber ◽  
Mohammad Shokouhifar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Congestion Control ◽  
Wireless Sensor ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Knowledge Based ◽  
Fuzzy Knowledge

A Fuzzy Knowledge Based Fault Tolerance Mechanism for Wireless Sensor Networks

2020 ◽  
pp. 679-697
Author(s):  
Sasmita Acharya ◽  
C. R. Tripathy
Keyword(s):  
Wireless Sensor Networks ◽  
Fault Tolerance ◽  
Sensor Networks ◽  
Cluster Head ◽  
Network Routing ◽  
Wireless Sensor ◽  
Tolerance Mechanism ◽  
Adaptive Clustering ◽  
Knowledge Based ◽  
Fuzzy Knowledge

Wireless Sensor Networks (WSNs) are the focus of considerable research for different applications. This paper proposes a Fuzzy Knowledge based Artificial Neural Network Routing (ANNR) fault tolerance mechanism for WSNs. The proposed method uses an exponential Bi-directional Associative Memory (eBAM) for the encoding and decoding of data packets and application of Intelligent Sleeping Mechanism (ISM) to conserve energy. A combination of fuzzy rules is used to identify the faulty nodes in the network. The Cluster Head (CH) acts as the data aggregator in the network. It applies the fuzzy knowledge based Node Appraisal Technique (NAT) in order to identify the faulty nodes in the network. The performance of the proposed ANNR is compared with that of Low-Energy Adaptive Clustering Hierarchy (LEACH), Dual Homed Routing (DHR) and Informer Homed Routing (IHR) through simulation.

A Fuzzy Knowledge Based Fault Tolerance Mechanism for Wireless Sensor Networks

2018 ◽  
Vol 5 (1) ◽  
pp. 99-116 ◽  
Author(s):  
Sasmita Acharya ◽  
C. R. Tripathy
Keyword(s):  
Wireless Sensor Networks ◽  
Fault Tolerance ◽  
Sensor Networks ◽  
Cluster Head ◽  
Network Routing ◽  
Wireless Sensor ◽  
Tolerance Mechanism ◽  
Data Packets ◽  
Knowledge Based ◽  
Fuzzy Knowledge

Wireless Sensor Networks (WSNs) are the focus of considerable research for different applications. This paper proposes a Fuzzy Knowledge based Artificial Neural Network Routing (ANNR) fault tolerance mechanism for WSNs. The proposed method uses an exponential Bi-directional Associative Memory (eBAM) for the encoding and decoding of data packets and application of Intelligent Sleeping Mechanism (ISM) to conserve energy. A combination of fuzzy rules is used to identify the faulty nodes in the network. The Cluster Head (CH) acts as the data aggregator in the network. It applies the fuzzy knowledge based Node Appraisal Technique (NAT) in order to identify the faulty nodes in the network. The performance of the proposed ANNR is compared with that of Low-Energy Adaptive Clustering Hierarchy (LEACH), Dual Homed Routing (DHR) and Informer Homed Routing (IHR) through simulation.

scholarly journals Fuzzy Logic Based Hardware Faulty Node Detection And Redundancy Mechanism For Wireless Sensor Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 45-52
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Faulty Node ◽  
Knowledge Based ◽  
Fuzzy Knowledge

In recent years, applications of wireless sensor network (WSN) is emerged as the revolutionary phase in many functional areas such as industrial, environmental, business, military and many need based self-intelligent real time systems. Some of the applications require data communication from harsh physical environment which poses great challenges to wireless sensor networks. The deployment of these sensor nodes in the hostile environment cause sensor nodes failure. This demands fast, redundant fault tolerant, energy saving approaches which meet the requirements of most recurring failures and path disruption scenarios in wireless sensor networks. Hence there is need for fuzzy knowledge based fault detection because traditional fault detection methods are endured by low detection accuracy. The proposed fuzzy knowledge based faulty node detection and redundancy approach (FNDRA) is presented to identify the faulty nodes and provide the management method for nodes reusability. The effectiveness of the proposed approach was implemented using Matlab and the results shows that the proposed approach meets the constraints and requirements of most common and predicated critical failure scenarios.

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