Adaptive extreme learning machine ‐ fuzzy system framework for energy optimization of IOTs in Wireless Sensor Networks

2020 ◽  
Author(s):  
Deepa S.N. ◽  
Jasmine Selvakumari Jeya I ◽  
Aruna Devi B
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Extreme Learning Machine ◽  
Fuzzy System ◽  
Energy Optimization ◽  
Wireless Sensor ◽  
System Framework ◽  
Learning Machine

scholarly journals An Enhanced Extreme Learning Machine for Dissolved Oxygen Prediction in Wireless Sensor Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 198730-198739
Author(s):  
Liang Kuang ◽  
Pei Shi ◽  
Chi Hua ◽  
Beijing Chen ◽  
Hui Zhu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Dissolved Oxygen ◽  
Extreme Learning Machine ◽  
Wireless Sensor ◽  
Learning Machine

scholarly journals A Novel Fault Diagnosis Strategy for Heterogeneous Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Li Cao ◽  
Yinggao Yue ◽  
Yong Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Heterogeneous Wireless Sensor Networks ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Fault diagnosis is a guarantee for the reliable operation of heterogeneous wireless sensor networks, and accurate fault prediction can effectively improve the reliability of wireless sensor networks. First, it summarizes the node fault classification and common fault diagnosis methods of heterogeneous wireless sensor networks. After that, taking advantage of the short learning time, fewer parameter settings, and good generalization ability of kernel extreme learning machine (KELM), the collected sample data of the sensor node hardware failure is introduced into the trained kernel extreme learning machine and realizes the fault identification of various hardware modules of the sensor node. Regarding the regularization coefficient C and the kernel parameter s in KELM as the model parameters, it will affect the accuracy of the fault diagnosis model of the kernel extreme learning machine. A method for the sensor nodes fault diagnosis of heterogeneous wireless sensor networks based on kernel extreme learning machine optimized by the improved artificial bee colony algorithm (IABC-KELM) is proposed. The proposed algorithm has stronger ability to solve regression fault diagnosis problems, better generalization performance, and faster calculation speed. The experimental results show that the proposed algorithm improves the accuracy of the hardware fault diagnosis of the sensor nodes and can be better applied to the node hardware fault diagnosis of heterogeneous wireless sensor networks.

scholarly journals Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1334 ◽  
Author(s):  
Atia Javaid ◽  
Nadeem Javaid ◽  
Zahid Wadud ◽  
Tanzila Saba ◽  
Osama Sheta ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Extreme Learning Machine ◽  
Belief Function ◽  
Decision Fusion ◽  
Wireless Sensor ◽  
Support Vector ◽  
Learning Machine ◽  
Fusion Approach

Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission. The decentralized classification fusion problem was the reason to use the belief function-based decision fusion approach in Wireless Sensor Networks (WSNs). With the consideration of improving the belief function fusion approach, we have proposed four classification techniques, namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM). In addition, WSNs are prone to errors and faults because of their different software, hardware failures, and their deployment in diverse fields. Because of these challenges, efficient fault detection methods must be used to detect faults in a WSN in a timely manner. We have induced four types of faults: offset fault, gain fault, stuck-at fault, and out of bounds fault, and used enhanced classification methods to solve the sensor failure issues. Experimental results show that ERELM gave the first best result for the improvement of the belief function fusion approach. The other three proposed techniques ESVM, EELM, and EKNN provided the second, third, and fourth best results, respectively. The proposed enhanced classifiers are used for fault detection and are evaluated using three performance metrics, i.e., Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER). Simulations show that the proposed methods outperform the existing techniques and give better results for the belief function and fault detection in WSNs.

scholarly journals Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1046 ◽  
Author(s):  
Yang Zhang ◽  
Yun Liu ◽  
Han-Chieh Chao ◽  
Zhenjiang Zhang ◽  
Zhiyuan Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Extreme Learning Machine ◽  
Incomplete Data ◽  
Evidence Theory ◽  
Wireless Sensor ◽  
Learning Machine
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