5 research outputs found
Hybridization of Energy Optimization Technique for Cluster Based Routing using Various Computational Intelligence Methods in WSN
Get PDFApproaches in WSN technology has determined by opportunity of tiny and inexpensive sensor nodes with adequacy of sensing multiple kinds of information processing and wireless communication. Network lifetime and energy efficiency are major indexes of WSN. Several clustering techniques are intended to extend the network lifetime but whereas there is an issue of incompetent Cluster Head (CH) election. To overcome this issue, an Integration of Novel Memetic and Brain Storm Optimization approach with Levy Distribution (IoNM-BSOLyD) has been proposed for clustering using fitness function. In the meanwhile, election of CH is done by utilizing fitness function, which incorporates following amplitude such as energy, distance to adjacent nodes, distance to BS, and network load. After clustering, routing techniques decides the detecting and pursuing the route in WSN. In this proposed work, a Water Wave Optimization with Hill Climbing technique (WWO-HCg) is introduced for routing purpose. This proposed methodology deals with ternary QoS aspect such as network delay, energy consumption, packet delivery ratio, network lifetime and security to select optimal path and enhance QoS as well. This proposed protocol provides better performance result than other contemporary protocols
Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application
Get PDFThe Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model’s performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology’s experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology
EEG Signal Classification Automation using Novel Modified Random Forest Approach
Get PDFDigitalization and automation are the two aspects in the medical industry that define compliance with industry 4.0. Automation is essential for speeding up the diagnosis process, while digitalization leads to smart medicine and efficient diagnosis. Epilepsy is one such disease that can use these automation techniques. The automatic monitoring of epilepsy EEG is of great significance in clinical medicine. Aiming at the non-stationary characteristics of EEG signals, the classification of EEG signals is based on the combination of overall empirical mode. It is proposed using the random forest method. The EEG signal data set has an epileptic interval over 200 single-channel signals with a seizure period. A total of 819,400 data are used as samples. First, the overall epileptic EEG signal modal is decomposed into multiple intrinsic modal functions. The effective features are extracted from the first-order intrinsic modal function. Finally, random forest and Least Square SVM (LS-SVM) are considered to classify the EEG signals characteristics. The correct recognition rate of random forest and LS-SVM is compared. The results show that random forest classification method has an ideal classification effect on epilepsy EEG signals during and between seizures. The recognition accuracy is 99% and 60%, which is higher than the accuracy of the LS-SVM. The proposed method improves clinical epilepsy. The efficiency of EEG signals analysis
