2690-6791ISSN (Online)
2769-786XISSN (Print)
Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques
Read MoreDoi: https://doi.org/10.54216/JISIoT.130101
Vol. 13 Issue. 1 PP. 08-20, (2024)
Cardiovascular diseases detection or diagnosis on appropriate time is crucial to avoid health complications. In this study, an advanced procedure for classifying changes in the blood pressure has been used analyzing the wave-forms inside the arterial system where such variation can occur due to improper timing in intra-aortic balloon pump (IABP) control. Inaccurate pressure extends with probable injury can be caused by improper timing in the heart valve in both pumping and compression of the balloon. This investigation focuses on accurately recognizing and classifying any irregularities in the artery wave-forms in IABP in the blood pressure initiated by mistiming. Accumulated blood pressure records are used for the progression of providing information to IABP trainer. The wave-forms require pre-handling employing image digitizing software to acquire automated identifications. Any undesirable image features have been removed using Wavelet in MATLAB software. Afterward, such features can be employed to develop a technique for arrangement depending on neural networks. The artificial neural network technique has used marked data to properly detect irregularities in wave-forms in vascular blood pressure due to improper IABP timing. As a result, the validation has proved to appropriately recognize and classify such anomalies, denoting a considerable prospect to improve patient protection with an efficacy of treatment in the area of cardiovascular prescription.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130105
Vol. 13 Issue. 1 PP. 59-70, (2024)
Cardiovascular diseases (CVD) stand as the leading cause of global mortality, claiming millions of lives annually. An electrocardiogram (ECG) records the heart's electrical activity based on the Internet of Things (IoT), crucial in detecting cardiac arrhythmias (CA), characterized by irregular heart rates and rhythms. Signals from the MIT-BIH Arrhythmia Physio net database are analyzed. This chapter aims to propose a hybrid approach merging Genetic Algorithm-Support Vector Machine (GSVM) and Particle Swarm Optimization-Support Vector Machine (PSVM) for CA classification. The study introduces an algorithm for categorizing ECG beats into six groups using Independent Component Analysis (ICA)-derived features. Optimal SVM settings are determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on ICA features computed via non-parametric power spectral estimation. The research delves into the origins and methodologies of GA and PSO. Simulation results comparing GSVM and PSVM are presented, emphasizing PSVM's superior performance in accuracy, sensitivity, specificity, and positive predictivity. Detailed performance metrics, including Sensitivity, Specificity, Positive Predictivity, and Accuracy percentages, are scrutinized and compared against the top classifier. The findings endorse PSVM's superiority over GSVM, indicating enhanced performance across multiple evaluation criteria.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130106
Vol. 13 Issue. 1 PP. 71-82, (2024)
This research introduces a novel approach to intelligent IoT-based audio signal processing for healthcare applications. Leveraging advanced feature extraction techniques such as Mel-Frequency Cepstral Coefficients (MFCC) and Wavelet Transform, combined with sophisticated classification models like Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), the proposed method demonstrates superior performance in accurately classifying healthcare data. Through extensive experimentation and analysis, the method achieves high accuracy, precision, recall, and F1 score, while exhibiting robustness in discriminating between different classes and maintaining precision in classification, as evidenced by its high AUC-ROC and AUC-PR values. The ablation study provides insights into the significance of key components and parameters, offering guidance for further refinement and optimization of the method. Overall, the proposed method holds promise for revolutionizing healthcare management through proactive monitoring and intervention, leading to improved patient outcomes and healthcare delivery.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130107
Vol. 13 Issue. 1 PP. 83-98, (2024)
Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-propagation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtained by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier uses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artificial feed-forward neural networks provides a rich framework for studying the Back Propagation Algorithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance characteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, specificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperforms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the most effective classifier for identifying cardiac arrhythmias.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130108
Vol. 13 Issue. 1 PP. 99-110, (2024)