Search

This research examines the profound effects of integrating IoT-enabled predictive maintenance in sustainable transportation fleets. By using real-time sensor data, this implementation aims to enhance fleet dependability and operational efficiency. The fleet, including a variety of vehicles such as electric buses, hybrid cars, electric trucks, CNG-powered vans, and hybrid buses, is constantly monitored using IoT sensors that capture important characteristics like engine temperature, battery voltage, and brake wear percentages. The predictive maintenance algorithms adapt maintenance schedules in response to live sensor data, enabling a proactive strategy that tackles prospective problems before they result in major failures. The examination of the maintenance records reveals prompt actions, showcasing the system’s efficacy in reducing operational interruptions and improving the overall dependability of the fleet. Moreover, the examination of percentage change confirms the system’s flexibility, demonstrating its capacity to anticipate fluctuations in engine temperature, battery voltage, and brake wear. The findings highlight the system’s ability to adapt to various operating situations and its contribution to lowering maintenance expenses while enhancing operational effectiveness. The established approach incorporates ethical issues, such as data security and privacy, to ensure responsible adoption of IoT technology. This study has broader ramifications beyond the particular dataset, providing a detailed plan for incorporating IoTenabled predictive maintenance into contemporary transportation infrastructures. The study’s findings offer valuable insights into the potential of proactive maintenance strategies to transform the transportation industry towards sustainability. This contributes to a future where fleets operate with increased efficiency, reduced environmental impact, and improved reliability.

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. [ABSTRACT FROM AUTHOR]