IoT-Enabled Wireless Sensor Networks and Geospatial Technology for Urban Infrastructure Management

Abstract

This study is the first to evaluate how effectively urban infrastructure may improve using an innovative approach that combines IoT-enabled wireless sensors, geospatial technology, and machine learning. Smart healthcare monitoring and management is a particular focus. We conducted a series of experiments and evaluations to prove the effectiveness of this approach in actual urban environments. In this research, a series of sensor networks is developed and deployed to collect real-time health data from patients. It is sent to a central controller, and then on up to the cloud for analysis. Various machine learning algorithms are used - such as ANN, LR, DT, and SVM - to predict patient health status on the basis of sensor readings. The results of our experiments show that the ANN model achieved an accuracy rate surpassing all others at 98.65%. Geospatial technology is also looked at in the research as a way to visualize and analyze urban health data. This is necessary for informed decision-making by healthcare providers and urban planners. This research paves the way for smarter, more resilient, and sustainable urban environments by using the latest technology and data-driven methods to advance urban infrastructure Management.