AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection
subject: Accuracy; Building Safety; Computational Time; Ensemble Learning; Fuzzy Logic Systems; Gradient Boosting Machines; Internet of Things; Latency Detection; Sensor Data; Support Vector Machine
description: The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. 2024 IEEE.
creator: Raja S.R.; Devi T.R.; Raj J.R.F.; Sankar V.K.; Krishnan R.S.; Venkatalakshmi R.
source: 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024 - Proceedings, pp. 73-80.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037642-5
format: Online
language: English
type: Conference paper
coverage: Raja S.R., Hindustan Institute of Technology and Science, Computer Applications, Chennai, India; Devi T.R., Christ University, Department of Computer Science and Engineering, Bangalore, India; Raj J.R.F., Saveetha Institute of Medical and Technical Sciences, Department of Electronics and Communication, Saveetha School of Engineering, Chennai, India; Sankar V.K., Scad College of Engineering and Technology, Department of Civil Engineering, Cheranmahadevi, India; Krishnan R.S., Scad College of Engineering and Technology, Department of Electronics and Communication Engineering, Cheranmahadevi, India; Venkatalakshmi R., Psna College of Engineering and Technology, Department of Information Technology, Dindigul, India