Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization.

Autor:	John, Neethu Maria, Joseph, Neena, Manuel, Nimmymol, Emmanuel, Sruthy, Kurian, Simy Mary
Předmět:	ARTIFICIAL intelligence in industry MACHINE learning INDUSTRIAL applications MANUFACTURING industry management PARTICLE swarm optimization
Zdroj:	EAI Endorsed Transactions on the Energy Web; 2022, Vol. 9 Issue 37, p1-8, 8p
Abstrakt:	The present pandemic demands touchless and autonomous, intelligent surveillance system to reduce human involvement. Heterogeneous types of sensors are used to improve the effectiveness of this surveillance system and a cooperative approach of such sensors will make the system further efficient due to variation in users such as corporate office, universities, manufacturing industries etc. The application of effective data aggregation technique on sensors is essential as the energy utilization of the system degrades the lifetime, coverage and computational overhead. The application of bioinspired optimization technique like Particle Swarm Optimization for scheduling leads to improved performance of the system as the nature of the system is heterogeneous and requirement is multi-objective. Similarly the application of Support vector Machine as a classification and prediction algorithm on the huge data collected periodically makes the system further autonomous and intelligent. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu