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

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...

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Autores principales:	Neethu John, Neena Joseph, Nimmymol Manuel, Sruthy Emmanuel, Simy Kurian
Formato:	article
Lenguaje:	EN
Publicado:	European Alliance for Innovation (EAI) 2022
Materias:	it-enabled social transformation intelligent systems cooperative surveillance system data aggregation machine learning particle swarm optimization Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/634b66bef9a14cbc8e1ce77312f52fc7
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id	oai:doaj.org-article:634b66bef9a14cbc8e1ce77312f52fc7
record_format	dspace
spelling	oai:doaj.org-article:634b66bef9a14cbc8e1ce77312f52fc72021-11-30T11:07:32ZEnergy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization2032-944X10.4108/eai.3-6-2021.170014https://doaj.org/article/634b66bef9a14cbc8e1ce77312f52fc72022-01-01T00:00:00Zhttps://eudl.eu/pdf/10.4108/eai.3-6-2021.170014https://doaj.org/toc/2032-944XThe 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 bio-inspired 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.Neethu JohnNeena JosephNimmymol ManuelSruthy EmmanuelSimy KurianEuropean Alliance for Innovation (EAI)articleit-enabled social transformation intelligent systems cooperative surveillance system data aggregation machine learningparticle swarm optimizationScienceQMathematicsQA1-939Electronic computers. Computer scienceQA75.5-76.95ENEAI Endorsed Transactions on Energy Web, Vol 9, Iss 37 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	it-enabled social transformation intelligent systems cooperative surveillance system data aggregation machine learning particle swarm optimization Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
spellingShingle	it-enabled social transformation intelligent systems cooperative surveillance system data aggregation machine learning particle swarm optimization Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95 Neethu John Neena Joseph Nimmymol Manuel Sruthy Emmanuel Simy Kurian Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization
description	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 bio-inspired 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.
format	article
author	Neethu John Neena Joseph Nimmymol Manuel Sruthy Emmanuel Simy Kurian
author_facet	Neethu John Neena Joseph Nimmymol Manuel Sruthy Emmanuel Simy Kurian
author_sort	Neethu John
title	Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization
title_short	Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization
title_full	Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization
title_fullStr	Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization
title_full_unstemmed	Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization
title_sort	energy efficient data aggregation and improved prediction in cooperative surveillance system through machine learning and particle swarm based optimization
publisher	European Alliance for Innovation (EAI)
publishDate	2022
url	https://doaj.org/article/634b66bef9a14cbc8e1ce77312f52fc7
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Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization

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