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

John, Neethu Maria and Joseph, Neena and Manuel, Nimmymol and Emmanuel, Sruthy and Kurian, Simy Mary (2021) Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization. EAI Endorsed Transactions on Energy Web. e40. ISSN 2032-944X

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: IT-enabled social transformation, Intelligent systems, Cooperative surveillance system, Data aggregation, Machine Learning, Particle Swarm Optimization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 20 Jul 2021 09:50
Last Modified: 20 Jul 2021 09:50
URI: https://eprints.eudl.eu/id/eprint/4882

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