Research Article
Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization
@ARTICLE{10.4108/eai.3-6-2021.170014, author={Neethu Maria John and Neena Joseph and Nimmymol Manuel and Sruthy Emmanuel and Simy Mary Kurian}, title={Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization}, journal={EAI Endorsed Transactions on Energy Web}, volume={9}, number={37}, publisher={EAI}, journal_a={EW}, year={2021}, month={6}, keywords={IT-enabled social transformation, Intelligent systems, Cooperative surveillance system, Data aggregation, Machine Learning, Particle Swarm Optimization}, doi={10.4108/eai.3-6-2021.170014} }- Neethu Maria John
Neena Joseph
Nimmymol Manuel
Sruthy Emmanuel
Simy Mary Kurian
Year: 2021
Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization
EW
EAI
DOI: 10.4108/eai.3-6-2021.170014
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.
Copyright © 2021 Neethu Maria John et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
