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

Authors

  • Neethu Maria John Mangalam College of Engineering
  • Neena Joseph Mangalam College of Engineering
  • Nimmymol Manuel Mangalam College of Engineering
  • Sruthy Emmanuel Mangalam College of Engineering
  • Simy Mary Kurian Mangalam College of Engineering

DOI:

https://doi.org/10.4108/eai.3-6-2021.170014

Keywords:

IT-enabled social transformation, Intelligent systems, Cooperative surveillance system, Data aggregation, Machine Learning, Particle Swarm Optimization

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.

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Published

03-06-2021

How to Cite

1.
Maria John N, Joseph N, Manuel N, Emmanuel S, Mary Kurian S. Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization. EAI Endorsed Trans Energy Web [Internet]. 2021 Jun. 3 [cited 2024 Dec. 23];9(37):e4. Available from: https://publications.eai.eu/index.php/ew/article/view/66