ew 18: e40

Research Article

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

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  • @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: Online First},
        volume={},
        number={},
        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
Neethu Maria John1,*, Neena Joseph1, Nimmymol Manuel1, Sruthy Emmanuel1, Simy Mary Kurian1
  • 1: Department of CSE, Mangalam College of Engineering, Kottayam, Kerala
*Contact email: johnneethumaria@gmail.com

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.