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[email protected] +91 9840521421 Scholarly Open Access, Peer-reviewed, Refereed Journal E-ISSN 3048-6408 Since 2024
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Original Article

Leveraging Hybrid Machine Learning for Wireless Sensor Network Optimization

  • Paper ID 17
  • Volume 1, Issue 3
  • Pages: 10-12
  • Published Online: September-December 2024
  • 409 Views
Open Access Peer Reviewed

Authors

  • Raj Mamta1
  • Gupta Sweta2
  • Tiwari Sachin3
  • Hyder Shahin4

1 Assistant Professor, Department of Electronics Communication Engineering, Vedica Institute of Technology, Bhopal, Madhya Pradesh, India. 2 3 4 Students, Department of Electronics Communication Engineering, Vedica Institute of Technology, Bhopal, Madhya Pradesh, India.

Article Information

DOI
No Doi
Published Online
September-December 2024
Pages
10-12
Article Type
Original Article
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Abstract

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The IoT is acquiring fame as a progressive innovation that permits all that can be connected to be associated. Besides, IoT can connection and accumulate information from anything as little as a pill to as enormous as a plane. Then, at that point, IoT joins different things associated by sensors and empowers them to convey without the requirement for human intercession, on account of the headway of advanced knowledge. Besides, the IoT has practically connected the globe, going about as a computerized texture that interfaces the virtual also, actual universes. Little sensor hubs are frequently kept up with and controlled by a base station in a WSN. Be that as it may, the sensor hubs' battery limit is restricted by their size. Since sensor hubs are arbitrary and dynamic in nature, it is exceedingly difficult to introduce an elective power hotspot for every sensor hub. The trustworthiness of a WSN is vigorously reliant upon the life expectancy of its sensor hubs, and the loss of a solitary hub might have a critical effect. Research in this space is presently zeroing in on ways of diminishing how much imperativeness that is utilized by hubs.

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  • ISSN 3048-6408
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