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  <doi_batch_id>aspg-22-3333-1791483522</doi_batch_id>
  <timestamp>20261008181842</timestamp>
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   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>International Journal of BIM and Engineering Science</full_title>
    <abbrev_title>IJBES</abbrev_title>
    <issn media_type="electronic">2571-1075</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>10</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Machine Learning-Enhanced Wireless Sensor Networks for Real-Time Environmental Monitoring</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Tatiraju V. Rajani</given_name>
      <surname>Kanth</surname>
      <affiliations>
       <institution>
        <institution_name>Senior Manager,TVR Consulting Services Private Limited, Gajularamaram, Medchal Malkangiri district, Hyderabad - 500055, Telegana, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>K.</given_name>
      <surname>Dhineshkumar</surname>
      <affiliations>
       <institution>
        <institution_name>Associate Professor, Department of Electrical and Electronics Engineering KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Haritima</given_name>
      <surname>Mishra</surname>
      <affiliations>
       <institution>
        <institution_name>Department: Artificial Intelligence and Machine Learning College: Sagar Institute of Research &amp; Technology, Bhopal, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Chandra Sekar</given_name>
      <surname>P.</surname>
      <affiliations>
       <institution>
        <institution_name>Professor, Department of ECE, Siddartha Institute of Science and Tech, Puttur, Andhra Pradesh, 51758, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Wireless Sensor Networks (WSNs) are pivotal for real-time environmental monitoring, providing valuable data on variables like temperature, humidity, and pollution levels. However, ensuring timely and accurate data transmission and analysis remains a challenge due to resource constraints in WSNs. This study introduces a machine learning-enhanced WSN framework that leverages predictive algorithms for efficient data processing and anomaly detection in real time. By integrating machine learning models, the system can predict environmental trends, detect sensor faults, and identify unusual events, improving data reliability and reducing network load. Experimental evaluations in a simulated environment show a 40% improvement in anomaly detection accuracy and a 35% reduction in data redundancy. Furthermore, this framework achieved a 25% increase in energy efficiency, enhancing network longevity. This machine learning-optimized WSN framework provides an effective solution for continuous environmental monitoring in applications such as wildlife tracking, pollution control, and smart agriculture.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>14</first_page>
     <last_page>19</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3333</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/IJBES.100103</doi>
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