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  <doi_batch_id>aspg-18-3606-1792025534</doi_batch_id>
  <timestamp>20261015005214</timestamp>
  <depositor>
   <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>Journal of Intelligent Systems and Internet of Things</full_title>
    <abbrev_title>JISIoT</abbrev_title>
    <issn media_type="print">2769-786X</issn>
    <issn media_type="electronic">2690-6791</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>16</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Multi-objective Optimization in Satellite-Assisted UAVs</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mohammed Ahmed</given_name>
      <surname>Jubair</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Technical Engineering, Al-Maarif University College, Al-Ramadi, 31001, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Shaima Miqdad Mohamed</given_name>
      <surname>Najeeb</surname>
      <affiliations>
       <institution>
        <institution_name>Technical Engineering College for Computer and AI, Northern Technical University, 41000, Mosul, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Kifaa Hadi</given_name>
      <surname>Thanoon</surname>
      <affiliations>
       <institution>
        <institution_name>Technical Engineering College for Computer and AI, Northern Technical University, 41000, Mosul, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mujahid Hamood Hilal</given_name>
      <surname>Alzakwani</surname>
      <affiliations>
       <institution>
        <institution_name>Centre for Language and Foundation Studies, A’Sharqiyah University (ASU), 400 Ibra, Oman</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Fatima Hashim</given_name>
      <surname>Abbas</surname>
      <affiliations>
       <institution>
        <institution_name>Medical Laboratories Techniques Department, Al-Mustaqbal University College, 51001, Babil, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Rabei Raad</given_name>
      <surname>Ali</surname>
      <affiliations>
       <institution>
        <institution_name>Technical Engineering College for Computer and AI, Northern Technical University, 41000, Mosul,</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Nowadays, Vehicular communication is used in intelligent transmission applications. The number of vehicles used in a particular region has numerously increased energy consumption, computation delay, and computation overhead. In this paper, Multi-Objective Optimization in Satellite Assisted UAVs (MO-SAUVs) is proposed under an improved Ant Colony Optimization (IACO) algorithm. The procedures that are considered for the process of MO are optimal logistics distribution, path prediction-based pheromone deposition, and evaporation. Using this method, effective region selection for the UAVs is performed which leads to improving the network energy efficiency by decreasing energy consumption and delay. The simulation is performed in NS2 and the proposed MO-SAUAVs method is compared with the TA-SAUAVs method and PL-SAUAVs method according to different parameters. The results show that the proposed MO-SAUAVs method achieves lower computation delay (70ms to 110ms), higher energy efficiency (6% to 16%), lower energy consumption (7% to 14%), and packets lower computation overhead (500 packets to 700) when we were compared with TA-SAUAVs and PL-SAUAVs.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>189</first_page>
     <last_page>198</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3606</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_data>
     <doi>10.54216/JISIoT.160116</doi>
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