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  <doc>
    <id>1185</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>22 Seiten</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>23</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel, Switzerland</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-02-20</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm</title>
    <abstract language="eng">In the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the efficiency of the LoRa Wide-Area Network (LoRaWAN), three criteria can be considered, namely, the Packet Delivery Rate (PDR), Energy Consumption (EC), and coverage area. A set of transmission parameters have to be configured to establish a communication link. These parameters can affect the data rate, noise resistance, receiver sensitivity, and EC. The Adaptive Data Rate (ADR) algorithm is a mechanism to configure the transmission parameters of EDs aiming to improve the PDR. Therefore, we introduce a new algorithm using the Multi-Armed Bandit (MAB) technique, to configure the EDs’ transmission parameters in a centralized manner on the Network Server (NS) side, while improving the EC, too. The performance of the proposed algorithm, the Low-Power Multi-Armed Bandit (LP-MAB), is evaluated through simulation results and is compared with other approaches in different scenarios. The simulation results indicate that the LP-MAB’s EC outperforms other algorithms while maintaining a relatively high PDR in various circumstances.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="doi">10.3390/s23042363</identifier>
    <identifier type="urn">urn:nbn:de:bvb:739-opus4-11853</identifier>
    <enrichment key="opus.import.date">2023-03-08T03:14:43+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">bf25cb4225585f91bfccc547a594d53f</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Nikolaos Athanasios Anagnostopoulos</author>
    <author>Benyamin Teymuri</author>
    <author>Reza Serati</author>
    <author>Mehdi Rasti</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Internet of Things (IoT)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LoRaWAN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>adaptive configuration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reinforcement learning</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="FundingAcknowledgement" number="">Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau.</collection>
    <thesisPublisher>Universität Passau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-uni-passau/files/1185/sensors-23-02363-v2.pdf</file>
  </doc>
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