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  <doc>
    <id>1401</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>24 Seiten</pageNumber>
    <edition/>
    <issue>13(22)</issue>
    <volume>2023</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-11-11</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">CNT-PUFs: highly robust and heat-tolerant carbon-nanotube-based physical unclonable functions</title>
    <abstract language="eng">In this work, we explored a highly robust and unique Physical Unclonable Function (PUF) based on the stochastic assembly of single-walled Carbon NanoTubes (CNTs) integrated within a wafer-level technology. Our work demonstrated that the proposed CNT-based PUFs are exceptionally robust with an average fractional intra-device Hamming distance well below 0.01 both at room temperature and under varying temperatures in the range from 23 °C to 120 °C. We attributed the excellent heat tolerance to comparatively low activation energies of less than 40 meV extracted from an Arrhenius plot. As the number of unstable bits in the examined implementation is extremely low, our devices allow for a lightweight and simple error correction, just by selecting stable cells, thereby diminishing the need for complex error correction. Through a significant number of tests, we demonstrated the capability of novel nanomaterial devices to serve as highly efficient hardware security primitives.</abstract>
    <identifier type="doi">10.3390/nano13222930</identifier>
    <identifier type="urn">urn:nbn:de:bvb:739-opus4-14011</identifier>
    <author>Florian Frank</author>
    <author>Simon Böttger</author>
    <author>Nico Mexis</author>
    <author>Nikolaos Athanasios Anagnostopoulos</author>
    <author>Ali Mohamed</author>
    <author>Martin Hartmann</author>
    <author>Harald Kuhn</author>
    <author>Christian Helke</author>
    <author>Tolga Arul</author>
    <author>Stefan Katzenbeisser</author>
    <author>Sascha Hermann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Carbon NanoTube (CNT)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Physical Unclonable Function (PUF)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nanomaterials (NMs)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>hardware security</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>security</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>privacy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Internet of Things (IoT)</value>
    </subject>
    <collection role="ddc" number="600">Technik, Technologie</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Fakultät für Informatik und Mathematik</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/1401/Frank_Anagnostopoulos_etal_CNT-PUFs.pdf</file>
  </doc>
  <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>
</export-example>
