TY - JOUR A1 - Frank, Florian A1 - Böttger, Simon A1 - Mexis, Nico A1 - Anagnostopoulos, Nikolaos Athanasios A1 - Mohamed, Ali A1 - Hartmann, Martin A1 - Kuhn, Harald A1 - Helke, Christian A1 - Arul, Tolga A1 - Katzenbeisser, Stefan A1 - Hermann, Sascha T1 - CNT-PUFs: highly robust and heat-tolerant carbon-nanotube-based physical unclonable functions N2 - 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. KW - Carbon NanoTube (CNT) KW - Physical Unclonable Function (PUF) KW - Nanomaterials (NMs) KW - hardware security KW - security KW - privacy KW - Internet of Things (IoT) Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-14011 VL - 2023 IS - 13(22) PB - MDPI CY - Basel ER - TY - JOUR A1 - Anagnostopoulos, Nikolaos Athanasios A1 - Teymuri, Benyamin A1 - Serati, Reza A1 - Rasti, Mehdi ED - Xie, Bin ED - Wang, Ning ED - Gu, Yi ED - Stefanidis, Angelos T1 - LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm JF - Sensors N2 - 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. KW - Internet of Things (IoT) KW - LoRaWAN KW - adaptive configuration KW - machine learning KW - reinforcement learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-11853 SN - 1424-8220 VL - 23 IS - 4 PB - MDPI CY - Basel, Switzerland ER -