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PLS-Assisted Offloading for Edge Computing-Enabled Post-Quantum Security in Resource-Constrained Devices
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PLS-Assisted Offloading for Edge Computing-Enabled Post-Quantum Security in Resource-Constrained Devices

Hamid Amiriara, Mahtab Mirmohseni and Rahim Tafazolli
2025 IEEE 26th International Workshop on Signal Processing and Artificial Intelligence for Wireless Communications (SPAWC 2025), pp.1-5
IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
2025 IEEE 26th International Workshop on Signal Processing and Artificial Intelligence in Wireless Communications (IEEE SPAWC 2025) (Guildford, Surrey, UK, 07/07/2025–10/07/2025)
04/09/2025

Abstract

edge computing latency minimization offloading Post-quantum cryptography resource allocation Resource management Scalability Servers Signal processing algorithms Standards Surveillance Uncertainty Wireless networks Cryptography Internet of Things
With the advent of post-quantum cryptography (PQC) standards, it has become imperative for resourceconstrained devices (RCDs) in the Internet of Things (IoT) to adopt these quantum-resistant protocols. However, the high computational overhead and the large key sizes associated with PQC make direct deployment on such devices impractical. To address this challenge, we propose an edge computing-enabled PQC framework that leverages a physical-layer security (PLS)assisted offloading strategy, allowing devices to either offload intensive cryptographic tasks to a post-quantum edge server (PQES) or perform them locally. Furthermore, to ensure data confidentiality within the edge domain, our framework integrates two PLS techniques: offloading RCDs employ wiretap coding to secure data transmission, while non-offloading RCDs serve as friendly jammers by broadcasting artificial noise to disrupt potential eavesdroppers. Accordingly, we co-design the computation offloading and PLS strategy by jointly optimizing the device transmit power, PQES computation resource allocation, and offloading decisions to minimize overall latency under resource constraints. Numerical results demonstrate significant latency reductions compared to baseline schemes, confirming the scalability and efficiency of our approach for secure PQC operations in IoT networks.
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