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.
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.
Internet of Things (IoT) devices are going to be the primary data source in smart cities, often generating and communicating critical, sensitive, and private data that face threats of confidentiality breaches due to the rise of quantum computers. Even though NIST has approved post-quantum cryptography (PQC) schemes that promise to protect from quantum computer threats, these schemes can not be implemented efficiently on IoT devices, which often have computational and memory constraints. Hence, due to the substantial key size requirements and computationally intensive nature of PQC schemes, their direct deployment on IoT devices within smart city architecture is impeded by inherent computational and storage limitations. Consequently, it is imperative to devise PQC-based, resource-efficient authentication protocols tailored to IoT devices operating within smart cities. In this paper, we propose an offloading scheme that uses PQ Edge Server (PQES) for the network that performs PQC-related computation and memory operations for IoT devices in the network. The suggested technique transfers all storage and processing responsibilities from IoT devices in a network to ensure their normal operation and performance without any compromise. PQES generates a distinct PQ key pair for every network device and oversees a separate stream for each device. Therefore, IoT devices in the network may function without cryptographic processes, improving their efficiency by removing the need for conventional cryptography. Our results show that the proposed scheme reduces RAM usage to 1.05 KB and CPU usage to 1.75%, with maximum CPU context switches of 92.8. The proposed scheme uses TCP, a transport layer protocol, which is applicable to lightweight end devices without application-level protocols like HTTP or MQTT. When benchmarked on a Raspberry Pi, the PQES can handle 1000 concurrent connections and use a maximum of 73.5% of RAM. In comparison, previous research used at least 5.1 MB of RAM for one concurrent connection and may use up to 552.6 MB of RAM for 1000 concurrent connections. The maximum CPU usage for our method was 1.75%, whereas the previous proposal had a utilization of 67.1%. The greatest CPU context switches for our system were 92.8, far lower than the 3449 observed for the usual approach. Hence, our proposed scheme reduces the computational, memory, and network load for IoT devices by manifolds and increases device performance while providing PQC security.
With the rapid advancement of quantum computing technology, IoT edge devices widely deployed in industrial settings are facing severe threats to their quantum-resistant security. This work focuses on the challenges of adapting Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) for resource-constrained environments. This work systematically identifies four critical obstacles: difficulties in physical integration due to device heterogeneity; constraints imposed by the limited computing power and storage resources of edge nodes on PQC algorithm execution; compatibility gaps between QKD/PQC and existing industrial protocols (such as Modbus and Profinet); and the engineering cost pressures associated with large-scale deployment. To address these issues, the paper proposes three synergistic lightweight countermeasures: (1) a tiered deployment architecture for quantum-safe security that accommodates device heterogeneity, enabling dynamic trade-offs between security strength and resource overhead; (2) middleware for quantum-classical hybrid network protocol coordination, ensuring seamless integration between QKD/PQC and industrial control protocols; and (3) a three-tier distributed key management mechanism (edge node, network proxy, and central system) that offloads computation- and storage-intensive tasks to high-resource nodes, thereby alleviating bottlenecks at the edge. Experimental validation within a smart manufacturing factory's sensor network demonstrates that the proposed scheme maintains post-quantum security capabilities (with QKD integration validated at the proof-of-concept level) while reducing per-node deployment costs by approximately 92.4% compared to pure QKD solutions and by about 32% compared to pure PQC solutions, all while facilitating seamless, non-disruptive upgrades. This study indicates that lightweight algorithm design, hardware-software co-optimization, and tiered architectural deployment constitute a viable pathway for advancing IoT edge security into the quantum-safe era.
The impending realization of quantum computing is a threat to the underlying security model of traditional cryptographic infrastructure, especially on the one based on Internet of Things (IoT) and edge computing setups. The post quantum cryptography (PQC) provides quantum resistant protocols that ensure quantum security against a combination of both classical and future quantum attackers, but adopting this technology in edge IoT ecosystems faces serious challenges in terms of resource limitations of low power devices. The paper suggests a performance-security model of PQC in edge IoT explaining the tradeoff between cryptographic robustness and operation efficiency. This model is a synthesis of empirical research in the recent field which looking at the performance of PQC algorithms on limited hardware, the integration issue with lightweight communication protocols, and strategic options such as algorithm optimization, hardware acceleration, hybrid cryptographic schemes, and offloading methods. The objectives of these strategies are to achieve the acceptable latency and energy consumption with the guarantee of quantum resilient security. The framework proposed helps decision making among researchers and practitioners who aim at implementing PQC on distributed resource limited IoT networks. Lastly, the paper also determines the research gaps and future directions to the achievement of scalable, efficient, and robust PQC enabled edge IoT ecosystems.
The impending realization of quantum computing is a threat to the underlying security model of traditional cryptographic infrastructure, especially on the one based on Internet of Things (IoT) and edge computing setups. The post quantum cryptography (PQC) provides quantum resistant protocols that ensure quantum security against a combination of both classical and future quantum attackers, but adopting this technology in edge IoT ecosystems faces serious challenges in terms of resource limitations of low power devices. The paper suggests a performance-security model of PQC in edge IoT explaining the tradeoff between cryptographic robustness and operation efficiency. This model is a synthesis of empirical research in the recent field which looking at the performance of PQC algorithms on limited hardware, the integration issue with lightweight communication protocols, and strategic options such as algorithm optimization, hardware acceleration, hybrid cryptographic schemes, and offloading methods. The objectives of these strategies are to achieve the acceptable latency and energy consumption with the guarantee of quantum resilient security. The framework proposed helps decision making among researchers and practitioners who aim at implementing PQC on distributed resource limited IoT networks. Lastly, the paper also determines the research gaps and future directions to the achievement of scalable, efficient, and robust PQC enabled edge IoT ecosystems.
Quantum computing is also posing a serious challenge to the classical cryptographic algorithms like RSA and ECC as they have vulnerability to quantum algorithms such as Shor and Grover. This is a serious problem to Internet of Things (IoT) and edge computing systems, where billions of devices work with strict resource constraints in processing power, memory and energy. To resolve this, we are introducing a minimal postquantum cryptography model to be used in edge platforms with constraints on their resources. This framework combines fierce encapsulation of key (Kyber512) on a lattice with traditional ECDH in a hybrid system of key establishment, along with the Ascon AEAD encryption algorithm to provide effective data hiding and integrity provision. Furthermore, rekeying and forward secrecy on a lightweight basis is possible to reconfigure a layer with negligible overhead on a secure transmission with forward secrecy. The experimental data with ARM Cortex-M and RISC-V architectures and 128-bit post-quantum security has shown that the proposed system can reduce the latency by up to 30 percent and the energy usage by up to 35 percent and considerably better throughput than PQC-only implementations. These findings validate the fact that hybrid PQC solutions have the potential to provide high-resistance quantum with minimal overhead and thus are best suited to IoT and edge applications. The paper offers a viable and extensible security design in conformance with the NIST PQC and IETF migration policies, which can support secure communication in the post-quantum era.
The emergence of quantum computing seriously threatens the security of legacy cryptographic protocols in consumer electronics devices based on the Internet of Things (IoT). These are essential components of mission-critical infrastructures, such as industrial control systems, smart grids, and healthcare. In particular, this work examines how to strengthen the resistance of the IoT to classical and quantum-based cyberattacks by combining deep learning methods with post-quantum cryptography (PQC). To protect key exchange, encryption, and authentication processes in IoT-consumer devices, we will continue to use quantum-resistant cryptographic algorithms, including lattice-based, hash-based, and code-based cryptography. To provide real-time anomaly detection and response capabilities, we suggest a hybrid security framework combining PQC with deep learning-based intrusion detection systems. Our methodology ensures secure communications and data exchanges linked to quantum attacks in the future within IoT systems while leveraging continuous learning over time to allow Intrusion Detection System (IDS) models to adapt to new attack vectors. We suggest optimized algorithms for PQC solutions that are lightweight and suitable for deployment in edge computing nodes and Internet of Things devices, considering the resource-constrained IoT situations where PQC implementation would be difficult. Through comparative studies and simulations, we demonstrate how our integrated PQC-deep learning framework offers an improved IoT security solution that could safeguard vital systems in a world powered by quantum technology. The results shows the key generation time which is 12.5ms, encryption/decryption time 25.3ms, latency 18.7ms, throughput 5000 operations/sec and energy consumption 2.4mJ.