The transition to post-quantum cryptography (PQC) presents significant challenges for modern computing environments due to increased computational overhead, communication latency, and implementation complexity. Existing evaluation methods typically focus on isolated cryptographic performance metrics and do not provide a unified framework for assessing the overall resilience of PQC deployments across heterogeneous systems. This paper introduces the Quantum Encryption Resilience Score (QERS), a novel system-level evaluation framework designed to quantify the resilience of post-quantum cryptographic implementations by integrating computational, network, and operational performance metrics into a single composite score. QERS provides a standardized methodology for comparing PQC algorithms across diverse deployment scenarios. In this study, the framework is experimentally evaluated using a heterogeneous ESP32-based embedded and IoT testbed with gateway-assisted post-quantum cryptographic processing. The proposed framework is validated through experimental implementations of the NIST-standardized ML-KEM key encapsulation mechanism and the ML-DSA digital signature algorithm under multiple communication protocols and heterogeneous hardware configurations. Experimental results demonstrate that QERS effectively distinguishes the trade-offs between security, computational efficiency, resource utilization, and communication performance, providing a practical decision-support framework for selecting appropriate PQC implementations. The proposed framework contributes a reproducible and extensible methodology for the system-level evaluation of post-quantum cryptography and establishes a foundation for future research into standardized resilience and trust assessment of quantum-resistant systems.
The transition to post-quantum cryptography (PQC) presents significant challenges for modern computing environments due to increased computational overhead, communication latency, and implementation complexity. Existing evaluation methods typically focus on isolated cryptographic performance metrics and do not provide a unified framework for assessing the overall resilience of PQC deployments across heterogeneous systems. This paper introduces the Quantum Encryption Resilience Score (QERS), a novel system-level evaluation framework designed to quantify the resilience of post-quantum cryptographic implementations by integrating computational, network, and operational performance metrics into a single composite score. QERS provides a standardized methodology for comparing PQC algorithms across diverse deployment scenarios. In this study, the framework is experimentally evaluated using a heterogeneous ESP32-based embedded and IoT testbed with gateway-assisted post-quantum cryptographic processing. The proposed framework is validated through experimental implementations of the NIST-standardized ML-KEM key encapsulation mechanism and the ML-DSA digital signature algorithm under multiple communication protocols and heterogeneous hardware configurations. Experimental results demonstrate that QERS effectively distinguishes the trade-offs between security, computational efficiency, resource utilization, and communication performance, providing a practical decision-support framework for selecting appropriate PQC implementations. The proposed framework contributes a reproducible and extensible methodology for the system-level evaluation of post-quantum cryptography and establishes a foundation for future research into standardized resilience and trust assessment of quantum-resistant systems.
Public-key cryptographic algorithms such as RSA and Elliptic Curve Cryptography (ECC) may become vulnerable once large-scale quantum computersand quantum chips become practical for real-world infrastructure and commercial deployment. To address this risk, Post-Quantum Cryptography (PQC)introduces cryptographic methods designed to resist quantum-based attacks.However, these mechanisms often require more processing power, bandwidth,and energy, which creates challenges for constrained IoT and Industrial IoT(IIoT) devices.This thesis introduces the Quantum Encryption Resilience Score (QERS), asystem-level evaluation framework designed to quantify the operational impactof post-quantum cryptographic deployment across embedded systems and networked environments. Instead of concentrating only on cryptographic accuracy,QERS integrates multiple performance dimensions, including latency, CPUutilization, energy consumption, signal strength (RSSI), and cryptographicoverhead into a unified and normalized score. A Design Science Research methodology is employed to design, implement, and evaluate the QERS frameworkusing four heterogeneous ESP-based microcontroller platforms. Experimentalevaluation is conducted using HTTP and HTTPS communication over Wi-Fi,incorporating post-quantum cryptographic configurations based on Kyber andML-DSA.The results demonstrate that post-quantum cryptographic mechanisms introduce measurable latency amplification and variability across devices, with cleardifferences in performance and stability depending on hardware capability.QERS enables systematic, cross device comparison of these effects, revealingcritical trade-offs between cryptographic strength and operational performance.The findings confirm that latency is the dominant factor influencing systemresilience, while CPU and energy contribute secondary effects. By integratingcryptographic, system, and network-level metrics into a single evaluative construct, this work provides a structured and reproducible approach for assessingresilience in post-quantum secure embedded and IoT infrastructures.
Post-quantum cryptography (PQC) is becoming essential for securing Internet of Things (IoT) and Industrial IoT (IIoT) systems against quantum-enabled adversaries. However, existing evaluation approaches primarily focus on isolated performance metrics, offering limited support for holistic security and deployment decisions. This paper introduces QERS (Quantum Encryption Resilience Score), a universal measurement framework that integrates cryptographic performance, system constraints, and multi-criteria decision analysis to assess PQC readiness in computer, IoT, and IIoT environments. QERS combines normalized metrics, weighted aggregation, and machine learning-assisted analysis to produce interpretable resilience scores across heterogeneous devices and communication protocols. Experimental results demonstrate how the framework enables comparative evaluation of post-quantum schemes under realistic resource constraints, supporting informed security design and migration planning. This work is presented as a preprint, with extended statistical validation planned as part of ongoing graduate research.
Post-quantum cryptography (PQC) introduces significant computational and communication overhead, which poses challenges for resource-constrained computer systems, Internet of Things (IoT), and Industrial IoT (IIoT) devices. This paper presents an experimental evaluation of the Quantum Encryption Resilience Score (QERS) applied to MQTT, HTTP, and HTTPS communication protocols operating under PQC. Using an ESP32-C6 client and an ARM-based Raspberry Pi CM4 server, latency, CPU utilization, RSSI, energy consumption, key size, and TLS handshake overhead are measured under realistic operating conditions. QERS integrates these heterogeneous metrics into normalized Basic, Tuned, and Fusion scores, enabling systematic comparison of protocol efficiency and security resilience. Experimental results show that MQTT provides the highest efficiency under PQC constraints, while HTTPS achieves the highest security-weighted resilience at the cost of increased latency and resource consumption. The proposed framework supports informed protocol selection and migration planning for PQC-enabled IoT and IIoT deployments.
6G networks will require quantum-secure cryptography deployed across core infrastructure, edge nodes, resource-constrained IoT devices. Although post-quantum cryptographic (PQC) algorithms have been standardized by NIST, their practical deployability in bandwidth and latency limited wireless systems remains unclear. This paper presents a practical evaluation of NIST selected PQC schemes, including ML-KEM (Kyber), ML-DSA (Dilithium), and Falcon. Benchmarks conducted with OpenSSL and the OQS provider on heterogeneous platforms show that while computational performance is acceptable, ciphertext and signature size expansion significantly impact handshake reliability and bandwidth efficiency, particularly at the network edge. The results highlight key system-level trade-offs and motivate the need for PQC optimization and deployment-aware design for future quantum-secure 6G networks.
The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by the U.S. National Institute of Standards and Technology (NIST) and global security stakeholders. While theoretical security analysis of these quantum-resistant algorithms has advanced considerably, comprehensive real-world performance benchmarks spanning diverse computing environments—from high-performance cloud infrastructure to severely resource-constrained IoT devices—remain insufficient for informed deployment planning. This paper presents the most extensive cross-platform empirical evaluation to date of NIST-selected PQC algorithms, including CRYSTALS-Kyber and NTRU for key encapsulation mechanisms (KEMs), alongside BIKE as a code-based alternative, and CRYSTALS-Dilithium and Falcon for digital signatures. Our systematic benchmarking framework measures computational latency, memory utilization, key sizes, and protocol overhead across multiple security levels (NIST Levels 1, 3, and 5) in three distinct hardware environments and various network conditions. Results demonstrate that contemporary server architectures can implement these algorithms with negligible performance impact (<5% additional latency), making immediate adoption feasible for cloud services. In contrast, resource-constrained devices experience more significant overhead, with computational demands varying by up to 12× between algorithms at equivalent security levels, highlighting the importance of algorithm selection for edge deployments. Beyond standalone algorithm performance, we analyze integration challenges within existing security protocols, revealing that naive implementation of PQC in TLS 1.3 can increase handshake size by up to 7× compared to classical approaches. To address this, we propose and evaluate three optimization strategies that reduce bandwidth requirements by 40–60% without compromising security guarantees. Our investigation further encompasses memory-constrained implementation techniques, side-channel resistance measures, and hybrid classical-quantum approaches for transitional deployments. Based on these comprehensive findings, we present a risk-based migration framework and algorithm selection guidelines tailored to specific use cases, including financial transactions, secure firmware updates, vehicle-to-infrastructure communications, and IoT fleet management. This practical roadmap enables organizations to strategically prioritize systems for quantum-resistant upgrades based on data sensitivity, resource constraints, and technical feasibility. Our results conclusively demonstrate that PQC is deployment-ready for most applications, provided that implementations are carefully optimized for the specific performance characteristics and security requirements of target environments. We also identify several remaining research challenges for the community, including further optimization for ultra-constrained devices, standardization of hybrid schemes, and hardware acceleration opportunities.
Post-quantum cryptography (PQC) is a critical area of research aimed at addressing the threat quantum computing poses to traditional cryptographic systems. It focuses on evaluating the efficiency, security, and practical performance of PQC algorithms through experimental analysis. This research supports the development of optimized cryptographic solutions, contributes to standardization efforts, and ensures secure and efficient implementations for a wide range of applications. Despite progress in PQC, gaps still exist in practical performance evaluations, particularly in resource-constrained environments. There is a lack of standardized comparisons, hindering the selection of suitable algorithms for specific use cases like IoT and cloud systems. Furthermore, research on scalability, integration challenges, and the trade-offs between security and efficiency remains insufficient. The methodology involves creating a comprehensive framework to evaluate various PQC algorithms, including lattice-based (e.g., Kyber, Dilithium), code-based (e.g., McEliece), and hash-based (e.g., SPHINCS+). These algorithms will be tested in diverse environments, from resource-constrained devices to high-performance infrastructures such as cloud platforms. Key metrics such as encryption/decryption speed, key sizes, memory usage, and computational efficiency will be measured, focusing on the trade-offs between security, efficiency, and scalability. The results will be benchmarked against standardized metrics to provide a clear understanding of each algorithm's suitability for real-world deployment. The findings indicate that lattice-based algorithms like Kyber offer a strong balance between security and efficiency but require considerable computational resources. Code-based algorithms like McEliece are highly secure but come with large key sizes and slower speeds. Hash-based schemes like SPHINCS+ provide strong security but are computationally expensive, making them less suitable for resource-limited systems. The highlights the importance of optimizing PQC algorithms according to specific application requirements, where the choice of algorithm must balance security, efficiency, and resource constraints, with continuous optimization needed to address evolving real-world demands.