QERS: Quantum Encryption Resilience Score for Post-Quantum Cryptography in Computer, IoT, and IIoT Systems

TL;DR

The study addresses the lack of holistic evaluation tools for post-quantum cryptography in constrained IoT/IIoT environments. It introduces QERS, a universal resilience score that aggregates latency, packet loss, RSSI, energy, key size, CPU usage, and cryptographic overhead via a multi-criteria decision model, with normalization and weighting leading to Basic, Tuned, and Fusion scoring modes. Formulas such as $QERS_{basic} = MS - ( \alpha L_{norm} + \beta O_{norm} + \gamma P_{loss,norm})$ and $QERS_{fusion} = \alpha (MS - P) + \beta S$ operationalize the approach, while an ESP32-based implementation evaluates five PQC algorithms (Kyber, Dilithium, Falcon, SPHINCS+, NTRU) under realistic wireless conditions. The framework enables reproducible PQC readiness assessments and migration planning in resource-limited deployments, with a Random Forest fusion layer aiding resilience to incomplete data and dynamic environments.

Abstract

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.

Figures (8)

• Figure 1: Conceptual QERS framework showing metrics, normalization, score variants, and readiness interpretation.
• Figure 2: Proposed QERS framework extended with ESP32-C6 PQC device, Raspberry Pi CM4 QERS server, and close-range and far-range wireless measurement contexts.
• Figure 3: Scenario 1: QERS multi-metric heatmap (normalized 0–100).
• Figure 4: Scenario 2: QERS multi-metric heatmap (normalized 0–100).
• Figure 5: Scenario 1: Distribution of normalized QERS scores.