Research Article
Pre-Authenticated ML-KEM Key Banking with Exposure-Bounded SM4-GCM Rekeying for Real-Time Industrial IoT
Bakhtiyor Abdurakhimov
,
Javokhir Abdurazzokov*
Issue:
Volume 10, Issue 2, December 2026
Pages:
35-46
Received:
20 September 2026
Accepted:
5 October 2026
Published:
23 October 2026
Abstract: Industrial Internet of Things (IIoT) systems require cryptographic migration toward post-quantum key establishment without violating the tight timing constraints of sensing, control, and gateway communication. This paper proposes (Pre-Authenticated Rekey Key-Bank with Real-Time Exposure Control), a hybrid key-management architecture that combines NIST-standardized ML-KEM with an SM4-GCM authenticated data plane. The central idea is to move ML-KEM key generation and public-key delivery out of the latency-critical rekey interval. During low-utilization periods, a device creates a rolling bank of application-enforced single-activation ML-KEM public keys, commits the bank with a Merkle root, and authenticates that root once with ML-DSA. During online rekeying, the peer uses a previously authenticated bank slot, transmits only the ML-KEM ciphertext and compact context, and completes explicit key confirmation before activating the new SM4-GCM epoch. A hard exposure envelope based on key age, message count, protected-byte volume, and security risk prevents indefinite postponement, while a soft pressure function and reserve-aware scheduler place preparation work into available real-time slack. For ML-KEM-768, pre-positioning the 1184-byte encapsulation key reduces the core KEM material on the online path from 2272 bytes to 1088 bytes, an exact analytical reduction of 52.11%. A link-rate model shows that the removed public-key transfer is most valuable on constrained links, while the principal cost is protected storage for unused decapsulation keys. The paper provides protocol state rules, nonce construction, security propositions, analytical communication and memory results, and a reproducible MQTT/OPC UA hardware-validation protocol. The construction does not modify ML-KEM, ML-DSA, or SM4; its contribution is the lifecycle and scheduling architecture that integrates standardized primitives with real-time IIoT constraints.
Abstract: Industrial Internet of Things (IIoT) systems require cryptographic migration toward post-quantum key establishment without violating the tight timing constraints of sensing, control, and gateway communication. This paper proposes (Pre-Authenticated Rekey Key-Bank with Real-Time Exposure Control), a hybrid key-management architecture that combines N...
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Research Article
Adaptive Hybrid MPPT with Tabular Reinforcement Learning for Adaptive PI Gain Tuning in a PV-Fed
Buck–Boost Converter
Olusesan Orebanjo*,
Stephen Nwachukwu Ukagu,
Dennis Okwuchukwu Okonkwo,
Osamudiamen Nelson Amasowomwan,
Ikenna Augustine Onyegbadue,
Dedacus Nnadozie Ohaegbuchi,
Lucky Olatunde Odejimi
Issue:
Volume 10, Issue 2, December 2026
Pages:
47-64
Received:
24 September 2026
Accepted:
8 October 2026
Published:
28 October 2026
Abstract: Maximum power point tracking (MPPT) is essential for efficient photovoltaic (PV) operation under varying irradiance and temperature. Conventional methods such as Incremental Conductance (INC) remain attractive for their simplicity, yet fixed proportional–integral (PI) gains limit dynamic performance when environmental conditions change rapidly. Under partial shading the power–voltage characteristic can also contain multiple local maxima, making reliable global tracking more difficult. This paper proposes a hybrid MPPT architecture in which a tabular Q-learning agent adaptively tunes the PI gains of a PV-fed buck–boost converter while INC retains responsibility for generating the operating-point reference. The learning state is defined from PV voltage, power and their increments; actions are bounded gain steps constrained by small-signal stability analysis; and the reward balances power improvement against excessive gain movement. Because the learning agent only supervises the inner-loop gains rather than replacing the entire control law, the design preserves the interpretability of classical INC while introducing operating-condition-dependent adaptation. The controller is evaluated in simulation against fixed-gain INC and direct-duty-cycle Q-learning under six irradiance profiles, with additional temperature, partial-shading, ablation and hyperparameter-sensitivity tests. Results demonstrate that the hybrid architecture achieves competitive tracking performance while maintaining adaptive PI gains within stability bounds. Ablation confirms that joint adaptation of both gains outperforms single-gain cases, and the sensitivity study identifies a robust hyper-parameter region. The study confirms the feasibility of lightweight, hierarchical reinforcement learning for adaptive PI regulation in PV converters and provides a practical framework for online simulation-based gain tuning under dynamic environmental conditions.
Abstract: Maximum power point tracking (MPPT) is essential for efficient photovoltaic (PV) operation under varying irradiance and temperature. Conventional methods such as Incremental Conductance (INC) remain attractive for their simplicity, yet fixed proportional–integral (PI) gains limit dynamic performance when environmental conditions change rapidly. Und...
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