What question did this study set out to answer?

The aim is to develop a framework that optimizes energy consumption, security, and latency in IoT applications.

June 10, 2026Open Access

Energy-Efficient Cryptographic Protocols for Sustainable IoT Security: A Federated Learning-Enhanced Lightweight Framework with Post-Quantum Resilience

Key Points

The aim is to develop a framework that optimizes energy consumption, security, and latency in IoT applications.
Proposed an Energy-Efficient Cryptographic Protocol Framework (EECPF) combining several cryptographic strategies.
Implemented a privacy-preserving federated learning approach for intrusion detection.
Assessed the framework using the Edge-IIoTset dataset under various IoT attack scenarios.
Achieved an intrusion detection rate of 94.7%.
Reduced energy consumption by 47.3% compared to other lightweight methods.
Lowered latency by 23.8% under the same conditions.

Abstract

The increasing pace of Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications has exacerbated the security challenges in resource-constrained environments, where traditional cryptographic protocols incur prohibitively high computational and energy costs. These constraints are also worsened by the advent of quantum computing, which poses a long-term security risk to popular crypto-key cryptographic-based efforts. To overcome these difficulties, this paper proposes an Energy-Efficient Cryptographic Protocol Framework (EECPF) that provides mutual optimization between energy consumption, security level, and communication latency to achieve sustainable IoT security. The presented framework proposes an adaptive encryption selection mechanism that dynamically chooses cryptographic algorithms depending on device capabilities, network conditions, and threat levels derived from intrusion detection outputs. EECPF combines privacy-preserving federated learning for distributed intrusion detection with collaborative threat intelligence sharing, eliminating centralized data sharing. In addition, lattice-based post-quantum cryptography primitives are added and combined with lightweight blockchain-enforced identity management to ensure long-term authentication resilience. The models on which the framework is based are mathematically based, modeling the consumption of energy, the robustness of security, and latency, providing principled multi-objective optimization under resource constraints. The publicly available Edge-IIoTset dataset was subjected to extensive experimental assessment under realistic IIoT and IoT attack scenarios. Experiments show that EECPF can reach an intrusion detection rate of 94.7%, while reducing energy consumption by 47.3% and latency by 23.8% compared with other commonly used lightweight cryptographic methods. These were continually noticed across different heterogeneous devices and deployment environments. In general, EECPF offers an energy-aware, quantum-resilient, and scalable security solution that can be used for next-generation IoT systems, such as smart healthcare, industrial automation, and smart city infrastructures.

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