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Hybrid Framework for Secure Low-Power Data Encryption with Adaptive Payload Compression in Resource-Constrained IoT

You-Rak Choi¹, Hwa-Young Jeong², Sangook Moon³

¹Nuclear System Integrity Sensing and Diagnosis Division, Korea Atomic Energy Research Institute (KAERI), Daejeon 34057, Republic of Korea.

Sensors (Basel, Switzerland)

|April 14, 2026

Summary

This study introduces a hybrid framework for resource-constrained IoT systems, balancing security and energy efficiency. It achieves significant data compression and enhanced cryptographic security, enabling 14-year autonomous operation for critical infrastructure monitoring.

Keywords:

IoT security acoustic leak detection adaptive compression authenticated encryption critical infrastructure monitoring energy-efficient cryptography hardware acceleration wireless sensor networks

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Area of Science:

Internet of Things (IoT) Security
Embedded Systems Energy Efficiency
Signal Processing for Sensor Networks

Background:

Resource-constrained IoT systems require both strong security and long operational life, especially for critical infrastructure monitoring.
A conflict exists between cryptographic security needs and energy efficiency in these devices.
Existing solutions often fail to adequately address both security and power consumption for long-term autonomous deployments.

Purpose of the Study:

To develop a hybrid framework that integrates signal-adaptive compression with hardware-accelerated encryption.
To resolve the trade-off between cryptographic security and energy efficiency in IoT systems.
To enhance the security and extend the operational lifetime of IoT devices for critical infrastructure monitoring.

Main Methods:

Implemented a Dynamic Payload Compression with Selective Encryption (DPCSE) framework.
Classified sensor data into three Signal-to-Noise Ratio (SNR) regimes for adaptive compression.
Integrated adaptive compression with hardware-accelerated Advanced Encryption Standard (AES) on an STM32L5 microcontroller.

Main Results:

Achieved an average payload reduction of 5.91-fold across 2714 acoustic sensor samples, with 100% detection accuracy.
Reduced power-data correlation from 0.820 to 0.041, significantly increasing resistance to differential power analysis attacks.
Extended projected operational lifetime to 14 years on standard batteries, exceeding industrial requirements.

Conclusions:

The DPCSE framework effectively balances security and energy efficiency in resource-constrained IoT systems.
Hardware-accelerated encryption combined with adaptive compression provides robust security against side-channel attacks.
The proposed system offers a viable solution for long-term, secure, and autonomous critical infrastructure monitoring.