Evaluating Lightweight and Post-Quantum Encryption for Compressed Image Transmission Over LoRa in the Era of Edge AI

LoRa (Long Range) is widely adopted for low-power, long-range Internet of Things applications, yet its strict payload and duty-cycle constraints pose challenges for secure data transmission. With the growing integration of Edge AI for on-device inference and real-time decision-making, these constraints become even more critical, as intelligent processing at the edge often requires transmitting compressed and encrypted data efficiently. This study investigates the combined impact of data compression and encryption on the efficiency, energy consumption, and reliability of image transmission at the LoRa physical layer. A Variational Autoencoder was employed to compress high-resolution environmental image into compact binary representations suitable for LoRa transmission. The compressed data were evaluated under three configurations: compression only, compression with lightweight encryption using ChaCha20, and compression with hybrid post-quantum security using CRYSTALS-Kyber key encapsulation combined with ChaCha20 encryption.Experiments were conducted using real LoRa hardware under controlled laboratory conditions, with each configuration repeated 100 times. Results show that lightweight encryption introduces negligible overhead in airtime and energy consumption, maintaining performance comparable to the unencrypted baseline. In contrast, the post-quantum configuration incurs substantial airtime and transmission energy overhead due to key encapsulation ciphertexts, increasing fragmentation and occasionally affecting reliability.These findings demonstrate that compression enables security-ready LoRa transmissions while highlighting the current limitations of post-quantum cryptography for ultra-low-power LoRa deployments.