Lightweight Encryption For Iot Devices Using Fog Computing
- DOI
- 10.2991/978-94-6239-616-6_93How to use a DOI?
- Keywords
- IoT; Fog Computing; Precision Management; Ascon; Lightweight Cryptography; Flask Microservices; Secure Data Transmission; Authenticated Encryption
- Abstract
The rapid growth of IoT in precision management, particularly in agriculture and environmental monitoring, demands secure, efficient data processing at the fog layer. Ensuring confidentiality, integrity, and authenticity in resource-limited environments is challenging. This work proposes a lightweight, high-security encryption method for Fog-IoPM microservices using the NIST-recommended Ascon algorithm. Integrated into a Flask-based microservice architecture, Ascon enables secure encryption of sensor data at the fog layer before storage, processing, or cloud transmission. Unlike resource-heavy AES, Ascon delivers optimized performance with low energy consumption and strong resistance to side-channel and differential attacks. Its authenticated encryption ensures tamper-resistant payloads and improved system reliability. The framework supports scalability for large-scale IoT networks while maintaining low overhead. By combining strong security with lightweight execution, it enhances performance and protection in modern IoT-based precision management.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - R. Anandkumar AU - M. Saranya AU - D. Shree Harini AU - P. Nithya PY - 2026 DA - 2026/03/31 TI - Lightweight Encryption For Iot Devices Using Fog Computing BT - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025) PB - Atlantis Press SP - 1273 EP - 1284 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6239-616-6_93 DO - 10.2991/978-94-6239-616-6_93 ID - Anandkumar2026 ER -