Survey on Energy-Aware Adaptive Key Generation using Genetic Algorithm and Chaotic Maps for IoT Edge Devices

DOI

10.2991/978-94-6239-616-6_94How to use a DOI?

Keywords

IoT Security; Lightweight Cryptography; Chaotic Maps; Genetic Algorithm; Energy-Aware Computing; Edge Devices

Abstract

The rapid expansion of Internet of Things (IoT) applications in healthcare, smart homes, and industrial systems demands cryptographic solutions that are both secure and energy-efficient. Conventional algorithms such as RSA and AES provide strong security but introduce significant computational and power overhead for constrained edge devices. This survey reviews recent (2020–2025) key-generation techniques that employ chaotic maps and Genetic Algorithms (GAs) to enhance randomness while reducing resource usage. Methods are analyzed in terms of design principles, energy implications, security performance, and practical limitations. The review highlights persistent challenges including the absence of real-time energy adaptivity, minimal hardware validation, and inconsistent benchmarking across studies. Finally, the survey identifies key research gaps and outlines future directions toward achieving adaptive, lightweight, and sustainable key-generation mechanisms for IoT edge environments.

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.

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TY  - CONF
AU  - T. Periyasamy
AU  - S. Nandhini
AU  - B. I. Gomugie
AU  - P. Dharani
AU  - M. Harini
PY  - 2026
DA  - 2026/03/31
TI  - Survey on Energy-Aware Adaptive Key Generation using Genetic Algorithm and Chaotic Maps for IoT Edge Devices
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1285
EP  - 1297
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_94
DO  - 10.2991/978-94-6239-616-6_94
ID  - Periyasamy2026
ER  -