A Robust Secure and Energy-Aware Cross-Layer Framework for IoT Networks
Date
Authors
Mustafa, Rashid
Sarkar, Nurul I
Mohaghegh, Mahsa
Pervez, Shahbaz
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The dual challenges of energy constraints and multi-layered cyber threats must be addressed in order to secure Internet of Things (IoT) environments. To overcome the above problems, we propose a secure and energy-aware cross layer framework for IoT networks. Our framework is based on the combined role-based access control, machine learning based anomaly detection, and lightweight encryption. We explore context-aware defenses that can remain scalable and energy efficient while dynamically adapting to changing attack vectors. The performance of the proposed framework is evaluated using real hardware (Z1 and EXP430F5438 motes) after being validated by simulations on the Cooja and NS-3 platforms. The results demonstrate up to 30% energy savings over AES while preserving high detection performance for both active and passive threat models and over 95% packet delivery. These results highlight the necessity of adaptive, multi-layer strategies for contemporary IoT deployments and show that a secure, scalable, and energy conscious IoT design is feasible.Description
Keywords
Cross-layer framework, energy-aware, IoT network, lightweight encryption, machine learning
Source
Proceedings of The 40th International Conference on Information Networking (ICOIN 2026), January 14-16, 2026. Hanoi, Vietnam. ISBN : 979-8-3315-7896-1
DOI
Publisher's version
Rights statement
This is the Author's Accepted Manuscript of a conference paper presented at ICOIN, 2026. The Version of Record is © IEEE, and available at https://icoin.org/program_proceeding