The fast growth of the Internet of Things (IoT) has enabled large-scale interconnection of smart devices across domains such as healthcare, smart homes, industrial automation, and intelligent transportation systems. However, ensuring secure communication in IoT environments is still a major problem because of limited computational capability, memory capacity, and energy resources of IoT devices. Conventional cryptographic algorithms provide strong security but are often unsuitable for resource-constrained platforms because of their high computational and energy requirements. Lightweight cryptography offers an efficient alternative; nevertheless, existing schemes frequently rely on static configurations and lack adaptability to dynamic network conditions and noise-prone wireless environments. This paper proposes an adaptive and energy-efficient security framework that integrates lightweight cryptography with Kalman Filtering (KF) and a Genetic Algorithm (GA). The KF is used to reduce communication noise and enhance data reliability through real-time state estimation, while the Genetic Algorithm dynamically optimizes cryptographic parameters such as key size and structural configurations to balance security strength and resource utilization. By combining noise reduction, adaptive parameter tuning, and lightweight encryption combined in a single system design, the proposed approach enhances communication reliability, security robustness, and energy efficiency. The framework is designed to meet the practical requirements of real-world IoT deployments and demonstrates strong potential for secure and scalable IoT communication systems.
Rajni et al. (Sun,) studied this question.