Abstract
IoT devices rely on authentication mechanisms to render secure message exchange. During data transmission, scalability, data integrity, and processing time have been considered challenging aspects for a system con-stituted by IoT devices. The application of physical unclonable functions (PUFs) ensures secure data transmission among the internet of things (IoT) devices in a simplified network with an efficient time-stamped agreement. This paper proposes a secure, lightweight, cost-efficient reinforcement machine learning framework (SLCR-MLF) to achieve decentralization and security, thus enabling scalability, data integrity, and optimized processing time in IoT devices. PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication. An IoT network gathers information of interest from multiple cluster members selected by the proposed framework. In addition, the software-defined secured (SDS) technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform. Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network's lifetime, energy, secured data retrieval rate, and performance ratio. By enabling the proposed framework, number of residual nodes is reduced to 16%, energy consumption is reduced by up to 50%, almost 30% improvement in data retrieval rate, and network lifetime is improved by up to 1000 msec.