Design of a quality of service‐aware fault‐proof secure Q‐learning‐based Internet of Things kernel patch with multipath and multichannel capabilities

Abstract

Summary In recent times, the expeditious growth of Internet of Things (IoT) offers applications to ease day‐to‐day activities with minimum human effort. Once the IoT application installed, the connected devices perform their tasks without human intervention. Hence, the need of performance optimization and security enhancement is vital to minimize end‐to‐end communication delay, improve kernel‐level security, mitigate faults adaptively, and have suitable backup options in case of node failure. This paper proposes a quality of service (QoS)‐aware fault‐proof secure Q‐learning‐based IoT (QIoT) kernel‐level protocol that integrates multipath aggregation and fuzzy authentication for security, with multichannel communication for improved QoS. Especially, this protocol integrates a source‐level clustering mechanism based on Q‐learning that aims at reducing route search delay. In order to provide fault tolerance, the kernel is equipped with real‐time fault‐tolerance mechanism that is activated in case of node‐level faults. Due to integration of Q‐learning, computational overheads are reduced by over 15% when compared with Zephyr, AliOS, and RTX kernels. This reduction in computational overheads facilitates light‐weight behavior of the kernel, due to which other QoS parameters like energy consumption, throughput, and routing overhead are reduced. The proposed QIoT kernel‐level protocol is compared with standard kernel modules, and performance evaluation showcases an improvement in authentication security by 8%, end‐to‐end delay by 5%, energy efficiency by 25%, and fault mitigation by 18%, thereby assisting the use of the proposed kernel for real‐time deployments.