Internet of Things (IoT) networks are designed to obtain the best possible end-user experience. This experience is measured in terms of security and speed of operation. In order to achieve high security, encryption, and authentication algorithms are proposed by researchers. The speed of operation is generally measured using the network's response time, and it can be improved using light-weighted algorithms for data processing and actuation. Most of these techniques are applied at the application level, making the network ineffective in its originally deployed form. Moreover, due to the addition of security and data processing layers, the overall IoT network's QoS reduces, reducing the end user's experience. In order to remove these issues, this work proposes a novel kernel-level multi-channel fuzzy-authentication and aggregation framework (KMFA2). The KMFA2 framework can solve authentication issues by using a kernel-level lightweight and secure authentication protocol for all IoT devices. KMFA2 also enables the IoT devices to possess multi-channel capabilities, with inbuilt aggregation capabilities. The overall implementation is carried out on the NS2 tool.The proposed KMFA2 protocol is tested on different IoT networks, and it is observed that the protocol improves the QoS by almost 10% and simplifies security using the authentication framework. The comparisons were performed with state-of-the-art IoT deployments.

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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.

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