An Efficient Swift Routing Model with Node Trust Identity Factor (SRM-NTIF) to Perform Secure Data Transmission Among IoT Gadgets

Abstract

According to a United Nations survey, the number of users using the internet has increased to 3 billion in recent years. The Auto-ID Center is a research organisation that coined the word "Internet of Things" (IoT) a decade ago, describing how it utilises wired or wireless networking technologies to create a channel of communication among technologies and networks available over the Internet. Despite the fact that a swing of routing protocols has been proposed in the literature, safe and energy-efficient routing protocol is still a work in progress. Many routing protocols expressly designed for resource limited wireless devices take the same approach and have nearly achieved their full improvements. The Internet of Things (IoT) has recently gained prominence as a result of the increasing number of connected devices being used in everyday human life with network lifetime constraints. Routing expertise is essential for establishing communication between nodes. A node should be capable of self-learning, self-configuring, and self-managing by gathering local knowledge and sharing it with its neighbours. The degree of trust determines the degree of cooperation between scattered mobile nodes. The term "trust" refers to a level of assurance based on node behavior. To ensure secure and proper data transmission in IoT network, the trust level of the nodes is calculated based on node behaviour. Because of the unexpected changes in the network structure, the complex existence of IoT network, and the underived prior trust relationship between the nodes, trust computation in IoT network is a difficult task. All IoT nodes willing to engage in data transmission are given a Digital Unique Identifier (DUI), and the proposed model must define their trust identity factors. Using the DUI, the proposed Swift Routing Model with Node Trust Identity Factor (SRM-NTIF) model, node authentication is performed to verify natural and malicious nodes in the network. The proposed model is compared with the traditional methods and the results show that the proposed model performance is better in security and trust levels.