An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework

Abstract

The term Internet of Things (IoT) refers to a network of connected, intelligent devices that are responsible for the collecting and dissemination of data. Because technology automates the tasks we do daily, our lives have become simpler as a result. However, with a typical architecture for the cloud and the Internet of Things, real-time data processing is not always practicable. This is particularly true for latency-sensitive apps. This eventually resulted in the development of fog computing. On the one hand, the fog layer may perform computations and data processing at the very edge of the network, which enables it to provide results more quickly. On the other hand, this pushes the attack surface closer to the machines themselves, which is a security risk. Because of this, the sensitive data that is stored on the layer is now susceptible to assaults. Therefore, considering the security of the fog-IoT is of the utmost significance. A system or platform's level of security is determined by a number of different elements. When it comes to conducting an accurate risk assessment, the sequence in which these considerations are considered is of the utmost importance. Because of this, determining the level of security offered by fog and IoT devices becomes a Multi-Criteria Decision-Making (MCDM) dilemma. This article presents a two-stage hybrid multi-criteria decision-making model that is based on type-2 neutrosophic numbers (T2NNs). The goal of this article is to give scientists and practitioners a decision-making tool that is both easy and versatile. The initial step of this process is determining the weights of criteria by the AHP method in the T2NN environment. Second, the T2NN-based Multi-Attributive Border Approximation area Comparison (MABAC) method is used to rank the various fog security based on IoT. Both of these methods are described in more detail below. With the help of the comparison study, the high reliability and robustness of the combined AHP and MABAC based type-2 neutrosophic model have been proven.