# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 1 Introduction  
In this section, we discuss the topic's background and the problem formulation. Cloud computing, or simply \"the cloud\", has had a profound impact on the IT industry, since it offers several advantages to end users, such as a reduced need for initial IT capital expenditures, more scalability, lower overall expenses, and so on [1, 2]. However, as more and more devices become interconnected, the issue of significant delay becomes more problematic for latency-sensitive applications. Many companies have seen dramatic changes because of cloud computing [3]. This is particularly true given the rapid increase in the use of enormous data sets. Meanwhile, there has been a meteoric increase in demand for private services. Cloud computing platforms provide various centralized systems, but with significant drawbacks [4, 5]. Inevitable, lengthy, unpredictable delays and time-conscious services are seen with clouds and their endpoints [6]. The stakes are high when there is a disruption in the information infrastructure or network connections. A privacy issue may arise here. In response, the fog computing [7] concept was developed to help improve computation, security, and privacy for Cloud-Edge, which is currently the industry standard. The proliferation of Internet of Things (IoT) devices and the increasing demand for low-latency applications have led to the rise of fog computing as a solution for decentralized data processing. However, trust and security in fog computing environments pose significant challenges. Malicious nodes, unauthorized access, and data breaches can compromise the integrity and reliability of data processing. Therefore, there is a solid motivation to address these gaps in trust management and enhance the overall security of fog computing. Optimizing network performance and reducing latency is crucial to ensuring a seamless user experience in fog computing. Improving the efficiency of data transmission and communication among fog nodes can significantly enhance the performance of fog computing networks [8]. Gateways, routers, switches, and even professionally installed conventional servers may all be considered fog devices [9]. In addition, fog computing is widely regarded as an innovative green platform with sustainability and tremendous security advantages in light of the current requirement for massive emission reduction. Several fog nodes (FNs) are considered renewable in the fog computing system. The sites of FNs might be dispersed throughout a wide area. The multiple FNs can function autonomously, yet in concert thanks to a well-formulated formula, significantly reducing the stress on the data center's infrastructure during computing. Fog computing allows for separating or sifting processing at the central layer between the endpoint and the cloud, which may improve QoS and reduce costs [10]. As we will see in the following subsection, fog computing was widely regarded as desirable to address the developing problems associated with the Internet of Things. Fog computing was the most practical method, since it interconnected all local devices, digital equipment, wireless access points, and the internet. Because of this interdependence, strict security and privacy breaches, such as the exposure of client data storage locations, the disclosure of sensitive information, and the theft of personal accounts, are possible. Cisco first investigated fog computing to extend cloud functionality to the system's peripherals. Fog computing has emerged as a viable alternative to local cloud computing, with significant benefits in QoS, latency, and geographical spread [11]. Fog computing is often regarded as a virtualized system [3], rendering services such as networking, storage, and, most critically, computation between the client and information center, with all the associated risks. Edge computing relies on decentralized, self-operating nodes to ensure data are not sent to a central server, but instead processed locally.