# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 2 Related Work  
In this section, we cover the existing trusted state-of-the-art fog computing. The system proposed in [22] relies on trust to detect and isolate malicious fog nodes. Fog computing trust management (COMMITMENT) aims to provide a system that leverages previous high-quality service and high-quality protection history measures from prior direct and indirect fog network interactions to assess the level of trust in fog computing nodes (as a consequence). It was possible to detect and decrease 66% of harmful attacks and interactions between fog nodes using the COMITMENT approach while reducing service response time by about 15%. In [8], the authors proposed a secure handoff and routed scheme to protect the nodes from attacks and classify each fog node based on their behaviors. Moreover, the scheme provides a trust management mechanism between IoT and fog layers. A new comprehensive trust management system (GDTMS), which is currently being developed, is described in the article. In [37], the authors suggest a two-way open-to-interpretation logic-based trusted management system that empowers a resource requester to confirm if a provider should provide trustworthily and if the job is correct and allows the service to maintain trust, verifying the legitimacy of the person requesting the service. The remedy can withstand a substantial population of rogue nodes and successfully prevent trust-based attacks. The author's [38] research work identified a comprehensive collection of efficient criteria for highly secured selection in a fog-based computing environment. Furthermore, a good work decision-making technique with fuzzy and excellent worst techniques is used to evaluate the contribution level of every metric on trust level, considering metrics ambiguity. With a value of 0.470, the results indicate that quality of service does have a massive effect on robust security selection. Study [39] shows a trust management system based on fuzzy reputations limited to QoS trust measures. This trust is calculated using data gathered both directly and indirectly. The lack of consideration for the social ties between internet-connected gadgets is the primary limitation of this study. [PERSON] et al. [40] focused on the social connections between IoT devices in defining trust management systems for IoT applications. To determine a node's level of trust, we use a variety of trust indicators, including honesty, cooperation, community of interest (COI), friendship, etc., as well as data gleaned through personal observation and the views of other nodes. The accuracy and convergence of their answers in performing trust assessments are crucial to their evaluation. The emphasis in [41] is on fixing the issue of misbehaving nodes whose traits may evolve. We see a trust administration system that can be expanded, modified, and maintained. According to their method, the trust management system's scalability is ensured by persistently storing trust information for the subset of nodes seen. The authors of [42] suggest a context-aware trust management system for the SiIoT. To successfully distinguish between trustworthy and untrustworthy devices, context-aware QoS determines which of the three trust contexts they operate in. In [43], the authors present a trust assessment approach based on behavior graphs and service groupings that considers identity and other features of relationships, as well as the development of interactions and quality indicators of services such as availability and dependability. In addition to these traditional measures of reliability, trust in the cloud may also be computed using measures of social interactions, such as the degree to which people are honest and sincere. In the health-care industry, the increasing reliance on technology and the proliferation of connected devices have led to the generation of vast amounts of sensitive data. These data include patient health records, diagnostic images, real-time monitoring data, and other critical information.