# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 1 Introduction
Trust administration involves a trustor and a trustee, and the trustor is the believing party or the party with confidence in the trustee. When people put their faith in another person or organization (called \"trustees\"), it is an example of trust in competence and dependability [26]. The directionality of trust means that the reliability of the trustee does not indicate the reliability of the trustor. What is reliable for one organization may not be reliable for another [27]. Objects in a network may assess the reliability of each other with the help of trust management. To rephrase, it allows one to determine whether or not to have trust in the party with whom contact is being made. It permits independent communication between elements in a network and enables the identification of damaged or misbehaving nodes [28]. Building rapport in a precarious setting requires trust [29]. The credibility of its providers guarantees the safety of sensitive data. Information security and user privacy can only be maintained if users have faith in the service they receive [28]. Trust is also linked to other positive characteristics, such as dependability, honesty, and the capacity to meet specific needs. This study presents a two-way trust management (TTM) system grounded in subjective logic. The trustor and the trustee take turns evaluating each other and computing trust to establish reliable channels of information exchange. The ultimate trust value is calculated using both the direct trust attained via one's observation and the indirect trust attained through the suggestions of nearby nodes. Trust computation allows the trustworthiness of a target entity to be computed on the fly. Five aspects of design go into creating a reliable computing method: trust composition, trust propagation, trust aggregation, trust update, and trust creation [30]. The nature of the trust will dictate what data are needed to compute trust, quality of service, and/or social-relationship data [24]. When someone trusts a service provider, they are said to have \"QoS trust\" [31]. It may be employed for trust computation, since people's social connections are mirrored in their technology [32]. Trust propagation determines the extent to which trust values are saved and computed. It has the option of being either centralized or decentralized. The trust update setting controls the frequency with which entity trust values are refreshed. Trusts may be changed in response to events or the passage of time. Trust composition identifies which characteristics of a trust should be included, while trust creation explains how to bring those characteristics together. Any number of assets may be placed in a trust. Trust aggregation involves choosing how to combine evidence of trust from various recommenders and one's experience. Trust aggregation strategies include the likes of weighted sum, Bayesian inference, fuzzy logic, subjective logic, and regression analysis. The proposed subjective logic-based bidirectional trust management system considers both quality-of-service and social trust data to determine the trustworthiness of fog nodes. However, trust management is challenging to enforce in a fog environment because of the fluidity with which fog nodes may be deployed. Fog nodes can come from various sources, with each provider owning, operating, and maintaining their own set of nodes. The nodes are vulnerable to corruption and rogue node construction because of their widespread geo-distribution and proximity to end users. Trust management in fog computing is complicated by its lack of redundancy, dynamics, high mobility support, and low processing power of nodes [33]. As a result, fog servers pose a risk to fog clients and other fog servers from the point of view of the fog's clientele. To reduce latency, increase dependability, and reduce bandwidth, fog computing is a concept for dispersed computing that pushes processing, storage, networking, and communication services to the edge of a network [34].