# Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach
## 2 Related Work
### Fog-Based Centralized Trust Models  
Several studies use the centralized approach such as [27; 28; 29; 33]. [PERSON] et al. [27] use a central entity called a Fog broker to maintain and compute the trust level of the participating nodes in a Fog computing environment. A fuzzy logic-based method evaluates the trust level of a particular node. It is calculated based on QoS, availability, security considerations, user feedback, and cost. The availability parameter of the node is used in the evaluation process to provide end-users with an available trustworthy Fog that satisfies their requirements. [PERSON] et al. [28] propose a trust management system for Fog-enabled cyber-physical systems. The direct trust level of Fog nodes is assessed using QoS and network-related parameters. In this study, trust computation is formulated as a statistical regression problem. Subsequently, it is solved using random forest regression. They conclude that the credibility and multi-factor trust evaluations enable precise and accurate trust computation and guarantee a trustworthy Fog-enabled CPS system.  
[PERSON] et al. [33] developed a trust model using fuzzy neural networks and the weighted weakest link (WWL) to evaluate the trustworthiness of Fog nodes. After passively classifying all Fog nodes using a neuro-fuzzy classifier and the WWL algorithms, the results are sent to a central entity called the Fog trust server. The used model enables the end-user to select the most trusted node upon requesting a service of interest. The information of all nodes needs to be collected and processed offline. [PERSON] et al. [34] propose a framework based on role-based access control and trust model for the Fog computing environment. In this study, a new Fog node is assigned a trivial task, and the model computes a node's trustworthiness based on the allocated task's satisfaction level. Once the trusted Fog nodes are discovered, they are given more access privileges and roles. The overall trust level of a Fog node is computed based on the composition of different trust attributes such as reliability, availability, turnaround efficiency, and data integrity.  
[PERSON] et al. [29] propose a three-tier trust model, cloud-cloudlet-device, based on social relationships among IoT devices owners. The proposed model employs a cloudlet as an intermediary layer for maintaining trust data. The calculation of the trustworthiness of all involved IoT devices is performed by the cloud in the region of cloudlets. [PERSON] et al. [35] propose a trust model to enhance IoT security based on subjective logic. The proposed system computes the trust level for each Fog node and keeps the trust level associated with Fog Id in the local list. Based on the aggregated trust values, the system determines if a Fog node is a legitimate node or a rogue node. [PERSON] and [PERSON] [9] develop a Generic and Lightweight Security mechanism utilizing artificial reasoning called Fuzzy logic and Fog computing technology called GLSFIoT. It integrates the benefits of fog computing and fuzzy logic into designing a minimal security solution for IoT. The cloud layer nodes employ anomaly-based and fuzzy logic-based trust management and detection techniques to spot malicious activity in the fog layer in the IoT environment and pinpoint the most reliable fog nodes. Additionally, they stated that combining many edge nodes with reliable fog nodes is crucial before implementing security measures to minimize load and improve system performance. The trustworthy fog nodes carry out processes for flagging and detecting unknown attacks.