## 2 Related Work  
Recently, a few studies have focused on edge-based and cloud-based smart cities. Moreover, some of surveys have examined fog computing applications in smart cities, among which only one followed a precise design. As mentioned above, determine and investigate delay and the location-sensitive vehicular utilization of smart cities through fog computing, we resolved to analyze the existing related reviews to lessen the delay and latency of fog computing in smart city advancement. These studies are based on an overview analysis of smart city fog computing. [PERSON] et al. [16] addressed new issues and difficulties of smart cities corresponding to smart vehicle transportation, and audited a few approaches and advanced technology. The domain characterization that was indicated in this audit was very much portrayed. Nonetheless, numerous elements were ignored that were benefits to executing smart cities, such as giving essential necessities and benefits to the public and advancing vehicle information assurance and appropriate manageability planning. Likewise, the audit approach was not precise; the method of choosing papers was not demonstrated. Recent smart city definitions characterize smart urban areas as metropolitan agglomerations in which a wide range of IoT devices and sensors are utilized to gather information, then apply them to deal with tremendous assets successfully. [PERSON] et al. [17] presented a \"fast hybrid multi-site computation offloading solution\". This technique offloads the task promptly, and they developed two algorithms to obtain the best. Another study [18] proposed an imaginative idea that transforms associated vehicles into mobile fog nodes based on vehicular fog computing and uses vehicle adaptability to give practical and on-request vehicular fog computing applications. Coincidentally, all of the vehicles in these works suffer from impetus issues, issues the fog nodes ought to consider. [PERSON] et al. [19] suggested an agreement-based technique for verifying the accuracy and stability of the vehicular information system within a fog computing network to minimize latency and assure the validity of the vehicular network system. [PERSON] et al. [20] describe a fog-assisted architecture for seamless handover. As a result, the proposed architecture enabled dispersed applications such as Vehicle-to-Everything (V2X) to meet critical requirements such as time, safety, and security-sensitive network operations. [PERSON] et al. [21] present a traffic management strategy based on vehicle-to-infrastructure communication or V2I communication. At each RSU, vehicles transmit a message, and when they encounter a jam, vehicles ask for information on nearby streets from the closest RSU. Each vehicle can find alternate routes using this data. According to the results of the simulation, the proposed method had the potential to reduce travel time while having little impact on distance traveled. For the efficient processing of IoT jobs, [PERSON] et al. [22] offer a capacity-based load-balancing strategy for vehicular fog distributed computing supported by a cluster. By taking into account vehicle position, speed, and direction when forming clusters that serve as a pool of computing resources, the authors suggest a dynamic clustering approach. For a vehicle to be identified as having left the cluster, the study suggests an identification process that may be used to anticipate its future position inside the dynamic network. Additionally, the authors offer a capacity-based load-distribution method for balancing load in the vehicular fog network on both an intra- and inter-cluster basis. The NS2 network simulation environment provided the most up-to-date findings for the simulations, and the results demonstrated that the suggested strategy resulted in balanced network energy usage, decreased network delay, and increased network utilization. [PERSON] et al. [23] summarized the current state of research on fog computing in VANETs and discussed future directions.