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the traditional approach of a distant cloud-driver is incapable of achieving with good performance due to latency. Therefore, introducing Metropolitan vehicle-based cloudlet, which is a form of mobile fog node model, solves the latency issue by dynamically placing the fog at the areas with high demand. Furthermore, by adopting a collaborative task offloading mechanism, the vehicle-based mobile fog nodes are capable of effectively distributing the processes across all the participative nodes, based on their encounter conditions [24].

       Federated intelligent transportation. Traffic jams start to have a considerable negative impact by wasting time, fuel, capital, and polluting the environment due to the nonstop increase in the number of vehicles on the roads [25]. Fortunately, cloud-driven smart vehicles have emerged as a facilitator to overcome the problem. The solution resides in considering the serviceability level of mobile vehicular cloudlets (MVCs), which are a form of the mobile fog node model, based on the real-world large-scale traces of mobility of urban vehicles collected by onboard computers. Based on the peer-to-peer communication network, vehicles can further improve the traffic experience by exchanging real-time information and providing assistance to the manned or unmanned vehicles [26].

       Vehicular opportunistic computation offloading. Public transportation service vehicles, such as buses and trams, which commonly have fixed routes and time schedules, can be the mobile fog nodes for the other mobile application devices inside the proximal encountered vehicles that need to execute time-sensitive and computation-intensive tasks, such as augmented reality (AR) processes used for the advanced driver assistance systems and applications [27].

      1.3.3 Marine Fog

Integrating IoT to existing legacy marine systems can provide rapid information exchange. Initially, classic marine communication systems utilize VHF radio to obtain ship identification, ship location, position, destination, moving speed, and so forth. Unfortunately, VHF can provide only 9.6 kbps, which is insufficient to provide marine sensory data streaming [28]. Alternatively, ships can utilize the new satellite Internet to deliver their data, which is capable of achieving 432 kbps. However, satellite communication is not affordable for small and medium-size businesses since a simple voice service can cost USD $13.75 per minute [28]. In order to overcome the issue, researchers have introduced fog computing and networking-integrated marine communication systems for the Internet of marine t hings (IoMaT) [4]. Here, we term such a fog computing model Marine Fog.

By integrating a virtualization or containerization technology-based fog server with onboard equipment, vessels are capable of realizing a software-defined network (SDN) that allows the vessels to (re)configure a message routing path dynamically. Afterwards, by utilizing Marine Fog–based SDN mechanism, vessels can easily establish an ad hoc–based DTN, which caches data at the onboard Marine Fog node until the vessel encounters the next Marine Fog node, for delivering sensory data from the data source to the base stations toward relaying the data to the cloud. Moreover, an advanced Marine Fog node within the network may also perform data preprocessing in order to further reduce the transmission latency [29] (Figure 1.2).

      Existing wireless sensor network (WSN) architecture in marine monitoring uses sea buoys as sink nodes, capable of communicating with nearby sensor nodes (other buoys, vessels) directly (e.g. using ZigBee), as well as via the cellular Internet network [30]. By introducing the previously mentioned virtualization, the WSN architecture could be extended to be used in Marine Fog. However, this approach amplifies the need for energy-harvesting technology at the buoys.

Figure 1.2 Maritime fog computing examples.

      1.3.4 Unmanned Aerial Vehicular Fog

UAVs, which are also referred to as drones, can be employed in a broad range of applications due to their unrestricted geo-location feature. Hence, they have become one of the major elements in the IoT ecosystem. For example, smart city application can utilize UAVs as mobile sensors that can be sent to critical environments that are dangerous for humans (e.g. a forest fire) [16]. Specifically, by utilizing UAVs, the system is capable of establishing a UAV ad hoc network that is capable of performing a wide-ranged geolocation-based sensory data streaming network dynamically. Moreover, by mounting fog nodes on UAVs to perform sensory data analysis, the system can further improve the agility of identifying the emergency situations.

Below, we describe the features of the UAV-Fog [31], with corresponding examples indicated in Figure 1.3.

       Fast deployment. Modern UAVs are capable of carrying on tasks programmatically without human interference. Further, a system can dispatch a large number of UAVs to perform a temporary mission in an area where the manned vehicles are unable to reach or unable to effectively perform the tasks. For example, UAV-Fogs can assist wildfire problems in Portugal, Spain, and Australia [32].

       Scalability. The rapid growth of large-scale IoT applications requires more network infrastructure and fog computing resources in order to compensate for ultra-low latency. However, investing in the base infrastructure in certain areas is not cost-efficient. Hence, instead of developing the infrastructural IoT and fog network, the service provider can deploy UAVFog nodes to those areas. For example, in order to support the sensory data streaming performed by underwater vehicles, UAV-Fog nodes can fly above the water surface in order to route the data stream to the base station at the shore [31].Figure 1.3 UAV fog computing examples.

       Flexibility. UAV-Fog nodes can equip heterogeneous capabilities to support various applications. For example, an Olympic event in a city lasts 16 days. During the contests, a large number of visitors are gathering in the city and many of them are using Social Network Services (SNS) to disseminate information (e.g. text, image, video posts) related to the event. However, the city's network infrastructure may not have sufficient capacity to provide the high-quality experience for the SNS users due to traffic overload. In order to support the best quality of experience (QoE) for the SNS users, SNS providers may deploy UAV-Fog nodes to the city to provide a temporary location-based social network (LBSN) mechanism that directly routes the content (e.g. Twitter posts, YouTube video stream, etc.) within the city when the content provider and the receiver are within the city.

       Cost-effective. The content described in previous paragraphs has indicated that employing UAV-Fog nodes is a cost-effective solution for many applications that require only a temporary enhancement for computational or networking needs. For example, wildfires in Australia often occur in areas where the network infrastructure is unavailable. Hence, establishing an infrastructural IoT-based smart monitoring system at such an area is unrealistic. Second, many cities in the world are unable to provide fundamental infrastructure for the rapid growth of IoT applications. Instead of waiting for the hardware service provider to complete the infrastructure, the IoT software service provider can simply deploy more UAV-Fog nodes to the areas that require more resources. Finally, many cities often spent a large amount of money on network infrastructure for temporary events, which is cost-inefficient. Although it is possible to send the manned land vehicular-based nodes (e.g. mobile base stations) to support the need, compared to unmanned UAVs, the manned solutions require payroll for human workers and extra petrol or electricity, since the movement of land vehicles is constrained based on the roads.

      1.3.5 User Equipment-Based Fog

UE represents the end-users' terminal devices (e.g. smartphones) that are connecting to an Internet Service Provider (ISP)'s network. Initially, UEs are thin clients in the IoT ecosystem. However, recent research efforts have utilized UEs as one of the major service provisioning elements. Accordingly, the following

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