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 use cases illustrate the usefulness of fog-integrated UEs (Figure 1.4).
1.3.5.1 Healthcare
Healthcare is one of the top-five application domains in fog computing that has potential market value up to $2737 billion by year 2022 [33]. Specifically, IoMT applications, which commonly rely on the central cloud for managing data and performing decision-making, are now distributing certain tasks to the intermediate gateways. In particular, IoMT applications broadly utilize UEs (e.g. smartphones, tablets, etc.) as the gateways of wearable body sensors, in order to let the IoMT servers (e.g. hospital) acquire the sensory data anytime, anywhere from the patients. Further, considering the need of agile sensory data stream processing when the patients are in outdoor areas, which may not be able to maintain a high-quality network connection, utilizing the infrastructural fog (e.g. hosted at the cellular base station or ISP's Wi-Fi access points [APs]) can highly improve the overall agility of the data processing and identification of emergency situations. Moreover, considering modern UEs have powerful central processing units (CPUs) and decent storage spaces when the infrastructural fog is unavailable, the processes can also migrate to UEs to support the service continuity [34].
Figure 1.4 UE fog computing examples. (See color plate section for the color representation of this figure)
1.3.5.2 Content Delivery
Besides utilizing UEs as computing-based fog servers, UE-based fog computing nodes (UE-fog nodes) are also the ideal networking service providers. For example, replays of soccer matches, which catch important moments of the game, haver become an indispensable element of the sports game for the fans both inside or outside the stadium. Fundamentally, the audience members download the replay video to their UEs via the Internet. However, considering that there can be a large number of requests coming from the audience, the local wi-fi or cellular base stations may not be able to provide sufficient speed, especially when the audience demands high-quality or even ultra-high-quality videos. In order to address this problem, the application can host fog servers in the UEs of the
