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encompasses four application domains: land vehicular applications, marine applications, unmanned aerial vehicular applications, and UE-based applications. Specifically, each domain involves both iFog- and mFog-based architecture. Ideally, the approaches of iFog aim to provide generic solutions that are applicable to all the MFC domains where the infrastructure is applicable. On the other hand, mFog-based approaches aim to overcome the challenges in which the iFog is inapplicable or is unable to resolve effectively. To clarify the terminologies used in the rest of the chapter, iFog denotes infrastructural fog node and mFog represents the generic term of mobile fog nodes. Moreover, we further classify mFog to four types: LV-Fog, Marine Fog, UAV-Fog, and user equipment-based fog (UE-fog) corresponding to the mobile fog node hosted on a land vehicle, a vessel, a UAV, and a UE (e.g. smartphone, tablet, etc.).

      1.3.1 Infrastructural Mobile Fog Computing

      1.3.1.1 Road Crash Avoidance

      The number of vehicles on the road is increasing every year as well as the number of road accidents [12]. In order to reduce or avoid the collision accident, academic and industrial researchers have been working on improving the safety aspect of the vehicles. Specifically, the advancement in communication technologies has allowed the development of advanced driver-assistance systems (ADAS), which has emerged as an active manner of preventing car crashes. ADAS has made many achievements through the development of systems that include rear-end collision avoidance and forward collision warning (FCW). The vehicle-to-vehicle (V2V) communication plays a big role in ADAS systems and it is manifested in ensuring that the controllers on board the vehicles (i.e. onboard unit, OBU) are capable of communicating with other vehicles for the purpose of negotiating maneuvers in the intersections and applying automatic control when it is necessary to avoid collisions [13]. The success of these systems relies a lot on the reliability of the communication. Therefore, many models of V2V communication have been investigated. Some of them focus on probabilities and analytic approaches in modeling the communication message reception while others adapt Markovian methods to assess the performance and reliability of the safety-critical data broadcasting in IEEE 802.11p vehicular network [14]. Vehicular ad-hoc network (VANET) has also contributed to integrate and improve the car-following model or platooning, which reduces the risks of collisions and makes the driving experience safer [15]. Explicitly, today's smart vehicular network systems have applied the fog computing mechanisms that utilize the cloud-connected OBUs of the vehicle to process the data from the onboard sensors toward exchanging context information in the vehicular network and participating in the intelligent transport systems.

      1.3.1.2 Marine Data Acquisition

Today, marine data acquisition and cartography systems can achieve low-cost data acquisition and processing by composing IoT, mobile ad hoc network (MANET), and delay-tolerant networking (DTN) technologies. Specifically, sea vessels, which equip multiple sensors, can utilize International Telecommunication Union (ITU) standards-based very high frequency (VHF) data exchange system to route the sensory data to the gateway node (i.e. cellular base station) at the shore via the ship-based MANET. Afterwards, the gateway can relay the data to the central cloud. In general, such an architecture may produce many duplicated sensory transmission readings due to the redundant data transmitted from different ships. In order to remove such duplication and to improve the efficiency, the system can deploy fog computing service at the gateway nodes to preprocess the sensory data toward preventing the gateways sending duplicated data to the central server [4].

      1.3.1.3 Forest Fire Detection

Emerged smart UAVs, which are relatively inexpensive and can be flexibly dispatched to a large area under different weather conditions, both during day and night, without human involvement are the ideal devices to handle forest fire detection and firefighting missions. Specifically, with onboard image detection mechanism and mobile Internet connectivity, UAVs can provide real-time event reporting to the distant central management system. Further, in order to extend the sustainability of the image-based sensing mission, the system can distribute the computational image detection program to the proximal iFog hosted on cellular base stations and made accessible via standard communication technologies, such as Long-Term Evolution (LTE), SigFox, NB-IoT, etc. Hence, the UAVs can use their battery power only for flying and sensing tasks [16] (Figure 1.1).

Figure 1.1 Land-vehicular fog computing examples. (See color plate section for the color representation of this figure)

      1.3.1.4 Mobile Ambient Assisted Living

Today's UE devices, such as smartphones, have numerous inbuilt sensor components. For example, the modern mobile operating systems (e.g. Android OS) have provided numerous software components that are capable of integrating both internal and external sensors to support mobile Ambient Assisted Living (AAL) applications such as real-time health monitoring and observing the surrounding environments of the user to avoid dangers. Fundamentally, classic mobile AAL applications rely on the distant cloud to process the sensory data in order to identify situations. However, such an approach is often unable to provide rapid response due to communication latency issues. Therefore, utilizing proximal fog service derived either from the MEC-supported cellular base station or individual or small business-provided Indie Fog [17] has become an ideal solution to enhance the agility of mobile AAL applications [18].

      1.3.2 Land Vehicular Fog

      The development of vehicular networking has improved safety and control on the roads. Especially, LV-Fog nodes have emerged as a solution to introduce computational power and reliable connectivity to transportation systems at the level of Vehicle-to-Infrastructure (V2I), V2V, and Vehicle-to-Device (V2D) communications [19]. These networks are shaped around moving vehicles, pedestrians equipped with mobile devices, and road network infrastructure units. Further, these aspects have facilitated the introduction of real-time situational/context awareness by allowing the vehicle to collect or process data about their surroundings and share these insights with the central traffic control management units or other vehicles and devices in a cooperative manner.

      To perform such activities, there is a need for adequate computing resources at the edge for performing time-critical and data-intensive jobs [20] and face all the challenges related to data collection and dissemination, data storage, mobility-influenced changing network structure, resource management, energy, and data analysis [21, 22].

Most of the techniques proposed to solve these challenges are focusing on merging the computation power between vehicular cloud and vehicular networks [19]. This combination allows usage of both vehicles' OBUs and RSUs as communication entities. Another side that has been investigated is the issues related to latency and quality optimization of the tasks in the Vehicular Fog Computing (VFC) [20], and it was formulated by presenting the task as a bi-objective minimization problem, where the trade-off is preserved between the latency and quality loss. Furthermore, handling the mobility complexity that massively affects the network structure is addressed by using mobility patterns of the moving vehicles and devices to perform a periodic load balancing in the fog servers [23] or distance-based forwarding (DBF) protocol [19]. The energy management and computational power for data analysis are controlled by distributing the load among the network entities to make use of all the available resources based on CR-based access protocol [22].

      Moreover, the design of the Media Access Control (MAC) layer protocol in the vehicular networks is essential for improving the network performance, especially in V2V communication. V2V enables cooperative tasks among the vehicles and introduces cooperative communication, such as:

       Dynamic fog service for next generation mobile applications. The emergence of new mobile applications, such as augmented reality (AR) and virtual reality, have brought a new level of experience that is greedy for more computational

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