and the satellite Internet is too expensive.
The goal of this chapter is to provide an introduction and guidance to MFC developments. Specifically, different from the existing works [7, 8] related to MFC, this chapter discusses MFC in four major application domains: land vehicular fog computing (LV-Fog), marine fog computing (Marine Fog), unmanned aerial vehicular fog computing (UAV-Fog), and user equipment-based fog computing (UE-fog).
The rest of the chapter is organized as follows: Section 1.2 clarifies the term MFC. Section 1.3 breaks MFC down into four application domains and describes their characteristics. Section 1.4 enlists the wireless communication technologies used in the mentioned application domains, while Section 1.5 proposes a taxonomy of nonfunctional requirements for MFC. Open research challenges, both domain-oriented and generic, are identified in Section 1.6, and finally, Section 1.7 concludes this chapter.
1.2 Mobile Fog Computing and Related Models
In this chapter, MFC has its specific definition and it is not an exchangeable terminology with the other similar terms, such as mobile cloud computing (MCC) or multi-access (mobile) edge computing (MEC). In order to clarify the meaning of MFC, one needs to understand the aspects of the parties who introduced or adapted the terminologies. Commonly, MCC refers to a system that assists mobile devices (e.g. smartphones) to offload their resource-intensive computational tasks to either distant cloud [7] or to the proximity-based cloudlet [8].
Fundamentally, MEC is an European Telecommunications Standards Institute (ETSI) standard aimed to introduce an open standard for telecommunication service providers to integrate and to provide infrastructure as a service (IaaS), platform as a service (PaaS), or software as a service (SaaS) cloud services from the industrial integrated routers or switches of their cellular base stations. Explicitly, MEC is an implementation approach rather than a software architecture model. Further, as stated by ETSI, ETSI and OpenFog are collaborating to enable the MEC standard and the OpenFog Reference Architecture standard (IEEE 1934) to complement each other [9].
Today, researchers of industry and academia have been broadly using edge computing as the exchangeable term with fog computing. However, National Institute of Standards and Technology (NIST) and the document of IEEE 1934 standard for fog computing reference architecture, which derives from OpenFog Consortium, have specifically explained the differences between fog computing and edge computing. Accordingly, “the Edge computing is the network layer encompassing the end-devices and their users, to provide, for example, local computing capability on a sensor, metering or some other devices that are network-accessible” [10]. Further, based on the literature in edge computing domain, which include cloudlet-based computing models [11], one can explicitly identify that edge computing is loosely a bottom-up model. Specifically, an edge computing-integrated system emphasizes task offloading from the end-devices to the nearby cloudlet resources, which are capable of providing Virtual Machine (VM) or containers engine (e.g. Docker1)-based service to the other nodes within the same subnet.
On the other hand, fog computing is a hierarchical top-down model in which the system specifically tackles the problem about how to utilize the intermediate networking nodes between the central cloud and the end-devices to improve the overall performance and efficiency. Commonly, such intermediate nodes are Internet gateways such as routers, switches, hubs (e.g. an adaptor that interconnects Bluetooth-based device to IP network). Moreover, a fog node is capable of providing five basic services – storage, compute, acceleration, networking, and control [1]. Correspondingly, when a cloudlet or an IoT device is providing gateway mechanism to the other nodes and they are capable of providing some or all of the basic fog services, we also consider them as fog nodes.
By extending the notion above, MFC is the subset of fog computing that addresses mobility-awareness. Specifically, MFC involves two types – infrastructural fog (iFog)-assisted mobile application and mobile fog node (mFog)-assisted application. In summary, iFog-assisted mobile application enhances the performance of a cloud-centric mobile application by migrating the processes from the central cloud to the stationary fog nodes (e.g. the cellular base station) that are currently connecting with the mobile IoT devices. On the other hand, mFog-assisted applications host fog services on mobile gateways (e.g. in-vehicle gateway devices or smartphones) that interconnect other devices (e.g. onboard sensors, body sensors, etc.) or other things (e.g. proximal cars, people, sensors, etc.) to the cloud.
1.3 The Needs of Mobile Fog Computing
MFC 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
