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the public applications, mobile IoT plays an important role in personal applications, such as Internet of Medical Things (IoMT) applications [5], which utilize both inbuilt sensors of the UE (e.g. smartphone) and the UE-connected body sensors attached on the patient to collect health-related data and forward the data to the central system of the hospital via the mobile Internet connection of the UE.

      Explicitly, the mobile IoT applications described above are time-critical applications that require rapid responses. However, the classic IoT system architecture, which relies on the distant central management system to perform the decision making, has faced its limitation to achieve the timely response due to latency issues deriving from the dynamic network condition between the front-end IoT devices and the back-end central server. Furthermore, the large number of connected mobile IoT devices have raised the challenges of mobile Big Data [6] that increase the burden of the central server and hence, lead to bottleneck issues. In order to improve the agility and to achieve the goal of ultra-low latency, researchers have introduced fog computing architecture [1].

      Fog computing architecture (the fog) distributes the tasks from the distant central management system in the cloud to the intermediate nodes (e.g. routers, switches, hubs, etc.), which contain computational resources, to reduce the latency caused by transmitting messages between the front-end IoT devices and the back-end cloud. Specifically, the fog provides five basic mechanisms: storage, compute, acceleration, networking, and control toward enhancing IoT systems in five subjects: security, cognition, agility, low latency, and efficiency [1]. For example, in IoV application, the central server can migrate the best route determination function from the cloud to the roadside fog nodes to assist the travel of the connected vehicles. As another example, in an outdoor-based IoMT application, the hospital system can distribute the health measurement function and the alarm function to the UE in order to perform timely determination of the patient's health condition and to perform an alarm to catch the proximal passengers' attention when the patient is having an incident.

      Here, we use the term mobile fog computing (MFC) to describe the fog-assisted mobile IoT applications.

MFC brings numerous advantages to mobile IoT in terms of rapidness, ultra-low latency, substitutability and sustainability, efficiency, and self-awareness. However, the dynamic nature of MFC environment raises many challenges in terms of resource and network heterogeneity, the mobility of the participative entities, the cost of operation, and so forth. In general, the static fog computing frameworks designed for applications, such as the smart home or smart factory would not fully address the MFC-specific challenges because they have different perspectives from the involved entities and the topology. For example, a classic fog computing framework, which may involve a thin mobile client-side application for smartphone users, would not consider how to provide a reliable fog service to the high-speed moving vehicles. Moreover, the classic fog computing framework also would not consider how to provide a reliable fog service to vessels at sea where the telecommunication base stations are not available, 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

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