# Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study
## 1 Introduction  
Vehicular Fog Computing (VFC) is a promising, as yet unexplored paradigm which aims to support services in order to include Internet infotainment applications. Cloud Computing (CC) has attracted big companies (such as VTube, Netflix, Cameleon, etc.) [1, 2] that seek to exploit the Fog Nodes (FNs) of the Infrastructure-to-Vehicle (I2V) with single-hop IEEE 802.11 wireless links for data dissemination, providing low energy consumption [2]. Furthermore, Fog Computing (FC) is used to design an efficient solution supporting low-latency [3], geo-distributed Internet of things (IoT) devices/sensors [4], as well as providing energy-efficiency and self-adaption. It provides spectrum efficiency by means of a strict collaboration among end-devices in the same integrated platform [5]. FC aims at distributing the self-powered data center (e.g., FNs) between resource-remote clouds and a resource-limited smartphone in the vehicles [6], in order to perform context-aware energy-efficient data mining and dissemination for the adoption of a smartphone device and clients' highly dependent mobile applications [3, 7, 8]. Moreover, being deployed in the proximity of the Vehicular Clients (VCs), the FNs efficiently exploit the awareness ofthe connection states supporting latency and delay-jitter. Among the contributions that deal with the adaptive scaling of the server resources in similar VCs, the work in [9] focuses on the impact of the dynamic scaling of the CPU computing frequencies on the energy consumption experienced by the execution of MapReduce-type jobs. Fog Data Centers (FDCs) are dedicated to supervising the transmission, distribution and communication networks [10], all of which require a precisely managed grid infrastructure from generation points to consumption points by using the communication of measured values and transmitted control information more accurately. The IoT applications (real-time requirements, such as stream processing) are distributed in different geographical locations [11], with numerous devices and emit data via gateways (FNs) for further processing and filtering. In addition, the FNs host application modules that connect sensors to the Internet. FNs include Cloud resources that are provisioned on-demand from a geographically distributed Fog Data Center (FDC). Fog Data Center services are mainly implemented in Fog; they use a distributed architecture over moderate-bandwidth to provide high availability operations. The vehicles are attached to one of the Road Side Units (RSU) in the FN [12]; the heterogeneous type of devices consider a physical infrastructure, with each FN consisting of a single server or a set of servers building heterogeneous FNs. Fog Data Centers entails a huge amount of computations and it is distributed; it may be more energy-efficient than the centralized Cloud model of computation, with the reduction of the FDC energy consumption being an important challenge. Also, this drastically reduces the traffic sent to the Cloud by allowing the placement of filtering operators close to the data sources. FDC, as a vital component of the IoT (vehicular) environment, is capable of filtering and processing a considerable amount of incoming data on edge devices, by making the data processing architecture distributable, thus scalable. Hence, it is an important task to provide a neat simulated scenario or case study, thus detailing the analytic structure of the FDC and the traffic injected to the engaged servers to make the presented model more efficient and interesting. Fog Data Centers, in each processing unit, executes the currently assigned task by self-managing its own local virtualized storage/computing resources. When a request for a new job (transferred through the data network) is submitted from the remote clients and transferred through the Internet to the FNs and FDC, the resource controller dynamically performs both admission control and allocation of the available virtual resources.