Fog Computing

There have been a variety of predictive models capable of handling binary targets, ranging from traditional logistic regression to modern neural networks. However, when the target variable represents a rare event, these models might not be appropriate as they assume that the distribution in the target variable is balanced. In this article, the impact of multiple resampling methods on conventional predictive models is studied. These resampling techniques include the methods of oversampling of the rare events, undersampling of the common events in the data, and synthetic minority over-sampling technique (SMOTE). The predictive models of decision trees, logistic regression and rule induction are applied with SAS Enterprise Miner (EM) software to the revised data. The studied data set is of home mortgage applications which includes a target variable with an occurrence rate of the rare event being 0.8%. The authors varied the percentage of the rare event from the original of 0.8% up to 5...

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/ cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.
Keywords Fog computing • Offloading • Internet of things (IoT) • Quality of service (QoS)

The emergence of Fog Computing has culminated into a plethora of service options for computing users. Fog computing provides opportunities where a network of remote servers on the Internet can store and process data, rather than a local server or a personal computer as elaborated by the Compaq Computer in 1996. Defining policies for adopting Fog Computing from the perspectives of both the individual users and organisations has become essential for choosing the right Fog Computing services. The aim of this chapter is to articulate the vantage points of Fog Computing in the informational computational continuum and explore the factors affecting the adoption of Fog Computing by users or organisations with a special focus in the developing world. Amongst others, the chapter explores the contemporary privacy and security concerns and other issues of Fog Computing especially from the standpoint of service excellence. Further, the chapter explores contextual challenges that may be encountered when implementing Fog Computing in developing world contexts such as low latency and jitter, context awareness and mobile support due to underdeveloped ICT infrastructures. The chapter integrates the Technology, Organisation and Environment (TOE) framework which can be employed to understand the factors influencing the penetration of Fog Computing in different businesses in the developing world. The chapter also explores opportunities bordering on how Fog Computing can advance the agenda of service ubiquity and pervasiveness.

Since the proliferation of fog computing, various distributed architectures have been proposed to extend the cloud to the edge of the network. However, so far there exists no study that compares different fog computing architectures, and produces quantitative results in order to examine the efficiency of each architecture for different use cases. Such a study could provide guidelines for selecting an appropriate distributed architecture for fog computing while taking into account the requirements of the final applications. To bridge this gap in the literature, we create a unified system model which is able to represent the basic architectures commonly used for fog computing, i.e., hierarchical and flat. Furthermore, we design algorithms that can be used for creating fog computing systems that follow these architectures, and we perform various experiments that focus on communication latency and bandwidth utilization. Notably, our results show that for applications that do not have a dependency on the cloud, i.e., no resource-demanding tasks are involved, the hierarchical architecture reduces the communication latency by 13% compared to the flat. However, for applications that also include resourcedemanding tasks, the flat architecture reduces the communication latency by 16% compared to the hierarchical.

Internet-of-Things (IoT) generate large data that is processed, analysed
and filtered by cloud data centres. IoT is getting tremendously popular: the
number of IoT devices worldwide is expected to reach 50.1 billion by 2020 and
from this, 30.7% of IoT devices will be made available in Healthcare. Transmission
and analysis of this much amount of data will increase the response time
of cloud computing. The increase in response time will lead to high service
latency to the end-users. The main requirement of IoT is to have low latency to
transfer the data in real-time. Cloud cannot fulfill the QoS requirement in a
satisfactory manner. Both the volume of data as well as factors related to internet
connectivity may lead to high network latency in analyzing and acting upon the
data. The propose research work introduces a hybrid approach that combines
fuzzy and reinforcement learning to improve service and network latency in
healthcare IoT and cloud. This hybrid approach integrates healthcare IoT
devices with the cloud and uses fog services with Fuzzy Reinforcement
Learning Data Packet Allocation (FRLDPA) algorithm. The propose algorithm
performs batch workloads on IoT data to minimize latency and manages the
QoS of the latency-critical workloads. It has the potential to automate the reasoning
and decision making capability in fog computing nodes.

Multimedia cloud computing has emerged as a popular paradigm for the support of delay-intolerable immersive multimedia applications with high-end three-dimensional rendering. To that end, fog computing offers distributed computational offloading solutions, by positioning rendering servers in close proximity to end users promising in this way continuous service provision, that is otherwise not easily attainable under the strictly centralized cloud-only model. Yet, in order to alleviate the multimedia providers from unnecessary capital expenditure, a strategic placement approach of the servers at the fog layer must be implemented, that can effectively cope both with the network dynamics and the overall imposed deployment cost, and still adhere to the delay bounds set forth by the multimedia application. In this paper, we formally formulate the problem as a facility location problem using constrained optimization over a finite time horizon. We then theoretically analyze the minimum acceptable conditions necessary for a decentralized location of the servers, utilizing solely local information around their immediate neighborhood, that iteratively leads to better solutions. Based on the analysis, we propose a distributed algorithm, namely the Autonomous Renderer Placement Algorithm (ARPA), to address it. ARPA employs localized service relocation to shift the placement according to simple rules that designate elastic migration, replication, and complementary consolidation of the underlying renderers. Simulation results under diversified deployment scenarios, as well as trace-driven comparisons against other approaches, testify to ARPA's accountability in obeying the delay limits and fast converge in finite time slots to a placement solution that both outperforms the baseline alternatives and is close to the optimal one, rendering it suitable for scaling up and down to meet the current demands of the offered multimedia applications.

ABSTACT
Fog computing is a prime example that extends Cloud computing and Internet of Things (IoT) application services to the edge of the network. Correlated to Cloud, Fog computing provides data transferring, larger and more immediate storage bandwidth and application services to end users. This innovative architecture stands in close correlation with its Cloud counterpart. As it is well known Cloud faces various security and privacy problems thus inheriting most of them to Fog computing along side some new security and privacy challenges because of Fog's distinct characteristics. Later on, we will cite these new challenges and threads.

In this paper, we discuss the most significant application opportunities and outline the challenges in performing a real-time and energy-efficient management of the distributed resources available at mobile devices and Internet-to-Data Center. We also present an energy-efficient adaptive scheduler for Vehicular Fog Computing (VFC) that operates at the edge of a vehicular network, connected to the served Vehicular Clients (VCs) through an Infrastructure-to-Vehicular (I2V) over multiple Foglets (Fls). The scheduler optimizes the energy by leveraging the heterogeneity of Fls, where the Fl provider shapes the system workload by maximizing the task admission rate over data transfer and computation. The presented scheduling algorithm demonstrates that the resulting adaptive scheduler allows scalable and distributed implementation.

Up to date, Social Internet of Things (SIoT) and Fog Computing (FC) are two standing-alone technological paradigms under the realm of the Future Internet. SIoT relies on the self-establishment and self-management of inter-thing social relationships, in order to guarantee scalability to large IoT networks composed by both human and non-human agents. FC extends cloud capabilities to the access network, in order to allow resource-poor IoT devices to support delay-sensitive applications. Motivated by these complementary features of the SIoT and FC models, in this position paper, we propose their integration into the novel paradigm of the Social Fog of IoT (SoFT). Specifically, we provide the following three main contributions: (i) after formalizing the SoFT paradigm, we motivate its introduction through a number of exemplary use cases; (ii) we describe the architecture and the main resource-management functions of the resulting virtualized SoFT technological platform. It merges the physical things at the IoT layer and their virtual clones at the Fog layer into a cyber-physical overlay network of social clones; and, (iii) as a proof-of-concept, we present the simulated performance of a small-scale SoFT prototype, and compare its energy-vs.-delay performance with the corresponding one of a state-of-the-art virtualization-free technological platform, that relies only on Device-to-Device (D2D) inter-thing communication. THE INCOMING ERA OF THE SOCIAL THINGS U NDER the vision of the Future Internet, the IoT paradigm aims at connecting anything, to be accessed at anytime from anywhere by using the Internet as basic network infrastructure. According to this vision, future IoT networks will be composed by a large number (e.g., an order of magnitude greater than the current one) of IP-enabled and resource-limited wireless (possibly, mobile) devices, that will cooperate for attaining common goals and/or providing composite target services [1]. Hence, due to scalability and energy consumption issues, the centralized management of future large-scale IoT networks is not a viable option. In order to address this challenge, the Social Internet of Things (SIoT) paradigm is quickly gaining momentum [2]. It integrates Social Networking (SN) principles into the native IoT model, in order to enforce distributed self-cooperation and self-management in physical networks composed by human and non-human agents. Specifically, in the SIoT vision, human and non-human entities interact according to the Peer-to-Peer (P2P) model by autonomously building up inter-thing social relationships with respect to the rules enforced by their owners. In so doing, the resulting social network of " friend " things: (i) is capable to self-evolve without any human centralized supervision and/or control; and, (ii) similarly to human social networks (like Facebook), it may be self-navigated by the involved things, in order to perform information and/or service discovery [2]. Hence, the SIoT model effectively addresses the scalability issue of the native IoT paradigm by enforcing the convergence of social and communication networks. However, it also opens the question about the network technology and protocol layer to be selected, in order to implement the SIoT network in an energy-efficient way. For this purpose, the Social Device-to-Device (S-D2D) paradigm has been recently proposed [3]. It aims at implementing the inter-thing social network at the Physical layer of the underlying protocol stack by directly exploiting D2D short-range wireless (possibly, mobile) connections. The main pro of this paradigm is that it operates in an " ad hoc " (e.g., infrastructure-free) way. However, its main con is that it forces the involved things to exploit their native (limited) resources, in order to support the resulting social network [3]. Motivations and main idea of the paper In principle, the emerging paradigm of Fog Computing (FC) [4] exhibits the right features for coping with the aforementioned technological issues. Shortly, Fog Nodes (FNs) are small-size virtualized interconnected resource-equipped data centers. They are hosted by wireless access points at the edge of the network, in order to build up a three-tier Cloud-Fog-Thing hierarchical architecture. FC natively supports three main services, namely, thing virtualization, Thing-to-Fog task offloading and inter-Fog resource pooling. In principle, these services could be efficiently exploited, in order to implement the SIoT social network as an overlay network of thing clones, that entirely relies on the bandwidth/computing resources of the supporting FNs. So doing, the native resources of the physical things could be employed only for the synchronization with the corresponding Fog-hosted clones. This is, indeed, the main idea behind the proposed SoFT paradigm. According to these considerations, the remaining part of this paper is organized into two main parts. First, after shortly reviewing and comparing the basic features of the FC and Cloud Computing (CC) paradigms, we introduce the IoT-SIoT-Fog interplay induced by SN principles through an illustrative example. Afterwards, we pass to describe a number of actual use cases that motivate the SIoT-Fog integration and point out its added value. Second, as main technical contributions of the paper: (i) we formalize the main building blocks and functionalities of the proposed SoFT technological platform; and, then, (ii) we compare the energy-vs.-delay performance of a SoFT test-bed with the corresponding one of a state-of-the-art S-D2D test-bed. Finally, we conclude the paper by addressing some future research directions.

The emergence of fog computing has brought computation capabilities towards the edge of the network to support the needs of latency-intolerant services. Fog computing is designed to complement and not to replace cloud computing. Connecting users continuously with their desired services is the most important prerequisite of the fog computing paradigm. As these services are consumed mostly by mobile end users, therefore, the services must be kept close to the users characterized by mobility, to ensure the quality of services. The inherent nature of mobility demands that services be migrated with the users to ensure consistent optimal quality. Studies have emerged to address the issue of service migration in fog computing. This paper aims to bring together studies on service migration approaches in fog computing. Finally, based on the study of the current state of literature, a list of gaps and future opportunities have been identified and presented in this paper.

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of objects) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT inf...

There is a growing requirement for Internet of Things (IoT) infrastructure to ensure low response time to provision latency-sensitive real-time applications such as health monitoring, disaster management, and smart homes. Fog computing offers a means to provide such requirements, via a virtualized intermediate layer to provide data, computation, storage, and networking services between Cloud datacenters and end users. A key element within such Fog computing environments is resource management. While there are existing resource manager in Fog computing, they only focus on a subset of parameters important to Fog resource management encompassing system response time, network bandwidth, energy consumption and latency. To date no existing Fog resource manager considers these parameters simultaneously for decision making, which in the context of smart homes will become increasingly key. In this paper, we propose a novel resource management technique (ROUTER) for fog-enabled Cloud computing environments, which leverages Particle Swarm Optimization to optimize simultaneously. The approach is validated within an IoT-based smart home automation scenario, and evaluated within iFogSim toolkit driven by empirical models within a small-scale smart home experiment. Results demonstrate our approach results a reduction of 12% network bandwidth, 10% response time, 14% latency and 12.35% in energy consumption.

Cloud computing provides a flexible and large-scale infrastructure for individuals and organizations to deliver service-oriented solution at nominal cost. The cloud infrastructure management becomes more complex as data centers are growing faster in terms of hardware and software resources. Hence, an efficient cloud monitoring system is needed to manage the cloud infrastructure and optimize overall performance of the cloud. The Multi-agent system is one of the best approaches to improve the performance of cloud. In this paper, we briefly discuss about basic concepts of cloud monitoring system along with comparative study of agent-based cloud monitoring system with respect to phases and properties of cloud monitoring system. A multi-agent cloud monitoring system collects raw data, filters out unwanted data and aggregates relevant data to aid the monitoring of cloud environment.

In this paper, we propose an architecture for privacy preserving protocols in an IoT-Fog-Cloud ecosystem computing hierarchy.
We consider the paradigms of Fog and Edge computing, together with a
multi-party computation mechanism that enables secure privacy-preserving data processing in terms of exchanged messages and distributed computing.
We discuss the potential use of such an architecture in a scenario of pandemics where social distancing monitoring and privacy are pivotal to
manage public health yet providing confidence to citizens.

In this paper we formulate the static load balancing problem in single class job distributed systems as a cooperative game among computers. It is shown that the Nash Bargaining Solution (NBS) provides a Pareto optimal allocation which is also fair to all jobs. We propose a cooperative load balancing game and present the structure of the NBS For this game an algorithm for computing NBS is derived. We show that the fairness index is, always 1 using NBS which means that the allocation is fair to all jobs. Finally, the performance of our cooperative load balancing scheme is compared with that of other existing schemes.

Imaging in poor weather is often severely degraded by scattering due to suspended particles in the atmosphere such as haze and fog. Vehicles which are travelling in hill stations or in the early morning during winter season will face problems due to fog formation in the atmosphere. In this method various methods have been used to clear the fog and to get a clear view of the road. The system uses a camera to detect the object present in the foggy region. The object can be a human or any inhuman material. The video captured by the camera is processed to detect the object. If the object is detected then that data will be given to the controller and it will be intimated to the driver by voice. Vehicle to vehicle also established in this system. If any vehicle goes near to another vehicle in foggy areas the two vehicles will communicate each other through WSN and it will be given by voice play back. Using this system accident can be prevented. Keywords: Suspended particles, fog, detect, Vehicle to Vehicle, WSN. I. INTRODUCTION Nowadays public places are monitored by several CCTV cameras in order to increase public safety. In many applications, the trained and experienced human operators can do this monitoring very well. However, watching multiple camera images at the same time is not only too expensive but also practically impossible. Moreover, surveillance video data is currently used only "after the fact" as a forensic tool, thus losing its primary benefit as an active, real-time medium. The goal of visual surveillance is not only to put cameras in the place of human eyes, but also to accomplish the entire surveillance task as automatically as possible. Thus intelligent visual surveillance (IVS) becomes an active research topic in computer vision. Detection of foreground objects of interest from a surveillance video sequence is a key step for an intelligent visual surveillance system. Unfortunately, this is not always true, such as when videos are taken under bad weather conditions, such as on a foggy day. The image suffers degradation and severe contrast loss. These low quality images are a nuisance for conventional object detection algorithms. Similarly, Murk is a thick cloud of tiny water droplets suspended in the atmosphere which obscures visibility. Diverse weather situations such as murk, smoke, rain or snow will cause multifaceted visual effects of spatial or temporal domains in images or video. Such artifacts may appreciably humiliate the performances of outdoor vision systems relying on image/video feature extraction or visual attention modeling such as event detection, object detection, tracking and recognition, scene analysis and classification, image indexing and retrieval. They generally fail to correctly detect objects due to low scene visibility. In order to get clear surveillance frames, enhancing visibility is an inevitable task. In recent years, as an active research topic in computer vision, considerable work has been done on haze removal techniques. Unfortunately, most countries in the world has an alarming record in number of death/disability due to tremendous number of accident. Accidents are occurred because of unawareness of the people. Researchers found that 57% of accidents where due to solely driver factors, which include his behaviour, decision making ability, reaction speed and alertness. The studies show that the accidents can be avoided if driver was provided with warning message few seconds before so that, they can take some alternative route or be cautious to avoid traffic congestion or accidents. The vehicular adhoc network was adopted to mimic the adhoc nature of highly dynamic network. In this network two vehicles can communicate with each other. For Vehicle safety a new technique can be created. VANET Communication is classified into two different types Vehicle to Vehicle communication and Vehicle to Infrastructure Communication. The vehicle to vehicle communication is a communication between two vehicles (i.e.) one hop communication, such as car to car communication. The vehicle to Infrastructure communication is communication between vehicle and road side Infrastructure. It acts as a multi hop communication. The vehicle to vehicle communication is a system designed to transfer basic safety related with vehicles to provide warning to drivers concerning accidents. The main objective of this system is to alert drivers when he closes to front vehicle. The communication between the vehicles takes place by means of LI-FI. The distance between two vehicles is measured using Ultrasonic sensor. The microcontroller controls the entire circuit and is programmed to notify the driver with a message when the vehicle comes within the Line of sight.

The widespread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.

Fog computing has been regarded as an ideal platform for distributed and diverse IoT applications. Fog environment consists of a network of fog nodes and IoT applications are composed of containerized microservices communicating with each other. Distribution and optimization of containerized IoT applications in the fog environment is a recent line of research. Our work took Kubernetes as an orchestrator that instantiates, manages, and terminates containers in multiple-host environments for IoT applications, where each host acts as a fog node. This paper demonstrates the industrial feasibility and practicality of deploying and managing containerized IoT applications on real devices (raspberry pis and PCs) by utilizing commercial software tools (Docker, WeaveNet). The demonstration will show that the application's functionality is not affected by the distribution of communicating microservices on different nodes.

Fog Computing (FC) is a system that connects cloud computing (CC) with the Internet of Things (IoT). It contributes to easier data transfer between cloud and IoT servers as it makes them closer to each other. FC effectively replaces the services, such as applications and information, at or near the cloud. This limits the bandwidth consumption, decreases delay and facilitates maximum network reliability since the data must not be transmitted to its intended destination or travel long distances. While Software Defined Networking (SDN) is a network engineering technique that permits network control and 'programming' through software applications in an intelligent and centralized way. It is a technology that provides greater programming and flexibility for networks by the separation of the control plane from the data plane. The software-based networks will respond to changes effectively in CC. The SDN presidency increases the efficiency of network setup and enhances network performance and reporting. To improve network efficiency, SDN can be built into FC. In this paper, we first defined FC and touched on its architecture and benefits, relying on the sources of previous studies. Secondly, we defined SDN and explained its components in detail with its method of operation, its benefits, and its impact on networks, and then we presented the method of combining the SDN with FC and the benefits of that. Finally, after relying on a large number of previous researches, we presented some applications that use these two methods together. The results were all indicating improvement in the work of networks in the various applications that were integrated between SDN and FC.

The continuous growth of the vehicles number, together with associated problems encountered in transportation systems have driven significant developments in the framework of Intelligent Transport System (ITS). Recently, an advanced solution-Internet of Vehicles (IoV) is proposed, seen as a part of Future Internet and specifically of Internet of Things (IoT), aiming to offer novel advanced commercial and technical capabilities. IoV will integrate the previous Vehicular Ad Hoc Networks (VANET) and also functionalities already developed in ITS. However, the architectural aspects of the IoV are still open research issues. This paper attempts a comparative critical study of several functional architectures proposed for IoV, including recent ones based on Cloud/Fog computing and Software defined networking (SDN)-control.

The Internet of Things (IoT) is creating a new evolution in the present and future Internet. The idea of IoT is to establish transmission capacities using a ubiquitous, distributed and diverse gadgets network. The rapid growth of the IoT makes the incorporation and connection of several devices a predominant procedure. The increasing numbers of IoT devices and diverse IoT traffic patterns has created the need for traffic classification methods to provide solutions for IoT applications'issues. Although it has been presented in many papers and surveys, network traffic classification is still undeveloped well in IoT because of the variations in traffic classifications in IoT and NonIoT gadgets. This paper discusses the arising patterns of IoT network traffic classifications and putting them in practical use. It also presents an overview of traditional traffic classification methods, as well as a discussion with a categorization.This paper evaluated the performance metrics such as accuracy, recall, precision and F1 score for these Machine Learning algorithms: Decision Tree (DT), K-Nearest Neighbors (K-NN), Naïve Bayes (NB) and Gradient Boosting (GRB) classifiers. The analysis of normal and attack traffic is done by using WEKA software tools and by utilizing the BoT-IoT dataset [1].

Fog computing is a prototype that protract Cloud
computing and services to the edge of the network. Like Cloud,
Fog provides data, compute, storage, and application services
to end-users. Fog Computing terminology is given by Cisco that
implies extending cloud computing to the edge of a network.
Broadly called Edge Computing or preparatory, fog computing
reinforces the operation of cloud, storage and networking
services between end devices and conveyed processing data
centers. Fog computing is a gifted computing aspect that
protract cloud computing to the edge of networks. Similar to
cloud computing with distinct characteristics, fog computing
faces new-fangled security, privacy and trust issues, control
information overhead and network control policies resist
other than those obtained from cloud computing. One of those
hurdle is data trimming. Because redundant communications
not only burden the core network but also the data center in
the cloud. For this purpose, data can be preprocessed and
trimmed before sending to the cloud. This can be done through
a Smart Gateway, accompanied with a Smart Network or Fog
Computing. We have reviewed these defies and prospective
plans briefly in this paper. We have provided a state-of-the-art
survey of Fog computing, its challenges and security issues.

The field of computing has covered a long path. From Mainframes to traditional desktop and then towards Ubiquitous Computing. Ubiquitous Computing combined with wireless Network embedded with cloud computing have given rise to new type of concept known as Internet of Things. But this is not the final destination. A new edge is added further to this which is known as Edge Computing or Fog Computing. With less maintenance and cost based on service usage, cloud computing’s layered architecture allows the client to purchase services at different levels such as IaaS, PaaS and SaaS. It permits location independence facility as users can access these services anywhere with an internet connection and a web browser. IoT devices generate data constantly, and often analysis must be very rapid. Main sources of this huge amount of data are devices, sensors or actuators. Handling the volume, variety, and velocity of IoT data requires a new computing model. Also the time it takes to makes its way ...

– Over the past few years, the pervasive deployment of Cloud computing has tremendously impacted the IT industry, due to its compute, storage, and processing power. However, the integration of the Internet of Things (IoT) and Cloud Computing has been a hot topic for many researchers (also known as CloudIoT), in relation to their complementarity. IoT, known as one of the major sources of Big data has limitations like insufficient storage, computational power, and security, which are partially resolved by the Cloud. Although, the emergence of an extension of the Cloud computing paradigm to the edge of the network, called Fog/Edge Computing, has been touted to be a better option as it is said to bring the cloud closer to the ground and efficiently tackle the limitations of Cloud Computing. In this paper, both Cloud and Fog computing techniques are explored, their comparisons, advantages, limitations, and extensive review of the current state-of-the-art in both cases are discussed. Implementation of the transfer of big data from an IoT device to the cloud, using the Fog Computing technique is demonstrated.

Although Internet of Things (IoT) brings significant advantages over traditional communication technologies for smart grid and smart home applications, these implementations are still very rare. Relying on a comprehensive literature review, this paper aims to contribute towards narrowing the gap between the existing state-of-the-art smart home applications and the prospect of their integration into an IoT enabled environment. We propose a holistic framework which incorporates different components from IoT architectures/frameworks proposed in the literature, in order to efficiently integrate smart home objects in a cloud-centric IoT based solution. We identify a smart home management model for the proposed framework and the main tasks that should be performed at each level. We additionally discuss practical design challenges with emphasis on data processing, as well as smart home communication protocols and their interoperability. We believe that the holistic framework ascertained in this paper can be used as a solid base for the future developers of Internet of Things based smart home solutions.

With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed.

Cloud computing with its simplicity, multi-tenancy, and massive scalability is indeed the platform of any successful organization in tomorrow’s world. Generally, cloud computing is a term that involves the delivering of hosted services over the Internet, but just like all new technologies that emerge, there are always new risks to be discovered and old risks to be re-evaluated. Resource sharing and management is one of much great priority in our educational institutions today, mostly with those in the developing regions of the world. These institutions have high demand for resources but can hardly afford to implement or even sustain the cost of traditional computing system. Cloud computing offers a way for students, instructors, and administrators to perform their duties effectively at reduced cost by utilizing the available cloud-based services offered by the cloud service providers. This solution provides the educational institution with very low entry cost, better security, automation, shorter deployment time and worldwide availability of resources. Although many articles already address these issues, this paper discusses on the concept, features, and application of cloud computing with particular focus on its implementation and benefits to the educational institutions.

Fog computing (FC) is a new architecture that aims to reduce network pressures throughout the core network as well as the cloud computing (CC) by bringing resource-intensive functions like computation, analytics, connectivity, also storage, nearest to the clients. In their operations, FC systems can make use of intelligence features to reap the benefits of data that is readily accessible with computing resources to be able to resolve the problem of excessive energy use with power for Internet-of-Things (IoT) apps that require speed. It generates large volumes of data, prompting the creation of a growing number of FC apps and services. Furthermore, Deep Learning (DL), an important field, has made significant progress in a variety of research areas, including robotics, face recognition, neuromorphic computing, decision-making, computer graphics, and speech recognition. Several studies have been suggested to look at how to use DL to solve FC issues. DL has become more common these days to improve FC apps as well as provide fog services such as security, resource management, accuracy, delay, and energy reduction, cost, data processing, and traffic modeling. The current review paper will focus on how to provide an overview of DL functions throughout the FC sector. The DL implementation for FC has evolved into powerful clients with services at the highest level, allowing for deeper analytics and mission answers that are more intelligent.

Due to the expansion growth of the IoT devices, Fog computing was proposed to enhance the low latency IoT applications and meet the distribution nature of these devices. However, Fog computing was criticized for several privacy and security vulnerabilities. This paper aims to identify and discuss the security challenges for Fog computing. It also discusses blockchain technology as a complementary mechanism associated with Fog computing to mitigate the impact of these issues. The findings of this paper reveal that blockchain can meet the privacy and security requirements of fog computing; however, there are several limitations of blockchain that should be further investigated in the context of Fog computing.

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically , we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities.

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013–2018, to identify and classify the architectures, infrastructure , and underlying algorithms for managing resources in fog/edge computing.

In the following article we will introduce the reader to a line of investigation that have been gaining greater interest in the last few months and which may define the origins to the expansion of this line of investigation, Fog Computing.
Cloud computing has been gaining more importance during the last decade . Yet Cloud Computing is a mere definition of a common known telecommunication structure, the Server-Client system.

A Server-Client relationship, in which Clients rely on Servers to either view information as well as save such information, is the core philosophy of the Internet and the W3C (World Wide Web Consortium)  since its public release.

Yet a certain evolution has been noticed in the last two decades, mainly in the speed in which content transactions have been taking place. The evolution of such speed increment can be seen in latest communication algorithms, and shows us that from the Internet conception to our present time, speed in communication suffered a significant number of changes .

Physical world integration with cyber world opens the opportunity of creating smart environments; this new paradigm is called the Internet of Things (IoT). Communication between humans and objects has been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications in business applications focusing in interoperability between machines (i.e., IIoT is a subset from the IoT). Number of daily life things and objects connected to the Internet has been in increasing fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity, dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete, and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge extraction, and search techniques based on Information Communication Technologies (ICT), such as data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data analysis and search in the future IoT presenting a framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and
presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications due to ICT integration.

Emerging technologies like the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. One of the key challenges in running IoT applications in a Fog computing environment are resource allocation and task scheduling. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This survey will help the industry and research community synthesize and identify the requirements for Fog computing. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architecture and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction for the Fog computing paradigm.

Vehicular networks and the recent Internet of Vehicles (IoV) are continuously developing, aiming to solve the current and novel challenging needs in the domain of transportation systems. Edge computing offers a natural support for Internet of Vehicles, supporting fast response, context awareness, and minimization of the data transfer to the centralized data centers-all these being allowed by the edge computing availability close to mobile vehicles. Multi-access (Mobile) Edge Computing, fog computing, cloudlets, etc., are such candidates to support IoV; their architectures and technologies have overlapping characteristics but also differences in approach. A full convergence between them has not yet been achieved. Also, it is still not completely clarified which solution could be the best trade-off to be adopted in the Internet of vehicles context and for which use cases. This paper is not a complete survey, but attempts a preliminary evaluation of some of the currently proposed Mobile Edge Computing and fog computing solutions for vehicular networks.

By leveraging cloud computing architecture and services to do centralized computation, especially when Internet of Things (IoT) scenarios want to react from insights resulted from that computation back to end devices, then we run into actual limitations of bandwidth congestion and the resulted high latency. Edge Computing come to existence with different implementations to gradually remove these barriers on limitations. Current networks relate to edge computing success to make an advance in their services to end users such as the shift from current 4G network infrastructure to 5G enhanced one. This paper aims twofold: Firstly, a review of the concepts and technology of edge computing is given. This includes cloud computing, the emerging edge computing and its implementations, comparison between existing terminologies, and an overview of wearable devices scenarios. Secondly, the paper presents an investigation of two promising technologies that have been adopted to implement the edge computing setup in our project. These are Azure cloud service and Raspberry Pi edge devices. Keywords: Cloud computing; edge computing; fog computing; IoT; Azure services. RESUMEN Al aprovechar la arquitectura y los servicios de computación en la nube para hacer un cómputo centralizado, especialmente cuando los escenarios de Internet de las cosas (IoT) quieren reaccionar a partir de los conocimientos resultantes de ese cómputo de regreso a los dispositivos finales, entonces nos topamos con limitaciones reales de congestión de ancho de banda y la alta latencia resultante. Edge Computing llega a existir con diferentes implementaciones para eliminar gradualmente estas barreras a las limitaciones. Las redes actuales se relacionan con el éxito de la informática de punta para hacer un avance en sus servicios a los usuarios finales, como el cambio de la infraestructura de red 4G actual a una mejorada 5G. Este documento tiene dos objetivos: en primer lugar, se ofrece una revisión de los conceptos y la tecnología de la informática de punta. Esto incluye la computación en la nube, la computación de borde emergente y sus implementaciones, la comparación entre las terminologías existentes y una descripción general de los escenarios de dispositivos portátiles. En segundo lugar, el documento presenta una investigación de dos tecnologías prometedoras que se han adoptado para implementar la configuración informática de borde en nuestro proyecto. Estos son el servicio en la nube de Azure y los dispositivos de borde Raspberry Pi. Palabras clave: Computación en la nube; computación de borde; computación de niebla; IoT; Servicios de Azure.

Serverless computing has gained importance over the last decade as an exciting new field, owing to its large influence in reducing costs, decreasing latency, improving scalability, and eliminating server-side management, to name a few. However, to date there is a lack of in-depth survey that would help developers and researchers better understand the significance of serverless computing in different contexts. Thus, it is essential to present research evidence that has been published in this area. In this systematic survey, 275 research papers that examined serverless computing from well-known literature databases were extensively reviewed to extract useful data. Then, the obtained data were analyzed to answer several research questions regarding state-of-the-art contributions of serverless computing, its concepts, its platforms, its usage, etc. We moreover discuss the challenges that serverless computing faces nowadays and how future research could enable its implementation and usage.