A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

Cloud computing is paramount in information technology, and cloud computing has revolutionized the traditional ways of dealing with IT resources. Fog computing can be used in both large cloud systems and big data structures which is used for solving growing difficulties in information technology. Cloud computing and fog computing are used for large amount of storage, data and applications for the end users. Compared to cloud, fog computing is near to the end users. The major difference between cloud computing and fog computing is that cloud computing is centralized system and fog computing is distributed decentralized infrastructure. Many faults occur in cloud and fog computing, and there are various methods for fault tolerant techniques.

As the call for data is rapidly increasing all over the globe it is necessary to deal with data safely to deal with data, we need some services like cloud and fog computing. Cloud computing and fog computing are the computing platforms which provide firm to run the communication without any kind of disturbance. This paper gives overview of cloud computing and fog computing. The survey of this paper focuses on cloud and fog computing and comparison of those both computing platforms. As we can easily analysis the best computing platform. As fog computing is the extended part of cloud computing that is it is add-on for the cloud computing platform. Cloud functions over internet it has many chances of collapsing while fog is more secure when compared to cloud as it has different protocols and standards in it and this fog have less chances of collapsing. Here fog computing security is higher when compared to cloud computing. This paper gives the review and analysis on fog and could computing.

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

The integration of Smart Grid (SG) with cloud and fog computing has improved the energy management system. The conversion of traditional grid system to SG with cloud environment results in enormous amount of data at the data centers. Rapid increase in the automated environment has increased the demand of cloud computing. Cloud computing provides services at the low cost and with better efficiency. Although problems still exists in cloud computing such as Response Time (RT), Processing Time (PT) and resource management. More users are being attracted towards cloud computing which is resulting in more energy consumption. Fog computing is emerged as an extension of cloud computing and have added more services to the cloud computing like security, latency and load traffic minimization. In this paper a Cuckoo Optimization Algorithm (COA) based load balancing technique is proposed for better management of resources. The COA is used to assign suitable tasks to Virtual Machines (VMs). The algorithm detects under and over utilized VMs and switch off the under-utilized VMs. This process turn down many VMs which puts a big impact on energy consumption. The simulation is done in Cloud Sim environment, it shows that proposed technique has better response time at low cost than other existing load balancing algorithms like Round Robin (RR) and Throttled.

The ever-changing rapid paradigm shift from one technology to another has led to extensive research in interdisciplinary fields. The different technologies are enhancing and transmuting at such an exponential rate that it has become important to effectively manage all the data and related environments associated with them. The aim of this chapter is to contribute to understanding the significance of fog and cloud computing and the related Internet of Things (IoT) networks supporting these technologies in real-time applications. This chapter provides insights on how the IoT frameworks provide increased scalability, increased performance, and immediate pay-as-you-go payment options to both the providers and consumers. This chapter aims to provide detailed information about IoT networks which can be employed in cloud and fog computing. The chapter seeks to provide the readers a brief description on introduction of IoT, IoT networks, cloud computing, and fog computing and their integration with IoT. A detailed comparison between cloud computing and fog computing paradigms throws light on the distinctions between the two paradigms. This chapter highlights the implementation of fog and cloud computing using IoT in detail and also highlights the security issues, threats and concerns related to the discussed technologies and their implementations. We have also explored the IoT network protocols used for the implementation of fog computing and cloud computing using IoT, along with few case studies which underline the applications and examples with respect to the technologies. Since the power and potential of IoT is being realized, major business giants are investing heavily in the technology as the gains are long termed. As per recent reports, the fog computing market is expected to reach an approximate 18 to 20 billion $ by 2022. Also, fog computing has become a necessity only for IoT and cloud, but also for embedded artificial intelligence, it has become an integral part of major interdisciplinary works. And since the technology is acting as the backbone of the industries, it cannot be ignored now and it is here to stay. It is expected that fog computing is anticipated to embark into already existing software, devices as Fog as Service (FaaS).

Internet of Things (IoT) generates a myriad amount of data, which is sent over the Cloud computing infrastructure for analytics and Business Intelligence. This application scenario suffers network delays, transmission delays and delays in decision making. Due to these drawbacks, the Cloud-based IoT infrastructure is not suitable for time-critical health care applications. To overcome this problem, a smart way is introduced called “Fog Computing” - a LAN based processing approach which has multiple advantages. When IoT, Fog and Cloud Computing are combined, the resultant system’s performance is far better. Hence, the combination results in a very efficient Health Care system. Fog and Cloud Computing have their dimensions that not only support each other but also explore many new application domains. In this paper, the real-time ElectroCardioGram (ECG) based Health Care system is implemented in Cloud and Fog Computing. Different Quality of Service (QoS) parameters like memory consumption, transmission delays, computation delays, network delays, Carbon dioxide emission, data transferred and response time are measured, analyzed and improved to make the system more efficient. Based on the Fog computing characteristics and capabilities, the Raspberry Pi 3 B+ model is configured as a Health Care serving gateway by using different installation and configuration steps. Initially, the proposed system is tested for one patients ECG data analysis over cloud and Fog. In every set up all QoS parameters are measured and later the system is subjected to multiple ECG streams for varying numbers of patients to find the limitations of the Raspberry Pi node as a Fog Computing node. The obtained results show that for more number of ECG streams the Fog node is not able maintain QoS in decision making time. Every QoS parameter is explored in detail for decision-making time. In the end, the Fog computing based proposed system is concluded for its pros and cons and future aspects of the Fog node are discussed to make better systems.

There is much hype about the emergence of Cloud and Fog Computing in achieving a truly pervasive access to information and computing resources. Given the increased need for the instantaneous access and analysis of data with huge dimensions (big data, with respect to volume, velocity and varied formats) and the emergence of predictive analytics, individuals and businesses will experience over-reliance on Fog Computing. Despite the promise, scholars and researchers are finding it difficult to distinguish between Cloud and Fog Computing and find little time in investigating the key issues that affect these emerging computing models. In this context, the aim of this chapter is to explore the formulaic definitions and key issues surrounding Cloud and Fog Computing. Using a meta-analysis of recent literature on Cloud Computing literature and authors’ experience, this chapter presents a development projectile and emerging dimensions of computing. The chapter articulates how Fog Computing can be used at the centre of instantaneous ubiquitous data and information management which is a key requirement for competitive business entities in any given setup. At the end of the chapter, we discuss Fog Computing landscape in Africa.

Huge growth in the scale of data generated through cloud computing has been led to Internet of Things (IoT). Fog computing has been recently adopted to improve some features of cloud computing and makes cloud computing more attractive to users. Furthermore, it comes to improve some parameters such as latency, security and load network. The combination of cloud and fog computing is seen a new progress in distributed computing and the appropriate platform for the data. Fog computing is defined as a new paradigm that works at the edge of the network to improve the quality of the network. In this paper, the use of fog computing in cloud computing is reviewed in this work. The characteristics, architectures and discussions and the relationship is further elaborated. Additionality, recommendation for further research is discussed. Keywords: Fog Computing, Internet of Things, Cloud Computing, Big Data, Edge Computing, CloudSim

Smart health care is the recent buzzword in the healthcare environment. Involvement of technology in the healthcare domain provides a platform for remote real-time monitoring and self-management of patients’ health. The cloud computing is much utilized for developing technological solutions for an efficient smart healthcare system. Since cloud is a centralized environment, its response time becomes an issue in supporting latency-sensitive obligation in real-time applications. Most solutions in the e-health field depend on immediate decision making over real-time data where latency cannot be tolerated. Fog computing is a distributed infrastructure that overcomes this weakness of cloud computing by having the facility of local storage and processing in providing immediate response for decision making. Hence, thereby, it reduces the network latency and bandwidth usage by decreasing the amount of data sent to the cloud. This combination of fog computing and cloud computing can able to employ a reliable and efficient distributed e-health applications. This paper presents a literature review on the common use of cloud computing and fog computing for providing solutions in health care, along with the challenges that exist in providing solutions through fog computing.KeywordsCloud computingHealth careInternet of ThingsFog computingE-health

Minimizing the electricity consumption and cost is one of the most demanding needs of today. As with the rapid increase in demand, there is a great need to design new solutions for effective energy management. With the advent of new Information Communication Technologies (ICT) traditional electricity grids, meters, buildings and appliances became Smart Grids (SGs), Smart Meters (SMs), Smart Buildings (SBs) and Smart Appliances (SAs). The SBs consists of a large number of SAs. These smart appliances are constantly sharing, their data with SGs, SMs and SBs. So a huge amount of data is generated every day. This data requires complex computations, faster retrievals and larger storage facilities [1]. Keeping this in view, a new energy management system is designed with the help of Cloud and Fog computing. As the Cloud Computing (CC) provides large number of data computation and permanent storage facilities however, it has limitations in fast data retrieval and causes response delays. On the other hand, Fog Computing (FC) offers faster information retrieval with less response delays with only limitation of temporary storage. The proposed system architecture integrates the qualities of both CC and FC by combining their services. To manage the load between different Virtual Machines (VMs) on Fog servers a new load balancing algorithm Modified Shortest Job First (MSJF) is the proposed. The performance of proposed algorithm is evaluated through different performance parameters. e.g. Processing Time (PT), Response Time (RT) and cost. To validate the performance of proposed scheme simulations are carried out in the Cloud Analyst tool. From the results it is assumed that the proposed technique can not outperforms the Round Robin (RR) and Throttled algorithms, due to its limitations in network delays and RT.

In Healthcare big data, data is originated from various heterogeneous sources. Numerous novel base particular healthcare applications offered to handling source of data from electronic health record (EHR) to medical images. Imaging, Electronic Health Report, technology in light of sensor and numerous different procedures created an immense measure of Healthcare data. Cloud computing development was an excellent paradigm to substantiate big data which incited find of imperceptible examples. Cloud computing is a developing new registering design intended to answer different contending administrations on the Web. Fog Computing is a design style in which arrange segments amongst devices and the cloud execute application-particular rationale. We in this paper investigate, characterize, and talk about various application of cloud and fog computing. We talk about the impact of cloud computing and fog computing on healthcare big data. Cloud base framework for Homediagnosis Service, Fog computing architecture and the justification of moving from cloud to Fog presented comprehensively in this paper.

In the world, many applications have been utilized with distributed manner and as a result, resources have been effectively utilized irrespective of different places. In distributed computing various subsections have been introduced like grid computing, cloud computing, and fog/edge computing. In cloud computing various services are introduced and are Infrastructure as a Service (IaaS), Software as Service (SaaS), Platform as a Service (PaaS), Security as a Service (SECaaS), and so on. Even though major advantages have been affluently available from cloud computing, the processing and computation has to be carried out remotely in a distributed manner. On the other hand, fog computing plays inevitable latency and bandwidth reduced distributed services are provided. Even though both cloud computing and fog computing provide various services using distributed resources, it is important to realize how energy is consumed and what percentage of consumption is used for what services. In this chapter, how energy is optimized and what techniques are available in cloud and fog computing are discussed.

Recently, the oil and gas industry faced several crucial challenges affecting the global energy market, including the Covid-19 outbreak, fluctuations in oil prices with considerable uncertainty, dramatically increased environmental regulations, and digital cybersecurity challenges. Therefore, the industrial internet of things (IIoT) may provide needed hybrid cloud and fog computing to analyze huge amounts of sensitive data from sensors and actuators to monitor oil rigs and wells closely, thereby better controlling global oil production. Improved quality of service (QoS) is possible with the fog computing, since it can alleviate challenges that a standard isolated cloud can't handle, an extended cloud located near underlying nodes is being developed. The paradigm of cloud computing is not sufficient to meet the needs of the already extensively utilized IIoT (i.e., edge) applications (e.g., low latency and jitter, context awareness, and mobility support) for a variety of reasons (e.g., health care and sensor networks). Couple of paradigms just like mobile edge computing, fog computing, and mobile cloud computing, have arisen in recently to meet these criteria. Fog computing helps to optimize services and create better user experiences, such as faster responses for critical, time-sensitive needs. At the same time, it also invites problems, such as overload, underload, and disparity in resource usage, including latency, time responses, throughput, etc. The comprehensive review presented in this work shows that fog devices have highly constrained environments and limited hardware capabilities. The existing cloud computing infrastructure is not capable of processing all data in a centralized manner because of the network bandwidth costs and response latency requirements. Therefore, fog computing demonstrated, instead of edge computing, and referred to as "the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IIoT services" (Shi et al., 2016) is more effective for data processing when data sources are close together. A review of fog and cloud computing literature suggests that fog is better than cloud computing because fog computing performs time-dependent computations better than cloud computing. The cloud is inefficient for latency-sensitive multimedia services and other time-sensitive applications since it is accessible over the internet, like the real-time monitoring, automation, and optimization of petroleum industry operations. As a result, a growing number of IIoT projects are dispersing fog computing capacity throughout the edge network as well as through data centers and the public cloud. A comprehensive review of fog computing features is presented here, with the potential of using it in the petroleum industry. Fog computing can provide a rapid response for applications through preprocess and filter data. Data that has been trimmed can then be transmitted to the cloud for additional analysis and better service delivery.

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).