Design and Application of Distributed Computing Environment Based on Data Drive

The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the data-generating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.

Improving consumer satisfaction in smart cities using edge computing and caching: A case study of date fruits classification

This chapter discusses the various security and privacy issues that prevail in Fog computing. The proposed Fog computing is distributed, latency sensitive and provides Quality-of-Service and location awareness to its end users. Edge computing and Fog computing are somewhat similar to each other and both the terms are used exchangeably; also, the main objectives of both of them are the same, to reduce the network and delay congestion. The chapter also discusses the related work in the load balancing domain of Fog computing. It investigates the current state-of-the-art works regarding load balancing and security issues in the Fog computing paradigm. Rahbari et al. proposed a hyper-heuristic algorithm for secure scheduling in Fog computing. The chapter examines the various security and privacy issues that prevail in Fog computing. In a Fog network, a huge amount of Fog nodes and Internet-of-Things devices are connected so it becomes important to have an identification of each individual device which is achieved by authentication.

IoT consists of millions of devices that range from voice assistants to smart meter, in store beacons, any touchable devices, etc., and these IoT devices are flooding the world. A study report says that it is expected to touch around 75 billion IoT devices by 2025. Huge amounts of data are being collected from all these devices and the problem is what to do with this data and how to analyse the data? The two possible solutions are edge computing and fog computing. Both the technologies leverage the computing capabilities to the local network. So that it is easy to carry out the heavy computation tasks locally within the network and this image is equal to the work carried out in cloud. Data can be processed in a fog node or in the IoT gateway which is located within the LAN in fog computing. Data can be processed in sensor or on any device itself in edge computing without transferring the data to anywhere. In such a way, the streaming analytics platform helps to build a model that helps to collect and analyse the data to infer the useful findings within an IoT device. Our proposed chapter will outline the contribution of edge and fog computing in streaming analytics or frameworks.

With the development of the Industrial Internet, technological innovation and iterative evolution in the industrial direction are getting faster and faster, and the amount of data generated by industrial equipment is also increasing explosively. The Industrial Internet itself is based on data and supported by intelligence. However, with explosive data, there is an irreconcilable contradiction between the simplicity of data storage and data collection and the scale of data. The system cannot fundamentally solve this contradiction, but through the components of the system itself, data collection can be made more portable under the premise of a large amount of data and ensuring data reliability. This system relies on the data format of industrial machinery and equipment, that is, structured time series data, which is stored in a time series database. In order to ensure data security, the system also uses distributed ceph block storage as the carrier of database storage to ensure the reliability of data storage.

Fog computing is a fundamental facilitating technology in the future of networks. It broadens cloud computing services to its network edge, so that fog computing is able to bear the load of different emerging applications like IoT, blockchain, and big data without incurring high latency or cost of bandwidth consumption. In order to reach the optimal capacity of fog computing, designing an incentive mechanism for its service provider is essential. Over the recent years, we have noticed steady growth in the adoption of Machine Learning not only for the enhancement of fog computing applications but also for providing fog services, which include better resource management, improved security, reduced energy consumption, latency and traffic modelling. There hasn’t been a study yet, which investigates the role of Machine Learning in fog computing paradigm and this is what our current research aims to shed light on. Machine Learning application used for fog computing needs high layers services profound analytics, a strong end-user as well as smart responses for its designated tasks. Here, in this paper we work on presenting a comprehensive study that underlines the current advancements in Machine Learning techniques associated with the management of three important aspects of fog computing: accuracy, resource, security, and, as well as highlighting the role of Machine Learning in edge computing.

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We'll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) has attracted considerable attention for the next generation optical access systems. Among potential applications of TWDM-PON, a major application is the support of mobile fronthaul streams between radio units (RUs) and distributed units (DUs) in the centralized radio access network (C-RAN) architecture, which consists of central units (CUs), DUs, and RUs. The upstream fronthaul traffic that an optical line terminal (OLT) receives is expected to become highly bursty due to the variable data rate generated by employing new functional split options and the synchronization of data transmission between neighboring RUs caused by time-division duplex (TDD). However, there has been no wavelength and bandwidth allocation scheme for TWDM-PON that is designed to efficiently accommodate fronthaul streams satisfying the strict delay requirement. Therefore, in this paper we propose a novel wavelength and bandwidth allocation algorithm that can minimize the number of active wavelength channels considering the high burstiness and delay requirement of fronthaul data transmission. Through computer simulations it was confirmed that the number of active wavelength channels can be reduced by 50% with the proposed algorithm, and thus more RUs can be efficiently accommodated using TWDM-PON.

Cloud, Edge, and Fog computing has recently attracted significant attention in both industry and academia. However, finding their definition in computing paradigms and the correlation between them is difficult. In order to support modern computing systems, the cloud, edge devices, and fog computing offer high-quality services, lower latency, multi-tenancy, mobility support, and many other features. Fog/edge computing is an emerging computing paradigm that uses decentralized resources at the edge of a network to process data closer to user devices, like smartphones and tablets, as an alternative to using remote and centralized cloud data center resources. Fog networking or fogging is one of the best used models recently. By addressing this issue, this work serves as a valuable resource for those who will come after. Initially, we present an overview modern computing models and associated areas of interest research. After that, we discuss each paradigm. After that, we go into great detail about fog computing, highlighting its exceptional function as the link between edge, cloud, and IoT computing. Finally, we briefly outline open research questions and future directions in Edge, Fog, Cloud, and IoT computing.

Health Information System (HIS) is a combination of hardware, software, process, infrastructure, people and protocol which is developed for management of healthcare data including collection, storage, analysis, decision making and transmission. It supports functional management of a hospital as well as provides an infrastructure that support policy decisions over healthcare data. HIS grown decade by decade starting from 1960 to present era and divided into four generation versions, starting from HIS. V.1.0 to HIS V.4.0. HIS V.4.0 is dedicated for smart medical technology with fog-based computing system including disease-oriented sensing system, IoT, artificial intelligence, high speed internet and advanced internet protocols. Fog Computing is a distributed computing framework and provide an extension to cloud computing. It lies between medical sensor (IoT devices) and cloud datacenters. This paper presents various HIS generations and technologies used in HIS V.4.0. Include review of cloud computing, IoT and Fog computing. Further this paper provides a systematic review of existing research in fog computing with various issue and challenges. Finally, paper describes comparison of fog computing with cloud and edge computing and present discussion, open challenges and recommendation in Fog-based HIS.

Fog computing (also known as edge computing) is a decentralized computing architecture that seeks to minimize service latency and average response time in IoT applications by providing compute and network services physically close to end-users. Fog environment consists of a network of fog nodes and IoT applications are composed of containerized microservices communicating with each other. Due to limited resources of fog nodes, it is often not possible to deploy all the containers of an application on a single fog node. Therefore, communicating containers need to be distributed on multiple fog nodes. Distribution and management of containerized IoT applications is always a critical issue to the system performance in a fog environment. Kubernetes, an open-source system, has grown into a container orchestration standard by simplifying the deployment and management of containerized applications. Despite the progress made by the academia and industry with respect to container management and the wide-scale acceptance of Kubernetes in cloud environments, container management in fog environment is still in the early stage in terms of research and practical deployment. This article aims to fill this gap by analyzing the expediency of Kubernetes container orchestration tool in the fog computing model. The paper also highlights limitations with the current Kubernetes approach and provide ideas for further research to adapt to the needs of the fog environment. Lastly, we provide experiments that demonstrate the feasibility and industrial practicality of deploying and managing containerized IoT applications in the fog computing environment.

Industrial Internet has been incrementally established in recent years to support smart manufacturing, which involves a collection of technology and value chain organization concepts. Along with the continuous development of the industrial Internet, an increasing number of threats have been exposed. In particular, the integration of traditional industrial settings and industrial Internet platforms leads to incompatible security countermeasures, making the industrial Internet vulnerable. This article comprehensively and deeply investigates the security challenges of the current industry Internet based on relevant research studies and realistic security scenarios. In response to our identified security challenges, we propose a data security transfer architecture solution for industrial platforms. Specifically, our proposal contains seven sub-modules, which enhances the industrial cloud platform with secure data security transfer technology to protect industrial data when it is connected to the cloud platforms and industrial equipment. To evaluate the utility of our proposal, we have specialized our security architecture solution to a realistic case of the industrial Internet.

Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet

Electronic gadgets, appliances, medical equipment, cameras, sensors, and cars are all part of the Internet of Things (IoT), which enables smart cities and infrastructure. It is predicted based on evidences that a minimum of forty nine billion IoT devices shall definitely be linked or connected with the Internet by or before 2020. Those devices will generate a huge and unprecedented amount of information, which conventional systems and cloud systems may find challenging to handle. Fog computing was created to address the shortcomings of existing technologies. Fog offers storage, computation, data, and application services at the network's edge, comparable to cloud computing. This article discusses the features that generally makes fog or edge computing a good stage for the recently evolved technology “IoT”, as well as application, characterization and interrelated research in the trending fields including IoT and Fog. Edge computing is a suitable platform for IoT because of its heterogeneous nature, mobility support, very low latency, and huge number of nodes. Rather of transmitting data to the cloud, data will be processed at the network edge.

Monitoring and maintaining equipment and production lines ensure stable production by detecting and resolving abnormalities immediately. In the Industrial Internet, operational technology and advanced information technology are fused to improve the digitalization and intelligence of monitoring and maintenance. This paper provides a comprehensive survey of monitoring and maintenance of equipment and production lines on the Industrial Internet. Firstly, a brief review of its architecture is given, and a reference architecture is summarized accordingly, clarifying the key enabling technologies involved. These key technologies are data collection technologies, edge computing, advanced communication technologies, fog computing, big data, artificial intelligence, and digital twins. For each of the key technologies, we provide a detailed literature review of their state-of-the-art advances. Finally, we discuss the challenges that it currently faces and give some suggestions for future research directions.