Fog computing is a term created by Cisco in 2014 describing the decentralization of computing infrastructure, or bringing the cloud to the ground. 1115 May 2015; pp. Sonmez C., Ozgovde A., Ersoy C. EdgeCloudSim: An environment for performance evaluation of Edge Computing systems; Proceedings of the 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC); Valencia, Spain. Efficient search keywords were used; these keywords cover how edge computing serves the IoT in many fields, such as IoT applications and challenges. Scaled-down, on-premises edge servers or datacenters can be easily moved and fit into comparatively small remote locations. Received 2020 Oct 2; Accepted 2020 Nov 6. The use of micro data centers dates to the early days of distributed computing, when different departments in the company used servers to process their own data. Chiang M., Zhang T. Fog and IoT: An overview of research opportunities. Mapping includes breaking down each ECA-IoT into its components and matching each component with the corresponding layer in each IoT model whenever applicable. For each ECA-IoT, we list the components of each architecture, the task of each component, and their corresponding layer(s) in each IoT model if applicable. A key-management scheme for distributed sensor networks; Proceedings of the 9th ACM Conference on Computer and Communications Security; Washington, DC, USA. links or advertisements. Classification of ECAs-IoT and sensitivity-to-delay category. IoT middleware: A survey on issues and enabling technologies. This taxonomy considers five categories in order to classify IoT applications. With this burst of new computing models and the previously unimagined innovations that are going to follow, comes the impetus for vast change across industries. Currently it is widely used in the following industries: Autonomous vehicles and charging stations: Not only does edge computing drive vehicles but it helps with the planning, predicting, monitoring, and management of charging stations for electric vehicles too. The second category covers the number of layers, and this is discussed thoroughly in Section 6; different IoT applications require different layer functionality. A Survey of IoT Applications in Blockchain Systems: Architecture, Consensus, and Traffic Modeling. Table 1 summarizes these surveys according to the dimensions of focus addressed by each survey. Transferring activity recognition models in FOG computing architecture. Rolls-Royces autonomous commercial ships. Jiang Y., Huang Z., Tsang D.H. They reside in homes and offices, atop buildings and other structures, out in the frozen tundra, in the depths of jungles, around swamps and farmlands, and even in space. This edge platform is responsible for the following tasks: performing virtualization processes through converting physical devices into virtual devices; dynamically adjusting cloud load by performing some services on the edge layer; ensuring the IoT reliability by cooperating with the edge network at the data level, and establishing the service-parsing template that is responsible for storing the matching information and parsing strategies. IFogStor system architecture consists of three main classes of actors shown in Figure 4: (1) data hosts, specialized nodes that store IoT data, which could be a fog node or a data center. The Inevitable Rise of Intelligence in the Edge Ecosystem, Deloitte on Cloud, the Edge, and Enterprise Expectations, Exploring Edge Computing as a Complement to the Cloud. IoT devices produce information from moving trucks, from machines on assembly lines, from drones in the field, or from telecommunications towers that are many miles away. Edge architecture is a distributed computing architecture that encompasses all the components active in edge computingall the devices, sensors, servers, clouds, etc.wherever data is processed or used at the far reaches of the network. Simulation results show that this architecture improves a VANET in terms of security and orchestration. The authors in [137] proposed a secure ECA-IoT in order to secure edge devices without the need to re-engineer the applications installed in edge devices by integrating embedded virtualization with trust mechanisms. This section presents the main challenges that face ECAs-IoT: Because of the nature of IoT devices and networks [107] IoT security challenges require different mechanisms compared to normal networks. However, when equipping edge devices with machine-learning capabilities, edge devices become more intelligent by increasing their ability to analyze data and make decisions without the need to connect to the cloud [70]. The initial browsing for related articles was to answer RQ1; then, we found that a great number of IoT challenges were covered by ECAs-IoT. Table 4 presents ECAs-IoT that address partially one or more security aspect and compares among them in terms of deployed techniques, security issues addressed, and architecture weaknesses. Table 18 summarizes this section by illustrating existing ECAs-IoT and potential IoT applications that they can serve. Section 2 provides the methodology of writing this survey. IoT networks generate a huge amount of data [113]. These mobile, self-contained units establish interoperable communications for first responders in emergency situations. Were the worlds leading provider of enterprise open source solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Petrolo R., Loscri V., Mitton N. Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms. ECAs-IoT face issues in placing IoT data in the appropriate edge nodes. Lao L., Li Z., Hou S., Xiao B., Guo S., Yang Y. This paper introduces the ECA-IoT concept and surveys current ECAs-IoT and possible research opportunities. Automating edge workloads with Red Hat Ansible Automation Platform can help you simplify IT tasks, lower operational expenses, and deliver smoother customer experiences across highly distributed edge architectures. 2326 June 2014; pp. 
First, a set of smart homes that are grouped on the basis of their cities. This takes advantage of using vehicles as fog nodes, and even parked vehicles could be used as fog nodes. Retail Applications: In-store item price checking, mobile app checkouts, passive in-store checkouts, and product suggestions offered in-store according to available inventory are use cases in retail for computer vision and other technologies coupled with edge computing. [108] proposed a taxonomy of fog-computing simulation-environment modeling. This category classifies IoT applications based on their main security requirements when considering the security triad CIA. The authors declare no conflict of interest. 16. Tong L., Li Y., Gao W. A hierarchical edge cloud architecture for mobile computing; Proceedings of the IEEE INFOCOM 2016The 35th Annual IEEE International Conference on Computer Communications; San Francisco, CA, USA. Immediate revenue models include any that benefit from greater data speed and computational power near the user. Table 14 classifies IoT applications on the basis of their security requirements. Daily life objects could be equipped with networking, sensing, and identifying capabilities [24]. For your security, if you're on a public computer and have finished using your Red Hat services, please be sure to log out. Although most applications involve some level of analysis, we also did not list analysis as the main function, unless it was a core function in the application. Data analysis is done by applying different machine-learning techniques on the basis of the generated data type from the sensors. Walmarts Gatik built delivery truck and Comparison between management-based architectures to manage an IoT network. The received data are trained and predicted on the basis of the new set of queries that were generated by the fog center. Mouradian et al. and S.A.; visualization, S.H., M.A. Ren et. 5862. Gia T.N., Tcarenko I., Sarker V.K., Rahmani A.M., Westerlund T., Liljeberg P., Tenhunen H. IoT-based fall detection system with energy efficient sensor nodes; Proceedings of the 2016 IEEE Nordic Circuits and Systems Conference (NORCAS); Copenhagen, Denmark. Further, companies are going to have more control over decisions in your private life even if that isnt their intention. Singh D., Tripathi G., Jara A.J. The site is secure. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications. 17 August 2012; pp. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Ngu A.H., Gutierrez M., Metsis V., Nepal S., Sheng Q.Z. 18. They provide the same components as traditional data centers but can be deployed locally near the data source. Fog computing architecture, evaluation, and future research directions. Xu X., Fu S., Qi L., Zhang X., Liu Q., He Q., Li S. An IoT-oriented data placement method with privacy preservation in cloud environment. At the top of cloud orchestrator, an IoT-aware global service orchestrator (GSO) is responsible for orchestrating global end-to-end services. Due to the constraints of sending large troves of unsifted data across the internet, the decision in most of these cases has been for the drone to collect the data itself on solid state drives, and then for those drives to be offloaded onto servers in field offices where the data is processed and stored. The output of those edge devices is separated into two parts: reports that are the results of analyzing the data, and feedback to the infrastructure to respond to threats that were monitored by sensors. automticamente. Bethesda, MD 20894, Web Policies Cisco products and solutions can help you get started with edge computing. Classification of IoT applications within application function category. The architecture in [130] enhances resources management in VANET. An IoT network is a system of associated and diverse devices, for example, vehicles and home appliances with abilities, such as communication and data transfer over the network. We classified ECAs-IoT according to the following criteria: In addition to providing an overview of edge-computing technology with a specific focus on edge computing for IoT, this survey extends the current literature in the following ways: Figure 1 shows the overall road map of the paper. 2427 October 2017; pp. Each node is responsible for a small associated community which is responsible for analyzing IoT data and providing services in a timely manner. [141] proposed an ECA-IoT to enhance smart cities. Extensive experiments were done while using the MATLAB platform in order to evaluate their architecture. Table 7 shows a detailed mapping between the surveyed ECAs-IoT and the two common IoT layering models. 11441151. View users in your organization, and edit their account information, preferences, and permissions. Ray P.P. Table 12 classifies IoT applications on the basis of their sensitivity to delay, whether these applications are highly, moderately, or lowly sensitive to delay, and it shows that the same IoT application could be sensitive to delay or not, depending on the case that it handles. This gives a guideline for IoT application designers and developers to pick the right ECA for their application according to the delay requirements. Nunna S., Kousaridas A., Ibrahim M., Dillinger M., Thuemmler C., Feussner H., Schneider A. This immediately implies the lack of support for upper layers such as heterogeneity and management. Vgler M., Schleicher J., Inzinger C., Nastic S., Sehic S., Dustdar S. LEONORElarge-scale provisioning of resource-constrained IoT deployments; Proceedings of the 2015 IEEE Symposium on Service-Oriented System Engineering; San Francisco, CA, USA. ODonovan P., Gallagher C., Bruton K., OSullivan D.T. 5057. Edge of things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment.
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