Integration of Cloud Computing with Internet of Things. Группа авторов

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СКАЧАТЬ delivers workshops, testbeds, and reports, along with its unique asset. The concept of a service layer on top of a connectivity layer—contributes significantly to the overall Industry 4.0 framework and industrial internet as a whole, ushering in a new wave of digitalization which will mark the beginning of Industry 4.0.

      SI has been introduced in the latest specification of oneM2M standard Release 2 [Swetina]. It allows the posting, distribution, and reuse of meta-tagged data through a gateway by providing timely notifications to interested clients or entities available in semantic discovery.

OneM2M acts as a software middle layer, by interconnecting devices with their respective application–infrastructure entities (cloud-based), independently from their underlying transport networks. In effect, it creates an abstraction layer that allows application developers to create value from their business and operational applications without having to deal with the technical protocols for connecting to and managing devices. The standard solves the problem of implementation variances for common service functions. Its technical specifications provide a global standard for the basic functions, such as device management, security, registration, and so forth. The use of oneM2M specifications in field-deployed devices ensures data and vendor interoperability. Furthermore, oneM2M provides global standardized APIs on the application-infrastructure side, where customers can interact with their device and/or even their platform. On the device side, oneM2M’s APIs help developers tailor applications for their specific purpose without the need to master technical details about the underlying connectivity networks. To enable end-to-end communication across different verticals, oneM2M provides the tools that enable various interworking possibilities. One approach is to map data models for shop-floor machines and sensors as oneM2M resource structures and vice versa. Since such inter-working definitions are available for other verticals, such as automotive and rail, different verticals can communicate with each other relatively easily. The primary aim of oneM2M is to standardize the common services necessary to deploy and operationally support IoT applications across multiple verticals. This implies a horizontal focus, aiming for a high degree of reuse and cross-silo interoperability. Vertical sector requirements are also important to oneM2M standardization participants. In the manufacturing and industrial sectors, oneM2M established a liaison with the Industrial Internet Consortium. OneM2M is also actively involved with the Open Connectivity Foundation, targeting interworking opportunities for consumer IoT applications. OneM2M’s standardization continues to address new frontiers for interoperability and interworking with the development of its latest specifications, Release 4. Release 4 will encompass industrial, vehicular, and fog/edge architectures. It also lays the groundwork for semantic interoperability and tools to help user adoption.

      We all know that SIoT is taking place in modern applications for industries and is developing very fast making analytics crucial to ensure security. Since the SIoT analytics operates using the cloud and electronic instrumentation, it requires programmers to control and access IoT data. IT professionals approaching IoT analytics should capture data via packets in automated workloads, also known as flow. Flow is the sharing of packets with the for example, if you stream a video on the internet, packets are sent from the server to your device. This is flow in action. NetFlow and sFlow are both tools that monitor network traffic. IT pros are still creating methods to capture the flow of data for analysis of IoT data. The number of cloud companies has increased, and as networks continue to grow, it’s very risky to carry down the large visibility gap for capturing data. Because of huge data traffic, many cloud companies have started to send information through their networks via IP Flow, sFlow, and NetFlow. When you start to capture IoT specific data, there are several advantages. The data gets standardized into industry-accepted data, and once the data is observed from the gateway, it can be correlated with traffic data coming out from the data center or cloud services in use. Every cloud environment can create flow by generating and exporting the data. For example, a few IT equipped companies such as Amazon, Google, Microsoft Azure have incorporated these attributes in different applications to facilitate the industries and consumers. Amazon is a popular platform for cloud services which takes into account both the cost and frequency response of the network. It has many features to enhance the IoT platform and can support many devices. This platform uses flow as the mechanism to communicate. The service is handled by the virtual private cloud. It comes across under certain levels such as ports, networks, traffic levels, and some other communication networks. Data gets stored using the CloudWatch logs in JavaScript Object Notation (JSON). Similarly, Google is popular in every technology. Google Cloud IoT Core is a fully managed service that allows you to handle easily and secure the connection with manages and ingests data from millions of globally dispersed devices. The data flow is run by logging the Stack driver. And the performance of the network operates with good latency. It handles large data which works still fine. Similarly, the ‘Microsoft Azure’ flows under a secured network system. The flow logs are work or travel in a flow and stored into Azure storage in the format of JSON. The data from the devices have been stored in a method of real-time data.

For example, the use of some sort of standards to announce each printer and its attributes is one way to elevate such an issue and there are so many such attributes.

      Today, each vertical industry comes along with its protocol and specifications bodies to develop their data models. For example, in the industrial automation industry, organizations like OPC are working on data models and objects which can be used on the shop floor. In the automotive industry, ETSI’s Intelligent Transport Systems technical committee is working collaboratively to define messages and data models for communication between cars. Many IoT applications also involve several partners in a distributed value chain. For instance, an intelligent application for an industrial plant might automatically order feedstock from one or more partners for its production line. Supplies are typically ordered and delivered by several partners. It is easy to see how this scenario can end up in up in an “island of things” configuration since different partners in the value chain belong to different verticals, each with their specific data models. It is thus desired to make sure the cross-availability of IoT devices, services, and data for the growth of new business and the emergence of opportunities. This can assist managing data from multiple sources, generate new avenues, and innovate suitable solutions for the existing service providers to scale new markets.

      1.5.2 Semantic Interoperability (SI)

      The last decade witnessed a many-fold increase in a host of heterogeneous devices, actuators, sensors, etc. with varied applications in the IoT platform. To cope up with the smart environment, an efficient distribution, monitor, support, coordination, control, and communication among these sensors remains essential that gives rise to the term interoperability. The interoperability can be achieved with the following major layers as shown in Figure 1.12.

      Technical interoperability is concerned with the communicability among the things or objects in IoT domain using the software and hardware. On achieving the suitable connectivity, the syntactic interoperability deals with the data models, data formats, data encoding, communication protocols, and serialization techniques using certain specified standards. Finally, Semantic interoperability establishes the desired meaning to the content and assists to comprehend of the shared unambiguous meaning of data. The interoperability concept can be better visualized using the five major perspectives and is given in Table 1.2.

      Figure 1.12 Different layers of interoperability.

Table 1.2 Taxonomy of interoperability: major perspectives.

Taxonomy of interoperability	Attributes
Device interoperability [19]	Involves both the low and high-end devices High-end devices are Raspberry Pi, smartphones, etc. with good computational abilities and СКАЧАТЬ В начало < 10 11 12 13 14 15 16 17 18 19 > В конец