Question

3)Edge (Fog) Computing (Data element analysis and transformation): You considered all possibilities that pertain to this level, and express how you tested this level was secure. You provided evidence to justify your claims

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Answer 1

Edge Computing, a category of Fog Computing that focuses on processing and analysis at the network node level. Edge computing brings computation and data storage closer to the devices where it’s being gathered, rather than relying on a central location that can be thousands of miles away. With the development of the Internet of Things (IoT), a device could recognize the environment and conduct a certain function by itself. Therefore, roles of a computing model that controls IoT devicesare paramount. IoT devices mainly consist of sensors. Edge computing was developed due to the exponential growth of IoT devices, which connect to the internet for either receiving information from the cloud or delivering data back to the cloud. Transferring and processing huge amount of datat hrough cloud computing caused several issues such as delayed service response time. In addition, the number of application area of a sensor network is expanding and the demand on real-time processing and transferring of information to control a device of IoT is also increasing. Edge computing is a part of a distributed computing topology in which information processing is located close to the edge, where things and people produce or consume that information.

Edge (Fog) computing bridges the gap between the cloud and end devices i.e.IoT nodes by enabling computing, storage, networking, and data management on network nodes within the close vicinity of IoT devices. In addition, fog computing services that have been developed so far consist of data creation, processing and transfer. Fog nodes are wide-spread and geographically available in large numbers. In fog computing, security must be provided at the edge or in the dedicated locations of fog nodes, as opposed to the centrally-developed security mechanisms in dedicated buildings for cloud data centers. The decentralized nature of fog computing allows devices to either serve as fog computing nodes themselves. Fog enables repeatable structure in the edge computing concept, so enterprises can push compute out of centralized systems or clouds for better and more scalable performance.

Edge-computing hardware and services help solve this problem by being a local source of processing and storage for many of these systems. An edge gateway, for example, can process data from an edge device, and then send only the relevant data back through the cloud, reducing bandwidth needs. Or it can send data back to the edge device in the case of real-time application needs.

Edge computing is a concept that contrasts with cloud computing. Cloud computing is a way to communicate directly with a central data center, whereas edge computing communicates primarily with the so-called “edge data center,” which is located near the device, and leaves secondary work to the central cloud. In other words, edge computing is a computing topology concept. Physical devices and controllers that might control multiple devices. Devices are diverse, and there are no rules about size, location, form factor, or origin. Communications and connectivity are concentrated in Level 2. One objective of the IoT Reference Model is for communications and processing to be executed by existing networks. Connectivity includes:

• Communicating with and between the Level 1 devices
• Implementation of various protocols
• Translation between protocols
• Security at the network level

Increasingly, though, the biggest benefit of edge computing is the ability to process and store data faster, enabling for more efficient real-time applications that are critical to companies. The functions of Level 3 are driven by the need to convert network data flows into information that is suitable for storage and higher level processing at Level 4 (data accumulation). This means that Level 3 activities focus on high-volume data analysis and transformation. For example, a Level 1 sensor device might generate data samples multiple times per second, 24 hours a day, 365 days a year. A basic tenet of the IoT Reference Model is that the most intelligent system initiates information processing as early and as close to the edge of the network as possible. This is sometimes referred to as fog computing. Level 3 is where this occurs. It is clear that while the initial goal for edge computing was to reduce bandwidth costs for IoT devices over long distances, the growth of real-time applications that require local processing and storage capabilities will drive the technology forward over the coming years. Level 3 processing includes :

• Evaluation: Evaluating data for criteria as to whether it should be processed at a higher level
• Expanding/decoding: Handling cryptic data with additional context
• Reducing and summarizing data to minimize the impact of data and traffic on the network and higher-level processing systems
• Finally, Assessment: Determining whether data represents a threshold or alert; this could include redirecting data to additional destinations.