Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective Full Text

Real-time rail monitoringRailway tracks can be equipped with fog nodes. Fog computing is essential for devices that must do complex computations and processing. The function of fog nodes is determined by the type of data they receive.

Hence, the fog architecture may be physically more distant from the edge architecture, sensors, and actuators. The IIoT is composed of edge, fog and cloud architectural layers, such that the edge and fog layers complement each other. However, this distinction isn’t always clear, since organizations can be highly variable in their approach to data processing. Fog computing uses edge devices and gateways with the LAN providing processing capability.

These devices need to be efficient, meaning they require little power and produce little heat. WINSYSTEMS’ single-board computers can be used in a fog environment to receive real-time data such as response time , security and data volume, which can be distributed across multiple nodes in a network. The use of WINSYSTEMS’ embedded systems and other specialized devices allows these organizations to better leverage the processing capability available to them, resulting in improved network performance. The increased distribution of data processing and storage made possible by these systems reduces network traffic, thus improving operational efficiency. The cloud also performs high-order computations such as predictive analysis and business control, which involves the processing of large amounts of data from multiple sources. These computations are then passed back down the computation stack so that it can be used by human operators and to facilitate machine-to-machine communications and machine learning.

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Edge computing is an extension of older technologies such as peer-to-peer networking, distributed data, self-healing network technology and remote cloud services. It’s powered by small form factor hardware with flash-storage arrays that provide highly optimized performance. The processors used in edge computing devices offer improved hardware security with a low power requirement. WINSYSTEMS’ industrial embedded SBCs and data acquisition modules provide gateways for the data flow to and from an organization’s computing environments. It reduces the required bandwidth and also reduces the back and forth communication present between cloud and sensors that may negatively impact the performance of the Education IoT system .

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  • Beside the wide range of benefits that are offered with the combination fog and cloud computing there are certain issues that hampers the performance.
  • All these are some of the significant benefits of using fog computing and internet of things.
  • Any structured and unstructured data is very useful for enhancement of the process and when it comes to the system of education, this is very important.

For building time to time communication fog computing has to be implemented in education IoT system. The efficiency of data storing and data accessing becomes easier and quicker with enabling fog computing in the education IoT system. These days most of the higher institutions are implementing regular office applications, online desktop, messaging services to hold web level solutions for students and teachers much specifically.

Benefits of fog computing

Privacy and security aspects are considered as one of the main concerns while using the fog computing method. The accuracy and adaptability get hampered due to full outsourcing of data. Beside the wide range of benefits that are offered with the https://globalcloudteam.com/ combination fog and cloud computing there are certain issues that hampers the performance. Secondly, the broadcasting of every data shared within the different layers of fog computing leads to data redundancy and congestion among the data.

Edge devices can process data natively or through an edge network sensor in the local area, even when more central networks and nodes are down. As a result, local operations can stay functional while greater data center issues are resolved. In essence, Fog Computing allows for big data to be processed locally, or at least in close proximity to the systems that rely on it. Newer machines could incorporate more powerful microprocessors, and interact more fluidly with other machines on the edge of the network.

Unfortunately, there is nothing immaculate, and cloud technology has some downsides, especially for the Internet of Things services. PaaS — a development platform with tools and components for creating, testing and launching applications. It generates a huge amount of data and it is inefficient to store all data into the cloud for analysis.

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The monitoring layer will conduct all activities that help monitoring needful operations . An excellent example of fog computing is an embedded application within a production line automation. Running automation within a production line will incorporate various IoT devices, sensors, and actuators. These embedded devices can include temperature sensors, humidity sensors, flow meters, water pumps, and more.

Fog can also include cloudlets — small-scale and rather powerful data centers located at the edge of the network. Their purpose is to support resource-intensive IoT apps that require low latency. The integration of the Internet of Things with the cloud is a cost-effective way to do business. In fog computing, all the storage capabilities, computation capabilities, data along with the applications are placed between the cloud and the physical host.

Disadvantages of fog computing

These developing and developed technologies can provide an excellent solution to the existing problem related to the loss, damage, and manipulation of the data in the system of education. However, these devices have different platforms making it difficult to integrate. Fog computing provides a unified interface to integrate all different independent devices and empowers smart home applications with flexible resources to enable storage, real-time processing and low latency. Currently, such resources are mostly being provided by cloud service providers, where the computation and storage capacity exists.

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The Fog layer is supposed to have several decentralised nodes present in each location. This layer has the task of handling all the networks and the data received . Finally, the cloud layer or the data centre’s layer is regarded as IoT architecture’s topmost fog vs cloud computing layer. This layer has the function of allowing network access, conveniently and properly across all the shared resources in the IoT network. The storage and services areas of the IoT network requires heavy duty and is performed by the cloud layer .

How does fog computing differ from edge computing? – ReadWrite

How does fog computing differ from edge computing?.

Posted: Fri, 05 Aug 2016 07:00:00 GMT [source]

On the other hand, fog computing brought the computing activities to the local area network hardware. Fog computing processes and filters data and information provided by the edge computing devices before sending it to the cloud. Fog computing will still be processing the information at the edge but physically farther from the data source and hardware that is collecting the information. Since fog is an additional layer within the IIoT architecture, edge computing can work without fog computing. Devices, sensors, and actuators are connected right on the running applications.

This type of attack is allowing hackers to fake identities which can help them gaining unauthorised access to sensitive information and compromising IoT applications and real-time services. Therefore, a mobile sybil defence scheme has been proposed by Quercia and Hailes in 2010 to match the communities of mobile users and identify the trusted users in untrusted communities as sybil attackers . Fog computing architectural layers are defined as a decentralized technical infrastructure used to compute, store, and apply data located in the cloud. The advantages of fog computing are nowhere different from edge computing.

Applications of Fog Computing in IoT

This server is purpose-built for complex data center workloads on public, private, and hybrid cloud models. DPU accelerated server combines the latest CPUs, GPUs, DPUs, and FPGAs for performance-driven scale-out architecture on the fog layer. With DPU on the fog layer, the host server can free up its precious CPU resources by offloading some processes to the DPUs. The host server then can allocate its CPU resources to other mission-critical applications. For instance, some of the benefits of implementing DPU servers on the fog layer is the ability to accelerate networking, storage, and security management functions directly on the network interface card.

Disadvantages of fog computing

The goal of fog computing is to bring the cloud closer to IoT devices. The major goal is to tackle the issues that cloud computing encounters while processing IoT data. Thus, it can be stated that network privacy becomes a very big concern. It has been observed that the network operator tends to generate a configuration manually. However, the fog nodes that can get impacted due to lack of proper protection can cause harm towards the sensitive information.

What is the role of Fog Computing in the IOT reference model

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Performing computations at the edge of the network reduces network traffic, which reduces the risk of a data bottleneck. Edge computing also improves security by encrypting data closer to the network core, while optimizing data that’s further from the core for performance. Control is very important for edge computing in industrial environments because it requires a bidirectional process for handling data. WINSYSTEMS’ embedded systems can collect data at a network’s edge in real time and process that data before handing it off to the higher-level computing environments. Beyond the prospect of simplifying cloud security models, edge computing can also lead to major cost-savings through reduced bandwidth.

The considerable processing power of edge nodes allows them to perform the computation of a great amount of data on their own, without sending it to distant servers. Embedded hardware obtains data from on-site IIoT devices and passes it to the fog layer. Pertinent data is then passed to the cloud layer, which is typically in a different geographical location. The cloud layer is thus able to benefit from IIoT devices by receiving their data through the other layers. Organizations often achieve superior results by integrating a cloud platform with on-site fog networks or edge devices. Most enterprises are now migrating towards a fog or edge infrastructure to increase the utilization of their end-user and IIoT devices.

The fog computing, Oh my good another layer in IoT!

Fog computing offloads the computation task from the cloud down to the local area network . Therefore, fog computing can enable intelligent applications to run at the edge in real-time by bringing powerful computing at the edge. However, by implementing an additional layer between the cloud and the edge, fog computing is adding complexity to the IoT network architecture. With the proliferation of millions of IoT connected devices, a massive volume of data is being generated at a rapid pace.

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