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MD4058 Research And Study Skills

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MD4058 Research And Study Skills

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MD4058 Research And Study Skills

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Course Code: MD4058
University: University Of Central Lancashire is not sponsored or endorsed by this college or university

Country: United Kingdom


You need to add a lot more content at the end abut the architectures of edge and fog, and what people use it for. Also a whole section on learning tools (how uni students best learn, and the different styles of learning).
I look forward to the completed lit review with more content, and an analysis of the problem you are solving in why we need to teach people about Fog and Edge Computing.
Only the presentation at a seminar that you are required to do. So you will only be submitting a presentation not the whole document. This is about what you have done so and your progress against what you said you would do in the proposal.
When you look for learning tools – you should talk about if you are looking on demand training or a competency-based learning tool.


The Internet of Things (IOT) generally refers to a comprehensive environment, which connects a multitude of different physical objects or even things such as appliances and facilities, to the internet thus enhancing the efficiency of the applications, for example, logistics, manufacturing, agriculture, etc. Fog and Edge computing helps to advance the cloud-centred Internet of Things (CIoT). Through extension of the cloud computing model and on to the edge networks of the IoT which involves the participation of the network intermediate nodes, for example, routers, hubs, switches. That it also includes the IoT devices with the processing of information and the ability to take decisions in order to improve security, agility, cognition, latency and efficiency (Butler, 2018).
Fog and edge Computing (FEC) Mind mapping explanation (reference)
Fog and Edge Computing (FEC) advanced the cloud computing technology as depicted in the diagram above. It advances the storage of data in organization by using the educational tools. In different organizational setting, companies use the local area network and horizontal networking as the educational tools of fog and edge computing technology. Fog and Edge computing applications the use intelligence based on security, cognition, latency, and efficiency is significant (Butler, 2018). Lastly, the architecture above assume a hierarchy of the inner edge, middle and outer edge.
Edge and fog computing work concurrently and both refer to the ability of the closer of process data to reduce the cost of latency and enhance the user experience in order for the consumer. They also allow the ability to filter the data before it hits the big data lake and reduces the amount of the data, which is needed to be processed. (“Cloud Computing, Fog Computing and Edge Computing: What’s the Difference?”, 2018) Fog and Edge computing is dependent on the basic idea of data logic fovement with respect to an outer ring data range.
The concept of Fog and edge computing has been built in order to properly respond to the increase in the case of data bandwidth which is required in case of the end devices. Again, it has also been propelled by the advance of the Internet of Things which has incremented the need of processing data which is comparatively closer to the consumer thus minimising the latency, cost and also enhancing the quality (Butler, 2018).
A Boeing 787 for example generally creates 40TB an hour of flight, but only half a TB of the generated is finally transmitted to a data centre in order for the analysis and its storage. Quite similarly, a retail store may approximately collect 10 GB of data in an hour but only 1 GB of that data is transmitted to the data centre. Neither is it sensible, nor is it possible to do a full data install on either a plane or inside a store (Butler, 2018). This is where Fog and Edge computing comes in order to validate and also process this data primarily inside a local network which is in case of fog or inside a gateway device as in case of edge.
The main difference between fog computing and edge computing lies in the fact that Fog computing pushes further the intelligence of data validation into a local network. On the other hand Edge computing propels the same data validation and intelligence processing onto edge devices such as switches and routers (Chang, Buyya & Buyya, 2018).
What are the various applications of Fog and Edge computing?
FEC helps provide a complement like in the case of the cloud as in IOT within the cloud and the other things in order to work towards providing a service continuum. In general FEC has its applications and utilities in five different fields:

Security: FEC ensures safe and trustworthy transactions by providing additional security. For example, the current wireless sensors when deployed in the outdoor places most often need to update the remote wireless source code in order to solve any security related issues. While owing to certain different dynamic environmental factors for example, unstable strength of the signal, interruptions and constant bandwidth etc.(Chang, Buyya & Buyya, 2018). The central distant backend server often faces certain issues in order to perform the updates swiftly and thus it also enhances the probability of a cyber security attack. The FEC on the contrary if available can configure the backend for the best routing path within the entire network according to various FEC nodes to apply rapid software security updates to the wireless sensors(“Cloud Computing, Fog Computing and Edge Computing: What’s the Difference?”, 2018).
Cognition: FEC also aids the clients in taking autonomous decisions regarding the whereabouts of the deployment of storage, computing and control functions. The FEC awareness includes certain mechanisms such as self-adaptation, self-healing, selfexpression etc, which shifts the role of IOT devices to active from the passive smart devices continuously operating and reacting to customer requirements without any decisions coming from the cloud.
Agility: FEC also enhances the speed of the deployment of large scope IoT system. Much to the contrast of the existing cloud system which mainly relies on the large business holder. FEC generally relates to bringing the opportunity to the small and individual businesses.
Latency (amount of time for a message to traverse in a system): The primary understanding of FEC is to provide fast responses regarding the applications which require an ultra-low latency.
Efficiency: FEC also accounts for a better efficiency of the CIoT in terms of improving the performance and also reduces the costs which are not essential.

How does FEC help with storage?
The storage mechanism of FEC corresponds to the data storing which is temporary and also the catching at the FEC nodes which is in order to improve the efficiency and performance of the information or the content which is being delivered. For example, the providers of content service can also perform the catching of multimedia content at the FEC nodes, which are the closest to the customers and aim to improve the experience quality. In addition, in case of connected vehicles the FEC nodes help fetch and share the information, which the vehicles have continuously collected. (Chang, Buyya & Buyya, 2018)
How does FEC help in acceleration?
FEC helps facilitate networking with the help of a key concept- “programmable”. FEC nodes also provide support and acceleration in two aspects – Networking acceleration and also computing acceleration:
Networking acceleration: FEC aids, network acceleration mechanism which is based on the virtualisation technology thus enabling the FEC nodes to operate several routing tables, which are in parallel and also realise the Software Defined Network (SDN).
Computing acceleration: FEC nodes utilises the updated embedded processing units for example, the Graphics Processing Units (GPUs) or Field Programmable Gate Arraysc (FPGA) units in order to provide computing acceleration.
Advantages and disadvantages of Edge and Fog computing?



Edge computing generally refers to the response of the system to the significant increase of the bandwidth that is required by the end devices, which underpin the IoT. These devices generate a continuous stream of data that has to be validated, analysed and processed in the real time providing a real time excellent experience.

Edge and fog computing can also make the entire network and the system a bit more complicated, increasing the time taken for the root analysis.

Use of Fog and edge computing also reduces the local bandwidth costs by reducing the total bandwidth needed.

with the increase in the edge and fog computing processing capabilities are moved to a decentralised location.

Edge computing also has the ability to bolster cyber defences mostly because certain security measures such as inscription are implemented in the local network.

Edge devices also have a higher number of refresh cycles (length of time that passes between installations) which results in the lock in of architecture designs.

What are the various networking connectivity of FEC that is a must to know?
 Networking of FEC involves both horizontal and vertical connectivity (Butler, 2018). Vertical networking interconnects the things and the cloud with the IP networks which needs proper demonstration for a clear understanding. On the other hand, horizontal networking is generally heterogeneous in nature, and the network signals and protocols depend on the supported and specified hardware of the FEC nodes.
What is the Architecture of Fog and Edge computing?
The Fog and Edge computing has three different edge layers:

Inner edge
Middle edge
Outer edge

As technology is advancing with such a rapid pace, so is the need for storing the vast and enormous size of data that is probably the most important aspect of cloud architecture. There are many benefits of cloud architecture in networking Industry that should be known before proceeding to our related topic. Cloud architecture first and foremost was initiated by companies such as Cisco to store a vast amount of data in networking that could either be computed or just kept there for future references (Chang, Buyya & Buyya, 2018). Later as the need evolved, this architecture was used to enhance the prospect of networking with a wide array for its usage being recognized simultaneously. The economic aspect of this architecture was also a reason for the massive rise in its users around the world. It uses a central server to store and transfer the data that is required throughout its network.
As the technology evolved and multiple other cloud concept evolved like the Big Data concept along with the IoT(Internet of things) enabled appliances and homes, there were many flaws that were seen in the existing cloud architecture. There was delay registered when the amount of data increased with several performance issues. There was also an issue of time taken to transfer the input and received an output from these devices that was very crucial in an effective working of any smart or IoT enabled appliances. The above-stated issues needed to be rectified to give way for better performance based technology in cloud architecture. This was where Edge and Fog commuting was born.
Fog commuting along with Edge commuting used the same technology in terms of their performance i.e both of them are involved with the processing of information and its creation in an intelligent and smart manner. However, the key difference is the place where this intelligence is placed in the respective commuting. Fog Computing places the intelligence in a LAN (Local Area Network) from where the return and processing of data take place. This is very vital to avoid any delay in the data transmission throughout the network. Fog, as well as the Edge technology, is very much important in the time of IoT and smart devices. The most important feature of Fog computing is the fact that it decreases the amount of data that is needed to be sent on the cloud which does also helps in decreasing the internet latency or lag, both of which are responsible for better response time in all the devices that are connected to a network. But due to a large layer of complexities that is required in the transmission of data in a network, Fog was not fit to be integrated with IoT and smart networking because every individual point of passage of data was a potential failure point in the network.
Edge computing is another dimension of Fog computing, where the place of installation of the intelligence along with the processing power of the network( most of which are smart or IoT enabled) is directly either on the device or in the very close proximity of the given data source (Chang, Buyya & Buyya, 2018). This “edge” technology removes the scope of sending the data of the IoT enabled network to the centralized cloud every time. This is a very crucial aspect of all the devices that might be connected to the IoT network because system response time is the most important aspect of all the appliances that are connected with IoT in a smart home network. Edge technology gives its users a cutting-edge response time for all the “sensors” and “actuator” that are involved in any IoT enabled device.
Fog computing pushes intelligence down to the local area network and architecture for processing data in IoT gateway. On the other hand, edge computing pushes intelligence as well as processing power and capabilities of communication and gateway for the appliance directly into the devices such as programmable automation controllers. On the other hand, in order to assist the distinctiveness between the process, intelligence transportation system management, industrial and commercial networking as well as autonomous vehicles are used with the help of fog and edge computing. Hence, the use of fog and edge computing has brought a massive change in fog and edge computing.
The various aspects of Edge computing and Fog computing has already been discussed. It can be seen that compared to the various traditional systems, FEC has several advantages which has been discussed thoroughly. The main purpose of the Internet of Things is to connect the devices and send their data directly to the cloud or to the internet. Fog and Edge computing are architectures of the network and the system in case of the manufacturing and the automation applications which attempt to collect and then analyse and process the data from these specific assets faster and with more efficiency compared to traditional cloud architecture.
Best learning method
The wiring of all the physical aspect of a device with a control system to provide data through automation by the PACs (programmable automation controller) via an onboard system makes it the best choice for all the IoT appliances that are there in a network. These PACs can collect as well as analyze while processing a large amount of data which does help it to recognize which data should be sent to the centralized cloud architecture and which data needs to be stored locally within the network, thus eliminating any time lag.
In order to learn fog and edge, computing the primary requirement will be the use of a computer. Secondly, a local area network would be highly appreciated as it can actually be used to demonstrate certain factors. Other than that, a proper theoretical understanding of the various platforms and the details and applications of fog and edge computing need to be included. Both vertical and horizontal networking (corporate business unites and between companies) need to be explained with proper demonstration. The architecture required for fog and edge computing also needs to be properly established (Butler, 2018).
22, O. (2018). Fog vs Edge Computing: What’s the difference?. Retrieved from
Butler, B. (2018). What is edge computing and how it’s changing the network. Retrieved from
Chang, C., Buyya, S., &Buyya, R. (2018). Internet of Things and New Computing Paradigms[Ebook]. Wiley STM. Retrieved from
Cloud Computing, Fog Computing and Edge Computing: What’s the Difference?. (2018). Retrieved from
Cloud Computing. (2018). Retrieved from
Edge computing vs. fog computing: Definitions and enterprise uses. (2018). Retrieved from
Fog Computing vs. Edge Computing: What’s the Difference? | Automation World. (2018). Retrieved from
How fog computing pushes IoT intelligence to the edge. (2018). Retrieved from
IOx and Fog Applications. (2018). Retrieved from
What is fog and edge computing?. (2018). Retrieved from

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