EDGE COMPUTING EXAMPLES

EDGE COMPUTING EXAMPLES:Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. “A common misconception is that edge and IoT are synonymous.

Edge computing is the practice of capturing, storing, processing and analyzing data near the client, where the data is generated, instead of in a centralized data-processing warehouse.

What is EDGE full form?

Enhanced Data rates for GSM Evolution (EDGE) also known as Enhanced GPRS (EGPRS), IMT Single Carrier (IMT-SC), or Enhanced Data rates for Global Evolution) is a digital mobile phone technology that allows improved data transmission rates as a backward-compatible extension of GSM.

Edge computing is a form of computing that is done on site or near a particular data source, minimizing the need for data to be processed in a remote data center.

Traditional edge devices include edge routers, routing switches, firewalls, multiplexers, and other wide area network (WAN) devices. Intelligent edge devices have built-in processors with onboard analytics or artificial intelligence capabilities. Such devices might include sensors, actuators, and IoT gateways.

The benefits of edge computing are as follows:

1.     Faster Response Times

Deploying computation processes at or near the edge devices helps reduce latency, as explained above.

For example, suppose one employee wants to deliver some urgent message to another employee in the same company premises. It takes more time to send the message as it routes outside the building and communicates with a distant server located anywhere in the world and then comes back as a received message.

With Edge computing, the router is the in-charge of data transfers within the office, significantly reducing delays. It also saves bandwidth to a great extent.

2.     Cost Efficiency

Edge computing helps save server resources and bandwidth, which in turn saves cost. If you deploy cloud resources to support a large number of devices at offices or homes with smart devices, the cost becomes higher. But edge computing can reduce this expenditure by moving the computation part of all these devices to the edge.

3.     Data Security and Privacy

Moving data across servers located internationally comes with privacy, security, and more legal issues. If it’s hijacked and falls into the wrong hands, it can cause deep concerns.

Edge computing keeps data closer to its source, within the boundaries of data laws such as HIPAA and GDPR. It helps process data locally and avoid sensitive data to move to the cloud or a data center. Hence, your data remains safe within your premises.

In addition, data going to the cloud or distant servers can also be encrypted by implementing edge computing. This way, data becomes more secure from cyberattacks.

4.     Easy Maintenance

Edge computing requires minimal effort and cost to maintain the edge devices and systems. It consumes less electricity for data processing, and cooling needs to keep the systems operating at the optimal performance is also lesser.

The disadvantages of edge computing are:

1.     Limited Scope

Implementing edge computing could be effective, but its purpose and scope are limited. This is one of the reasons people are attracted to the cloud.

2.     Connectivity

Edge computing must have good connectivity to process data effectively. And if the connectivity is lost, it requires solid failure planning to overcome the issues that come along.

3.     Security Loopholes

With the increased usage of smart devices, the risk vector of attackers compromising the devices increases.

There are far too many examples of edge computing use cases to list here, so we’ve chosen 10 important ones below:

1.     Autonomous vehicles

Autonomous platooning of truck convoys will likely be one of the first use cases for autonomous vehicles. Here, a group of truck travel close behind one another in a convoy, saving fuel costs and decreasing congestion. With edge computing, it will be possible to remove the need for drivers in all trucks except the front one, because the trucks will be able to communicate with each other with ultra-low latency.

2. Remote monitoring of assets in the oil and gas industry

Oil and gas failures can be disastrous. Their assets therefore need to be carefully monitored.

However, oil and gas plants are often in remote locations. Edge computing enables real-time analytics with processing much closer to the asset, meaning there is less reliance on good quality connectivity to a centralised cloud.

3. Smart grid

Edge computing will be a core technology in more widespread adoption of smart grids and can help allow enterprises to better manage their energy consumption.

Sensors and IoT devices connected to an edge platform in factories, plants and offices are being used to monitor energy use and analyse their consumption in real-time. With real-time visibility, enterprises and energy companies can strike new deals, for example where high-powered machinery is run during off-peak times for electricity demand. This can increase the amount of green energy (like wind power) an enterprise consumes.

4. Predictive maintenance

Manufacturers want to be able to analyse and detect changes in their production lines before a failure occurs.

Edge computing helps by bringing the processing and storage of data closer to the equipment. This enables IoT sensors to monitor machine health with low latencies and perform analytics in real-time.

5. In-hospital patient monitoring

Healthcare contains several edge opportunities. Currently, monitoring devices (e.g. glucose monitors, health tools and other sensors) are either not connected, or where they are, large amounts of unprocessed data from devices would need to be stored on a 3rd party cloud. This presents security concerns for healthcare providers.

An edge on the hospital site could process data locally to maintain data privacy. Edge also enables right-time notifications to practitioners of unusual patient trends or behaviours (through analytics/AI), and creation of 360-degree view patient dashboards for full visibility.

6. Virtualised radio networks and 5G (vRAN)

Operators are increasingly looking to virtualise parts of their mobile networks (vRAN). This has both cost and flexibility benefits. The new virtualised RAN hardware needs to do complex processing with a low latency. Operators will therefore need edge servers to support virtualising their RAN close to the cell tower.

7. Cloud gaming

Cloud gaming, a new kind of gaming which streams a live feed of the game directly to devices, (the game itself is processed and hosted in data centres) is highly dependent on latency.

Cloud gaming companies are looking to build edge servers as close to gamers as possible in order to reduce latency and provide a fully responsive and immersive gaming experience.

8. Content delivery

By caching content – e.g. music, video stream, web pages – at the edge, improvements to content deliver can be greatly improved. Latency can be reduced significantly. Content providers are looking to distribute CDNs even more widely to the edge, thus guaranteeing flexibility and customisation on the network depending on user traffic demands.

9. Traffic management

Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows.

With edge computing, there is no need to transport large volumes of traffic data to the centralised cloud, thus reducing the cost of bandwidth and latency.

10. Smart homes

Smart homes rely on IoT devices collecting and processing data from around the house. Often this data is sent to a centralised remote server, where it is processed and stored. However, this existing architecture has problems around backhaul cost, latency, and security.