Introduction

Edge computing, the recent technology, has come as a boon to applications requiring low latency data processing. The smart IoT applications in automated vehicles and industrial use cases need fast computation. As per Gartner predictions, by 2022, 50% of the data generated will be created and processed by centralised cloud centres by edge technology. Here let’s take a look at edge computing definition; what is an edge network? And what is edge technology, along with few edge computing examples? And cloud computing vs edge computing. 

  1. Edge Computing Definition
  2. Edge server
  3. Edge Computing Examples
  4. Cloud computing vs Edge computing
  5. Benefits of Edge Computing

1) Edge Computing Definition

Edge computing takes processing power from the central processing unit to nodes. These nodes are also known as the computing edge. Edge networking is an example of distributed computing. Here the data is processed and stored very close to the point where the request is received.  It can also be referred to as the computing of data locally instead of sending the data to the cloud’s data centre. In edge computing, centralised processing power is decentralised completely. Though a centralised data centre still exists, the computing requests are handled at the logical nodes and themselves. 

2) Edge server

So, what is an edge server? The logical nodes that handle the computing requests are known as Edge servers. Edge servers are physically close to the systems where requests are raised. The need for data to make quick decisions led to using edge servers. The time taken for the data to be accessed from the central processing unit was delaying the process. Addressing this need is what edge servers came into existence.

3) Edge Computing Examples

Edge computing is implemented in commercial, industrial, and military applications. Here are lists of edge computing applications.

  1. Navigational assistance

Drivers using automated navigational assistance while driving from source to destination. The route and estimated time are updated in real-time. The data is obtained from the edge servers for a quick update to the drivers. The edge computing technology enables to update the data quickly to users.

  1. Soldier’s wearable gear

Soldier in the battlefield can fight effectively when he has real-time data in hand on time. The edge computing gets the data to the soldier on processing it in real-time. This is known as IoMT (Internet of Military Things), or as IoBT (Internet of Battlefield Things). The soldier’s devices from helmet, suit, and weapon together have a huge amount of data inclusive of the soldier’s physical health and help identify the enemy positions. This supply of real-time data in a matter of seconds aids soldiers to go with their job efficiently. Edge computing aids in quick computation when lives are at stake.

  1. Oil Rigs

Edge computing is the solution to remotely located offshore oil rigs when internet connectivity is very limited. With data being processed by IIoT, the data generated by oil rigs are more than what the networks can handle. So, edge computing solves the issue by completing the computation in an edge server closer to the data source, thereby reducing latency. 

  1. Autonomous vehicles

Autonomous vehicles or Self-driving vehicles are a good example of edge computing data. They need a huge amount of processed data in a short time. The autonomous vehicle cannot wait for the data to be processed at the cloud data centre to decide which lane to take. These calls need to happen immediately in a matter of a few seconds for the safety of the people within the vehicle and the lane. Edge computing is designed to aid in this real-time decision-making as latency is reduced to a minimum. This instant data to make a decision is done by integrated computation is called point-of-origin processing.

  1. Assembly line and surveillance cameras 

Cameras at the manufacturing facilities and security surveillance record the surroundings and send huge data to the cloud for storage and processing. Here, what kind of data to be stored can be decided. An application can remove the footage that does not have any action in it and store the ones with activities such as a man moving or a defective product. This huge data requires a lot of bandwidth to transfer to the cloud. So, if the cameras themselves decide the data that needs to be sent to the cloud, this reduces the data sent across the network, does not need huge bandwidth, and there will be less traffic in the network.

  1. Virtual Assistants

On using the cloud, virtual assistants can process the data and respond quickly, thereby improving the response time. The virtual assistants use an edge server instead of getting in touch with the cloud data centre. This improvisation of the response time will aid users to take timely decisions.

The above examples of edge computing in real-time have added great value to a working environment. 

4) Cloud computing vs Edge computing

Cloud computing and edge computing vary in the way requests are processed and responses are delivered.

Difference in AreasCloud ComputingEdge Computing
Technology suitable for enterprisesIs suitable where huge data needs to be accessed.Is suitable where response time is critical. 
Solution ProgrammingOne programming language is preferred.Different platforms can be used for programming.
SecurityDoes not need as advanced security as in the case of edge computing.Needs advanced security to handle cyberattacks.

5) Benefits of Edge Computing

Edge computing has proved to be advantageous in the following areas.

  • Reduction in bandwidth usage
  • Reduction in latency
  • Cost savings
  • Increase in Reliability and Security

Conclusion

Edge computing has changed the way data is accessed and computed rationally. As per the research, the IoT base worldwide is expected to reach 75.4 billion devices. This estimate is sufficient to understand how they have been incorporated across sectors. Edge computing architecture is expected to reach the peak earlier than expected with the research and developments in artificial intelligence and connectivity technologies with the increase in demand for smart IoT applications.

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