Edge Computing: Definition, Types, Working, Benefits,  Disadvantages, Applications

Edge Computing

Edge computing is in the form of computing and it is done on-site or at a particular data, source to minimize the need for the data to be processed in a remote data center. It is used to process time-sensitive data and mainly focuses on placing the processing as close as feasible to the source of data to reduce the latency and bandwidth usage. It is a distributed computing framework that brings the enterprise applications closer to the data sources like IoT devices or local edge servers.

Aim of Edge Computing

The main aim of Edge Computing is to push the computation to the edge of the network away from data centers, mobile phones, and network gateways to provide the services and processing on the behalf of the cloud.

Types of Edge Computing

  • Device Edge
  • Sensor Edge
  • Mobile Edge
  • Far Edge
  • Internet of Things Edge
  • Wireless Access Edge
  • Router Edge
  • Service Provider Edge
  • Branch Edge
  • On-Premise Edge
  • Near Edge
  • Network Edge
  • Enterprise Edge
  • Multi-Access Edge Computing
  • Data Centre Edge
  • Cloud Edge
  • Cloudlets

Differences between Edge Computing Vs. Cloud Computing

In Cloud Computing, the former relies on the central computing model that delivers the services, processes, and data services and the lattice refers to the computing model which is highly distributed. Edge environments usually move applications and data processing close to the data-generation site as possible. As digital twins, robotics, drones, autonomous vehicles, and other digital technologies mature the need to handle computing outside the cloud grows. The organization uses both cloud and edge networks to design the modern IoT framework. These two technology platforms are complementary and each has a role in building the contemporary data framework. At the same time, edge computing can deliver a more agile and flexible framework and reduces the latency of IoT devices. It does not accommodate the enormous volumes of data to feed an analytics application or smart city framework. Cloud computing supports a more streamlined IT programming framework and its bandwidth is highly scalable

Working Procedure

Generally, Edge Computing works by processing information and capturing the source of data or the desired event as possible. It may rely on the sensors, computing devices, and machinery to collect the data and feed edge servers to the cloud. So depending upon the desired task and outcomes the data might feed analytics and machine learning systems, delivery automation capabilities, or offer visibility to the current state of the device, system, or product.

Most of the data calculations take place only in the cloud or at a data center. The organizations migrate to the edge model with the IoT devices and need to deploy the edge servers, gateway devices, and other gear to reduce the time and the distance required for computing tasks and to connect the entire infrastructure. The part of the infrastructure includes smaller edge data centres located in the secondary cities or even rural areas or cloud containers to move easily across the clouds and systems as needed. In some cases, IoT devices may process the data on-board to send the data to smartphones to handle calculations. These include mobile edge computing works on wireless channels and fog computing incorporates the infrastructure that uses the clouds and other storage to place the data in the most desirable location and so-called cloudlets to seminars ultra-small data centres. Sensors and edge IoT devices can track traffic patterns to provide real-time insights into congestion and routing.

Industrial Edge Computing

Edge Computing can be immediately captured at the edge of the standalone computer, device, and equipment. Industrial Edge Computing refers to the process of managing data handling activities such as the smart edge devices. Depending on the importance of data analysis, the information can be transferred to the cloud for further analysis or integration into the bigger data.

Benefits of Industrial Edge Computing

To analyze the benefits, take the scenario of an Automated Guided Vehicle(AGV) delivering the materials to multiple workstations on a shop floor. For the success of AGV, it must be able to process and collect the data from the shop floor in real-time to navigate new areas.

 Although AGV uses a mobile network to access the industrial cloud and it will be more effective and faster if it handles its computing on board. The benefits of this process are varied and cut across enhancing security, delivering real real-time business insights, and enhancing productivity. Edge computing reduces security challenges by capturing and analyzing the data and making the resultant decisions in real-time.

Edge Computing drives the automation of predictive maintenance initiatives. smart edge technologies such as sensors, actuators, and controllers can be used to track the health of equipment and moving parts within it. The increased visibility edge computing brings to manufacturing also play a role in delivering the promise of a lights-out factory.

Advantages of Edge Computing

  • It provides high speed and reduces latency and better reliability allowing for quicker data processing and content delivery.
  • It offers a less expensive route to scalability and versatility which allows combining IoT devices and edge data centers.
  • It offers better security by processing storage and applications across a range of devices and data centers.
  • It improves data management and reliability.

Disadvantages of Edge Computing

  • It requires more storage capacity.
  • It can able to analyze only data.
  • It requires advanced infrastructure.
  • The security challenge is high due to the large amount of data.