Everything You Need to Know about IoT Edge Computing


The number of IoT (Internet of Things) devices circulated today is already growing exponentially and billions of devices are joined to the IoT network while leveraging the power of the cloud. Writing the future of the IoT devices, IoT edge computing has come to the stage addressing latency issues by providing data processing closer to the source. Organizations that manage physical assets can reap tremendous cost savings and unlock new opportunities by switching to intelligent edge computing architectures. In many ways, edge computing is the network architecture that makes IoT possible in the first place.

Demanding for high capacity data processing techniques in real-time with a spacious network is the authentic challenge that data scientists and data analysts are facing today. But, no worries anymore! This problem is now taking care of by IoT Edge Computing Technology.

What is IoT Edge Computing?

According to International Data Corporation (IDC), IoT Edge Computing is a network of small data centers where critical data is stored and processed locally. These data centers are connected as a mesh and they push the received data to a central storage repository.

Smart devices of IoT Edge Computing look like tiny data centers with minimum latency that are intelligent in processing critical data fragments and providing real-time response. These devices avoid the delay caused by sending the data through the internet to the cloud and linger for cloud response.

IoT Edge Computing Architecture


Conventional analytical clusters do not support edge computing as a result of the benefits of power, cost, and space. The reason behind this is the expensiveness of power, cooling, space, and other functional costs. These conventional analytical clusters do not offer the simplicity or speed that is required for the feasibility of edge computing.

Businesses have moved forward from the x86 (a group of instruction set architectures originally developed by Intel) clustered architectures that have hindered real-time analytical innovation. Now their interest is grabbed by the accelerated systems that ensure the size, performance, and required speed.

These new systems have acquired hybrid technologies that integrate numerous computing technologies like x86, FPGA or GPU. They require minimum power but provide high performance that goes beyond existing conventional systems. In situations where there is a lack of resources, these versatile systems complement the incumbent infrastructure and increase the performance of the existing clusters.

Benefits of IoT Edge Computing

  • Low latency

The edge is much closer to the IoT devise than the core or cloud which means it performs communications to reach local processing power, significantly speeding up data communications and processing within a shorter roundtrip.

  • Longer battery life for IoT devices

Ability to open communication channels for shorter periods of time due to improved latency is an advantage here. It leads to the extended battery life of IoT devices. Distributed ledger, or a hybrid open-source ledger implementation could be used to obtain the advantage of a distributed ledger which provides features from the NoSQL database MongoDB on which it is based.

  • More efficient data management

Processing data at the edge support simple data quality management such as filtering and prioritization in an efficient way. Completing this data administration at the edge implies cleaner data sets can be presented to cloud-based processing for further analytics.

  • Access to data analytics and AI

Data analytics and AI require very fast response times or to process large ‘real-time’ data sets that are unfeasible to send to centralized systems. Edge processing power and data storage address this problem as well.

  • Resilience

When comparing to a centralized model, the edge offers more possible communication paths. The resilience of data communications is more assured and if a failure occurs in the edge, other resources are stand by to provide continuous operation.

  • Scalability

In the edge model, less load should ultimately be placed on the network since the processing is decentralized with it. This implies that scaling IoT devices consists of less resource impact on the network, particularly if application and control planes are located at the edge along with data.

IoT Edge Computing Use Cases

IoT Edge Computing is being used in various applications where the usage is increasing daily. Let’s have a look at some use case here. IoT Edge Computing can be used for supporting general IoT functionality, adding value to IoT or supporting customer solutions

  • Device management
  • Security
  • Priority Messaging
  • Data Aggregation
  • Data Replication
  • Cloud Enablement
  • IoT Image and Audio Processing

Cutting the Long Story Short

IoT Edge computing is recognized as an important aspect of enabling faster connectivity. It bridges the cloud services with the edge devices while lowering latency and providing new applications and services to consumers. It will be the basic technology that empowers future hybrid computing where computing decisions are made in real-time either locally or in the cloud or at the device based on latency, power, and overall storage and performance needs.

Reference

 

 

Comments

  1. Dulanga KithminiDecember 9, 2020 at 8:27 PM

    Nicely written Ruvishka. Can you share on what this priority messaging is and what are the design considerations for priority messaging at the edge?

    ReplyDelete
    Replies
    1. Ruvy RathnayakeDecember 11, 2020 at 10:05 AM

      Most of the data generated by the IoT is low priority data which has more economic value. There is some critical data which should be prioritized and immediately acted upon. The expanse of priority messaging is not limited to single applications. And if I mention some design considerations in very brief, they are Fast processing at the edge, Message association, Routing and Battery life. If you love to read more, let me suggest this document.
      https://www.gsma.com/iot/wp-content/uploads/2018/11/IoT-Edge-Opportunities-c.pdf

      Delete
  2. Suranga WijayarathnaDecember 9, 2020 at 8:40 PM

    Nice flow Rivishka, Can you explain on how edge computing is used in data aggregation?

    ReplyDelete
    Replies
    1. Ruvy RathnayakeDecember 11, 2020 at 10:10 AM

      Many IoT devices are connected means more data generations. More data results in more replication of data from those devices and not everything needs to be sent back to centralized servers. Edge aggregates the collective data from various sensors and selects which data to send. For example, Edge aggregate the data from many temperature sensors in the same location and produce statistical measures can be mentioned.

      Delete
  3. Chamal DemelDecember 10, 2020 at 1:45 AM

    Interesting read Ruvishka.

    ReplyDelete
  4. Thidasala RathnayakeDecember 10, 2020 at 6:02 AM

    Very informative blog Ruvishka. Just to clarify, who are the major edge cloud service providers today?

    ReplyDelete
    Replies
    1. Ruvy RathnayakeDecember 11, 2020 at 9:54 AM

      Thank you Thidasala. In addition to application developers, some Edge Cloud service providers are Hyperscalers, Global Content Network (GCN) operators, Data Center Operators (DCOs), and Communications Service Providers (CSPs).

      Delete
  5. KaweeDecember 10, 2020 at 8:30 AM

    Informative article!! Keep up the good work

    ReplyDelete

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