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Internet of Things vs. Edge Computing: Processing Real-Time Data

By   /  July 26, 2018  /  No Comments

IoT vs. Edge ComputingIn Edge Computing, “data” is processed near the data source or at the edge of the network while in a typical Cloud environment, data processing happens in a centralized data storage location. The Forbes post This Is What You Need to Learn about Edge Computing provides a good illustration of this point.

New iPhones have special chips to store authentication data inside the device – away from Apple servers; Nvidia has enabled new Pixel phones to process images a directly on the device. Those are just two examples of the relatively new trend in data processing. In the pre-Edge era computing did take place devices as well, but much has changed, so how has Edge Computing modernized the technology?

Edge Computing has introduced the concept of using “gateway servers, cloudlets, fog nodes, and microdata centers,” all of which are highly advanced and sophisticated technologies to augment the benefits of Edge Computing. According to the authors of the DATAVERSITY® article titled Why You Should Be Experimenting with Edge Computing Right Now, Edge Computing is in a stage of transition – taking off from where the previous Analytics technology evolution ends. Edge is also in the middle of negotiations, between risks and rewards.

Edge Computing provides a better alternative for managing computer resources, which enables a typical Cloud environment to optimize its own resources. During Edge Analytics, the processing control of all applications, services, and data are shifted from the Cloud to the edge of the network, where a physical contact is made with the data source. Edge Analytics was adopted by server vendors like Intel or Cisco, which allowed businesses to pre-process their data near its “origin.” The KDNugget post The Evolution of  IoT Edge Analytics: Strategies of Leading Players helps to understand the market strategy and positioning of bundled Analytics with servers adopted by large players. The salient point of Edge Analytics is “creating the Analytics Model” in one location, and then executing it in another location or in multiple different locations.

Now many companies use their servers as Edge devices, which translate to more storage, more processing horse power, and enhanced Analytics capabilities. Many people harbor a misconception that Edge Computing is good only for IoT Analytics. That is not true. Edge Analytics has been successfully used even for other forms of Analytics such as Video Analytics.

Edge Computing for Internet of Things (IoT)

The enhanced processing power of average computing devices is a big relief for Analytics users as such devices take the pressure away from the Data Centers. Moreover, the connected devices are often used to execute business processes on the Cloud or within the device environments. Edge Computing facilitates a strong computing ecosystem, where end-to-end connected devices are capable of running applications and Advanced Analytics services on their own. To understand how Edge Computing can integrate and distribute the processing workload across CPUs and GPUs based on relative efficiency, review the article Why Edge Computing is Critical for the IoT.

In a nutshell, the Edge devices collect the data and facilitates an “Analytics environment “that enables businesses to make the best use of smart devices, gateways, and the Cloud to extract maximum business performance and efficiency.

An authoritative article from Networked World offers a useful discussion about Edge Computing and  how it’s changing the network (connected environment). In the case of IoT, much data traffic from the devices are directly managed and processes in real time on the devices for aiding business processes as they happen. In this scenario, data does not get time to travel to faraway Data Centers or Cloud-based setups. The data pile is segregated as “critical,” which remains locally and “non-critical,” which is sifted to a central data repository. In many cases, Edge devices collects process, and compile the data, and then send the compiled data to central nodes.

A future note: The Edge Gateway, often referred to as “fog,” is basically a buffer area between the data processing location and the data storage equipment in the network.

Edge Computing and IoT in 2018: The Forward Move

As explained earlier, while Edge Computing is not a new computing environment, Fog Computing is. However, both Edge and Fog are raising trends mainly due to the massive data volumes of typical IoT devices. In case of Industrial IoT (sensor-intensive), the data needs to be analyzed and utilized as they come in.

A good example of such a case is oil rigs, where thousands of data points across the wells may be accepting data at the same time, where data velocity, bandwidth,  and latency have to be handled efficiently, and this is where technologies like the Edge and the Fog play a critical role. Read about intelligence gathering at the edge on the iScoop Guide Edge Computing and IoT 2018 – when intelligence moves to the edge. This guide states that by 2022, the worldwide Edge Computing market has been forecasted to reach $6.72 billion at a CAGR of 35.4 percent.

The business objectives of both Edge Computing and Fog Computing, and the earned benefits in case of each are the same: High processing speed, bandwidth optimization, best use of storage, reduced time and cost. All these great benefits are achieved mainly by controlling the dataflow from one location to another. The IEEE paper A Survey on the Edge Computing for the Internet of Things provides a comprehensive survey of sample Edge Architectures with detailed notes on the relative merits and demerits of different types of architectural models.

Shifting Intelligence to the Edge: How Do Businesses Gain?

To best explain this, the author of this post takes the example of the Cloud server used for business processing needs. No matter how agile and strong the telecommunications infrastructure may be, the reality is that even networks have finite bandwidth and security limitations. Considering the risks involved in business data transfer from on-premise to Cloud, digital businesses have to think of a better solution. This is where Edge Computing can play a significant role by allowing businesses to use private networks in conjunction with Edge devices to by-pass the public internet.

Another critical issue – that of a third-party intercepting confidential business data on the public internet and putting a business at risk can also be tackled through Edge Computing quite well. Many businesses are choosing Private Cloud with Edge Computing to ensure their data moves fast but securely from the IoT devices to a Cloud network. The IoT Evolution World offers an interesting review of the transformative capabilities of Edge and Fog computing in the article How Will IoT and Edge Computing Change Business in 2018?

Business Analytics for IoT: The Unique Features

The KDNugget post Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science discusses how IoT verticals will impact the Edge Computing environment and traditional Data Science as practiced in “on-premise Analytics ecosystems.” In this post, the reader gets an insider’s view on the practical utility of Machine Learning and Deep Learning algorithms in say, a telecom business, where churn modeling, cross-sell-or up sell models, or customer lifetime analysis are commonplace.

  • Data Storage Issues: Though Edge and Fog Computing promise a lot of good things for the future of Business Analytics, one nagging problem will remain – managing the data transfers between locations. Apart from the risks of Data Security, latency, and limited bandwidth, data processing locations are also a critical concern for most businesses as Public Clouds are not the typical safe havens for mission-critical business information.
  • Data Security Issues: As Edge Computing will control the physical connection between the IoT devices and the Cloud landscape, Edge is destined to play a very important role in ensuring Analytics happen with the least amount of data piracy, corruption, or loss. The safety and accuracy of business processes, to a large extent, will depend on the sanctity of the Edge Computing model deployed at a business.

As IoT use cases continue to rise across industry sectors, the need for Edge Analytics will also rise. The DATAVERSITY® article, IoT Use Cases: Internet of Things Applications Evolve discusses how IoT is rapidly engulfing business operations and day-to-day processes.

Conclusion

In IoT environments, when vast amounts data are collected in real-time, shifting such huge amounts of data to a processing location can be a huge challenge. Find out how How Edge Computing and the Cloud Will Power the Future of IoT by provisioning an interim (Edge) location for storing and processing the data as it comes. The greatest benefit of this new Analytics Architecture is the speed and immediacy of Data Analysis without burdening the Cloud networks. But there is life for Edge Computing beyond the IoT – retail businesses can now think of offering hyper-personalized shopping experiences to their customers by Edged buying environments.

 

Photo Credit: metamorworks/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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