IoT devices and resultant data is rapidly increasing and only the Cloud might just not be enough to suit abrupt processes and evaluate the troves of data generated. It’s time to explore new techniques for better customer experience and effective management.
Several enterprises today are currently deploying or planning to deploy sensors or IoT devices across their business operations. However, collecting, storing, integrating and analyzing real-time data from various devices is a key challenge to a successful IoT implementation. 60% IT leaders believe that IoT will add tremendous complexity in the networking or data analysis while integrating to existing IT infrastructure. That is where the Edge Computing comes to rescue organizations.
Let’s learn how Edge Computing help enterprises.
What is Edge Computing?
Edge Computing isn’t a completely new concept, but a mixture of several trends that have created an opportunity to turn huge amount of machine-generated data into actionable intelligent insights.
Industrial enterprises are already driving new levels of performance and productivity using the latest technology innovations. We now know that Cloud Computing is a key profitable enabler for industrial transformation. Edge Computing is rapidly taking its place in the Industrial Internet of Things (IIoT) equation to fast-track digital transformation to reach the next level.
This is how Edge Computing, Cloud Computing, and Fog Computing differ.
Edge Computing vs. Fog Computing
Both Edge Computing and Fog Computing deliver the same functionalities in terms of approaching data and intelligence to analytic platforms that are situated on/close to the source of the data, whether they are sensors, screens, motors, speakers or pumps.
The key difference in Fog and Edge Computing is:
- Fog network or Fogging is a decentralized computing infrastructure where the data, calculations, storage and applications are circulated in the most logical and efficient way in the middle of the data source and the Cloud. It is focused on Edge devices that speak to each other.
- Whereas, Edge Computing is just focused on the devices and the technology that are connected to the “Thing”, for example industrial machines.
Edge Computing vs. Cloud Computing
Industrial organizations rely on the value of huge data being generated by several machines. To get intelligent insights from such data, Edge Computing and Cloud Computing need to work together. For example, in IIoT one hand we have “Edge” and on the other there’s “Cloud” working parallel. Sometimes Edge takes the lead while sometimes the Cloud does and there are times when they are both needed in equal measure.
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- Edge Computing controls the need for low latency where there are bandwidth constraints slowing data upload and download.
- Cloud Computing leads where data storage is needed for operations like managing data volumes across plants, machine learning and more.
Business benefits of using Edge Computing
The substantial benefit of this new approach is the way it networks the architecture.
By 2020, IT Spend on Edge Infrastructure Will Reach up to 18% of the Total Spend on IoT Infrastructure, Driven by Deployments of Converged IT/OT Systems That Reduce the Time to Value of Data Collected from Their Connected Devices – IDC
Some of the core benefits are:
- Speed – Edge Computing can increase the network performance by reducing latency and process the data closer to the source and cut-down the physical distance it travels.
- Security – As an additional security measures to guard against harmful DDoS attacks and other cyberthreats, Edge Computing distributes data processing, storage, and applications within a wide range of devices and data centers, that makes it difficult for any single disruption to take down the network.
- Scalability – Edge Computing offers a cost-effective route to scalability that allows enterprises to expand their computing ability using a combination of connected devices and Edge data centers.
- Versatility – Edge Computing can extract and formalize unstructured data obtained by edge networks to deliver quick services. Besides, it also enforces machine learning and powerful analytics to identify trends and notable data points. The scalability of Edge Computing itself makes it an amazingly versatile.
- Reliability – Many Edge Computing devices and Edge data centers are connected to the network and a comprehensive edge architecture that can therefore deliver supreme reliability and makes it more difficult for anyone to shut it down the entire service.
The Edge is a SMART business move
IIoTs benefit particularly from capitalizing on Edge Computing powered Intelligence. We are gradually moving towards a ‘SMART FUTURE’ that is driven by IoT right from our homes to our cars to the way we do business and live our lives.
Edge Computing will largely play a crucial role since it’s attached to Industrial IoTs such as heavy machinery, Inventory Management, Logistics, Smart City management and more where having access to real time actionable intelligence makes all the difference.
In a sensor intensive environment using advanced sensors working together with automation based programmed resources, it’s easier to leverage big data analytics for more relevant, consistent and accurate insights. This will directly empower businesses to detect and address inefficiencies earlier and improve customer experience while increasing productivity.
The future will largely have huge data being generated from a variety of devices right at the Edge. You need to be backed by technology that not only senses this data accurately but gathers it making it ready to be analyzed to deliver AI powered quick relevant insights.
In fact, this will be easier to arrive at without storing massive data over servers saving additional operational, maintenance, security and support costs.
Edge computing offers numerous advantages over traditional forms of network architecture and it will surely continue to play a key role for organizations going forward. And, here is a checklist of top 5 factors to consider for deploying reliable Edge Computing.