Beginner’s Guide to Edge Computing – All You Need to Know to Get Started
February 27, 2019 | Technology
Today, we have more connected devices than people on the planet.
Even if we exclude our day-to-day connected devices like smartphones, tablets, laptops, etc., there are still more than 7 billion connected IoT devices surrounding us worldwide, deployed in efforts to make our lives more convenient.
In future, we will have even more things connected to the network, including the most common things of every day’s use – water bottles, shoes, hairbrushes, and pretty much anything you can think of.
Cloud’s Incompetence with Connected Devices
These connected devices generate a huge amount of varied and incomplete data that needs to be processed and responded to in a short time. Traditionally, the cloud has been an essential part of this process. But as the need to process IoT data in real-time increased, cloud computing has become a little incompetent for the following reasons:
- Overload: Being centrally deployed on a large scale, cloud platforms usually need to process an enormous amount of data
- Latency: As the physical distance between cloud and device (user) increases, transmission latency increases, so does the response time
- Dependency on the user’s device: With cloud computing, transmission time and processing speed depends highly on the user’s device
Edge Computing to Rescue
Edge computing enables processing of some parts of application & data to be performed by a small ‘edge server’ (referred as FOG Nodes), positioned between the cloud and user (preferably, in a location closer to the user). This allows some of the workload to be offloaded from the cloud as well as from the user’s device – resulting into decreased latency and better performance.
Adoption and Applications
For enterprises aiming at IoT implementation, edge computing has emerged as a great support. Apart from that, various AI applications and emerging 5G communication network also heavily rely on edge computing. As these applications will gain more mainstream adoption, they will also help spur future growth of edge computing.
According to a TrendForce research, edge computing market will grow at CAGR of more than 30% from 2018 to 2022. And this growth is apparent all around us, as edge computing is being used for a wide range of applications. Some examples include:
- Autonomous Vehicles: Autonomous vehicles need to do most of their data processing onboard and in real-time. Depending on the cloud to decide what the vehicle is supposed to be doing in every situation will lead to accidents. Without edge computing, autonomous vehicles are simply not feasible.
- Industrial Automation: In industrial processes, machines generally need to be adjusted as per the surroundings – such as temperature, light, and quality of material coming in. With edge computing capabilities, machines can sense these things and adjust on their own, leading to improved efficiency and extended life of the machine itself.
- Connected Homes/Offices: With Alexa, Google Home, and other voice assistants rapidly becoming a part of our day-to-day life, it is important that their response becomes quicker. Currently, it takes a few seconds for them to respond. With edge computing, their response time will become near real-time.
- Retail: Edge computing is being extensively used in the brick-and-mortar retail space to analyze and respond to customer data in real-time and provide them with a better shopping experience and personalized service.
Recommended: 19 Retail Trends for 2019 [Infographic]
Addressing Security Concerns
A general perception about edge platform’s security is – ‘since data stays in the local environment, (unlike cloud, where it has to travel far through network) security threats go down’. However, on the flipside, edge platform has its own share of security concerns:
a) IoT devices can be compromised by hackers
b) Unlike cloud computing, edge computing data often flows over untrusted public network segments
To address these emerging threats, cybersecurity experts recommend using secure tunnels and VPN to have more control over data in transit. Besides that, using cryptographic keys embedded in IoT device chips for authentication and encrypting local device communication can significantly enhance the data safety within the edge network.
In Conclusion: Overcoming Implementation Challenges
As with any other new technology, edge computing inherently puts new businesses at advantage and imposes threat on the advantages enjoyed by incumbents so far. And their resistance to change only slows down the widespread adoption of the technology, does not stop it.
It is required of businesses to be agile. Adopt edge instead of sticking to the same old practices. Make their processes more efficient, encourage adoption organization-wide, and be proactive to address the security concerns that may emerge with the new ways of doing things.