Is the future of IoT at the Edge in 2020?
Whether we realize it or not, we now live in the Internet of Things (IoT) world, where everything from our devices, home, vehicles and us, the humans are connected. While many of today’s connected devices take advantage of cloud computing today, market research firm Statista, predicts over 30 billion IoT devices to be deployed globally by 2020, making it impossible to store the amount of data in pure cloud environment.
In fact, the current computing power in the cloud isn’t fast enough to optimize performance in real-time. Hence, organizations will move processing from the cloud, closer to the end-device or the ‘edge’, believe experts, making Edge Computing ever more important for the ‘connected’ enterprise.
On the Edge
Edge computing conquering the IoT space may sound strange to some, as we are still rooted in the cloud era, running almost all our connected devices with ease. But when cloud majors like Amazon, Microsoft, IBM and Google started seeing most of the new opportunities for the “cloud” lie at the “edge,” analysts begun to pay greater attention.
According to research firm Gartner, by 2022, 75% of enterprise data will be processed outside of the cloud as well as traditional data centers. As a result, the size of the edge computing market will surpass $13 billion worldwide within the same timeframe from the current $8 billion market. The analyst also noted that 2020 is the year when more organizations will invest in edge computing for IoT to monitor and process data in real time.
Explaining why edge computing is essential in the IoT era, Avinash Velhal, Group CIO, India and EMEA, at Atos, says, “In edge computing, the value of analytics is vital. It is more real-time.”
Giving an example of self-driving cars to understand the importance of edge computing in IoT, he explains, “The moving vehicle simply cannot rely on a remote server to decide if it needs to stop when there’s a pedestrian crossing the road in front of it. The decision needs to be made immediately. The data has to be processed on the spot, regardless of the internet connection. Plus, vehicles on the road can communicate with each other more efficiently because they don’t need to send data about accidents, weather conditions, traffic or detours to the remote server first.”
A recent McKinsey report sees edge adoption for IoT’s wide range of applications in manufacturing, healthcare, energy and utilities, retail and transport as well as other industries when it comes to time-sensitive tasks. McKinsey researchers believe, each industry has a unique perspective on why it would adopt edge computing and which characteristic of edge it benefits from most. For example, smart sensors in agriculture need not turn to the central server to decide when they need to water the plants nearby or add fertilizers. They can easily perform the routine tasks on their own and sync with the main cloud when required.
Again, by combining edge computing with 5G or Wi-Fi 6 can open up new possibilities to enable smart factories. Connected robots and vehicles within the production facility can be operated remotely or run in an automated way to increase productivity significantly.
In healthcare too, edge computing has wide application in the IoT environment. Robot-assisted surgery is a case in point, where every nanosecond can mean the difference between life and death. These robots need to be able to analyze data on their own in order to aid in surgery safely, quickly and accurately.
As Vibhore Sharma, CTO at Info Edge India Limited explains, “Edge technology is all about getting services at the click of a button and for that every device needs to be smarter, the IoT revolution we are witnessing at the moment.”
Sharma says that more organizations will invest in edge because not only are they looking at reducing operational costs, but are looking to scale up, improve efficiency while at the same time increase data security.
Emphasizing on why edge is the way to go for IoT security, Gautam Dutta, Senior Director – Marketing at Siemens Digital Software says, “Because the data is decentralized in edge technologies, distributed among the devices where it is produced, it’s difficult to take down the whole network or compromise all of the data with a single attack. This approach is also preferred in terms of GDPR compliance, as the less sensitive information is sent through your network and stored in your cloud.”
Likewise, lower data traffic and reduced cloud storage, lead to more efficient business operations, believes experts. More importantly, connection issues won’t be issue as they are for other IoT products that rely on the cloud. This is due to the fact that your devices can work autonomously, without internet connection.
Dutta sees in 2020, organizations will begin to fully exploit the potential of edge computing. CIOs will use processing on devices to provide faster services for end users; to avoid the risk of network failure or of having to create and share duplicates of sensitive data and make their services more cost-effective when operational costs such as energy use are, at least in part, shared with the end device.
Edge and Cloud to Co-exist
While some in the industry are anxious whether edge computing is set to replace the cloud, Velhal argues that, edge computing is more of a complement to cloud computing than its inevitable replacement.
The future will see an integration of edge, cloud and IoT, a new paradigm where edge, cloud and IoT are merged together and make a threesome. While this stage may involve new level of research and complexities, what will happen in the current scenario is more akin to coexistence.
For Dutta, edge computing is a response to cloud computing, a way of covering for some of its shortcomings not replacing the latter. For example, if you want to store large scale data and online processes, virtual servers will still be the way to go. But if you want to build a responsive solution with reduced latency, supplement it with Edge processing to make it faster and more reliable. The coming year will see this constant back and forth between the two models.