If you’re old enough, cast your mind back to office life in the 1990s. Remember the days when all processing happened within the computer tower sat under your desk? Back when transmitting data to work on remotely consisted of copying files from your office workstation to a disk that you then copied to your home computer. Oh, the good old days! Well actually, you might not have seen the back of those days just yet. While you may be glad to hear that computer towers and floppy disks aren’t making a comeback, we are beginning to see the return of local processing and storage of data, also known as ‘edge computing’.
In simple terms, edge computing is a way of streamlining the flow of traffic from IoT devices to provide real-time local data analysis and over the last few years, the need for edge computing has grown ever more important.
Today, we demand more from networks than ever before. The explosion of intelligent technologies is generating incomprehensible amounts of data every day. As of 2018, we were generating 2.5 quintillion (that’s 2.5 with 18 zeros on the end!!) bytes of data each day, but that pace is only accelerating with the growth of the Internet of Things. To put this into context, over the last two years alone 90% of the data in the world, has been generated.
While these numbers might sound impressive, to actually enable 100% accuracy and delivery of data to end users, you need a higher capacity network, computational power and lower latency because intelligent technologies will only be useful when a response is received in real time. But unfortunately, many networks can’t cope with the demands. Latency is fast becoming a daily issue for many organisations who are trying to get real-time insights into their data. But this is exactly where the edge comes in.
Edge computing works by eliminating the need to transport data to a centralised location, enabling data processing, as well as fewer latency issues due to the fact that data is no longer being transferred to and from the cloud or on-premises servers. By decentralising this data handling process, storing and processing data on the edge of the network, and only sending out data that will be used by cloud servers, it will help to save both bandwidth and server space. IDC recently predicted that by the end of 2019, 45% of IoT-created data will be stored, processed, analysed, and acted upon near the edge of the network. But to really make sense of it and the benefits it’s having on different industries, let’s take a look at a few use cases.
1. Predictive maintenance
The manufacturing industry heavily relies on the performance and uptime of automated machines. A study in 2017 carried out by Oneserve in partnership with British manufacturers discovered downtime was costing UK manufacturers more than £180bn every year. And this number is set to increase as rising financial investment in technologies and the growing profitability in the market make unexpected service interruptions more expensive.
But with Edge computing, this could all change. With edge computing, IoT sensors can monitor machine health and identify signs of time-sensitive maintenance issues in real-time. The data is analysed on the manufacturing premises and analytics are uploaded to centralised cloud data centres for reporting or further analysis. Analysing anomalies can allow the workforce to perform corrective measures or predictive maintenance earlier, before the issue escalates and impacts the production line. Analysing the most impactful machine health metrics can allow organisations to prolong the useful life of manufacturing machines. As a result, manufacturing organisations can lower the cost of maintenance, improve operational effectiveness of the machines and realise higher return on assets.
2. Voice Assistance
Voice Assistance technologies such as Amazon Echo, Google Home and Apple Siri are pushing the boundaries of AI. Gartner predicts that 30% of consumer interactions with the technology will take place via voice by 2020. But this fast-growing consumer technology segment requires advanced AI processing and low-latency response time to deliver effective interactions with end-users.
Particularly for use cases that involve AI voice assistant capabilities, the technology needs to go beyond computational power and data transmission speed. The long-term success of voice assistance depends on consumer privacy and data security capabilities of the technology. Sensitive personal information is a treasure trove for underground cybercrime rings and potential network vulnerabilities could pose unprecedented security and privacy risks to end-users. To address this challenge, vendors such as Microsoft and Amazon are enhancing their AI capabilities and deploying the technology closer to the edge, so that voice data doesn’t need to move across the network.
3. Fleet Management
Logistics service providers leverage IoT telematics data to realize effective fleet management operations. Drivers rely on vehicle-to-vehicle communication as well as information from backend control towers to make better decisions. Locations of low connectivity and signal strength are limited in terms of the speed and volume of data that can be transmitted between vehicles and backend cloud networks. With the advent of autonomous vehicle technologies that rely on real-time computation and data analysis capabilities, fleet vendors will seek efficient means of network transmission to maximize the value potential of fleet telematics data for vehicles travelling to distant locations.
By drawing computation capabilities in close proximity of fleet vehicles, vendors can reduce the impact of communication dead zones as the data will not be required to send all the way back to centralised cloud data centres. Effective vehicle-to-vehicle communication will enable coordinated traffic flows between fleet platoons, as AI-enabled sensor systems deployed at the network edges will communicate insightful analytics information instead of raw data as needed.
As companies continue to make their operations smart, the market is set to make significant gains to keep up with the compute needs of those platforms. The edge is no longer in proof-of-concept phase; it has entered into mainstream adoption and is predicted to grow at a compound annual growth rate of 41%. And when we consider the ongoing research and developments in AI and 5G connectivity technologies, coupled with the rising demands of smart industrial IoT applications, edge computing may reach maturity faster than expected.