5G has suffered bad press from both detractors and supporters. Spoof stories about it spreading coronavirus were soon dismissed, but banal predictions of refrigerators ordering milk and shoppers wearing headsets to receive advertising were even more likely to blunt our interest. 5G undoubtedly creates the groundwork for an enormous technical revolution but adjusting the central heating with our smartphone or watching B-movies in higher resolution is not the point. Manufacturing and logistics industries will lead the real 5G revolution.
Although the public 5G network will take some time to get up to speed, local area networks can implement true 5G more quickly. This will enable factories, ports, universities, farms and airports to have their own industrial IoT systems (IIOT) today. Numerous factories are already claiming the ‘first’ 5G production lines, including a Nokia factory in Oulu Finland, Worcester Bosch in the UK, Mercedes Benz in Sindelfingen Germany and General Motors in Michigan.
Speed is often mentioned as a key advantage of 5G, but it helps if we break down the meaning of ‘speed’. 5G radio waves don’t move more quickly than 4G ones, rather the entire system has been optimised for faster data transfer. 5G can reduce latency to as little as a millisecond, enabling machinery to respond to sensors almost instantly.
Consider how quickly a driverless car must respond in order to operate safely and you will understand the value of low latency. In a similar way, 5G will enable a whole new generation of robots and automated machinery to radically improve dexterity, quality control and safety. Ericsson’s vice-president Åsa Tamsons explains:
"With one millisecond latency, you can sense whether there is a deviation in the process before the tool even hits the blade and you can stop the machine before the error happens".
‘Edge’ responses in today’s driverless cars are achieved by mounting the control device directly on the vehicle. 5G cars will achieve similar response times but with all the benefits of environmental network connectivity too.
5G also has far broader channels so that more devices can be connected simultaneously. It is said that 5G will soon be able to connect a million devices per square kilometre. Imagine what an engineer could do with ten thousand eyes and ten thousand hands. All the extra data feeding into AI enabled machinery would provide a precise real-time grasp of complex distributed systems and emergent situations with many industrial applications.
Not all 5G systems need to be this fast, but a typical industrial 5G LAN will match a good Ethernet one. A huge disadvantage of Ethernet is the wires, they are expensive to install, prone to breakages and need regular maintenance. In contrast, once setup a wireless 5G system is easy to maintain and reliable (99.9999% or ‘six nines’ reliability).
One reason for hard-wiring a system rather than using ‘wi-fi’ is because most types of wireless connection can fail to penetrate walls and metal obstructions. However, 5G is relayed between multiple small nodes and can re-route itself instantly if a passing tanker or crane blocks any particular path between devices. The technology is called ‘coordinated multi-point’ (CoMP).
Finally, 5G provides much improved network control, including the ability to subdivide the network. Known as ‘network slicing’, this means each virtual sub-net can be customised and optimised for multiple different purposes.
Whether public or private, 5G networks have applications everywhere. By planting sensors in the ground, farmers will know precisely how much water or fertiliser their crops need and when, or query weather satellites and predict their ideal harvest time and yield. Driverless machinery will often deliver it. The health of herds can be monitored remotely and assets tracked across the farm and supply chains.
The IoT has already demonstrated multiple applications in health and fitness. We are beginning to use proximity sensors and temperature sensitive cameras to track disease outbreaks. In the future 5G may be able to stop a public health threat in its tracks. Augmented reality may also facilitate remote examinations, benefitting people in isolation and the NHS system.
5G supports three rather different kinds of technology; smartphone broadband, large-scale IoT and critical ‘edge’ operations. Because smartphone makers need to sell handsets to pay for the public network, some of the more frivolous ‘benefits’ have been hyped. Many people will receive a Samsung S20 this Christmas and wonder what to do with it. However, the real revolution will be quieter and more impressive: few enterprises will be able to ignore 5G and still remain competitive.
The last decade has seen huge advances in artificial intelligence, smart devices and video analytics. The next will see a dramatic increase in the devices built from them. In fact, demand will be so high that we need to start thinking about our capacity to deliver them.
One bottleneck is the networks over which we expect them to connect. As 5G rolls out, 4G is still patchy outside urban areas and the capacity of our networks to carry 5G traffic has been questioned. Its rollout was also somewhat muted by attacks on phone masts by protesters.
Data centres are also feeling the strain. As more companies, individuals and devices link to Cloud services, data centres have to increase capacity, but noise abatement and heat dissipation make expanding or finding new sites a challenge.
The irony is that only a few emerging technologies need an explosively growing network; demand seems to be driven by people rather than machines. Follow any link to a 4G or 5G website and you quickly discover the benefit of being able to download a 2hr movie in 10 seconds. A strange boast considering that almost everyone now streams, not downloads, movies (and we can’t help wondering why they need them on the move).
By comparison, a smart meter reports your gas and electricity usage about six times per day, taking about 3 seconds in total. Smart meters also use data maintaining their network but that only raises their usage to about 1 minute.
Only a few devices need to transmit more than a few kilobytes of information per hour, nothing comparable to a movie download. Visual feeds from cameras are heavier on bandwidth, but how many hours of CCTV footage of empty buildings do we really need to collect on central servers?
The IoT is a outstanding medium for data gathering and remote control; the Cloud is ideal for data storage and leasing advanced applications, but the most exciting frontier is the development of autonomous systems. When we can store sophisticated algorithms on a chip, smart devices are not only less dependent on human management, but also less dependent on networks. Problems such as communication interruption, bandwidth overload, and response latency begin to disappear.
The obvious example is the self-driving car. Not only are they heavily dependent on advanced image recognition but must perform it at a blistering speed. If they had to depend on a remote server for their analytics, they could never match the response times of human drivers. There are several other reasons for providing self-driving cars with a connection (traffic information for example) but the visual analytics that enable it to drive have to be local.
Video feeds are also a heavy load on human observers. CCTV security systems will be more effective when the equipment itself can identify salient events. In fact, the raison d'être for driverless cars is to improve on the situational awareness and sluggish responses of tired human drivers.
Cloud (or other network) dependence is the weak link in many IoT deployments, impairing its speed and reliability. The alternative is to distribute the processing workload close to the edge of the network - near the device. This is often called “Edge computing”.
Rapid situational awareness can often be achieved by incorporating AI or video recognition algorithms onto the device itself, or supplying them in a specialised processing unit in close proximity. This infrastructure can still work in symbiosis with distant resources and control systems, but the bulk of the processing is shifted as close as possible to where it is immediately needed.
In the next few years, real-time information response capabilities will find a multitude of new niches and transform existing ones. For example, video surveillance has been booming for years (in retailing, transport and security systems), but re-establishing those systems on edge architectures will transform their value by making the intelligence they collect actionable.
Knowing which bus ran you over might be useful in an inquest, but we would rather be warned that the bus is coming. Or consider the difference between scouring a police officer’s bodycam footage to see who fired at them, with a system that can recognise a gun and issue a warning that saves their life.
Ideal solutions will often be hybrid. Many systems can learn to recognise faces locally, for example at ATMs and robotic checkouts, yet they can still liaise with central repositories when needed.
Fully autonomous robots are no longer far-fetched, but in the meantime let Net 4 show you how to future proof your video processing systems.
