If one pays attention to the hype, 5G and edge networks are the solution to everything. Throw in a bit of AI, NFV, SDN, DSS, MIMO, mmWave, etc., and the wireless world will assume a life of its own, sans any overwatch. They might even self-deploy one day.
Seriously, however, between the edge and 5G, that is not that far from the truth once both get real traction. In fact, the edge, coupled with wide-bandwidth spectrum (read: mmWave), will handle the majority of data communications – and autonomously. However, do not look for this to resemble any kind of reality before about 2025.
I am not going to go into a lot of detail as to why I believe that will be the case. There is plenty of analysis and assessment of both platforms if one is interested in drilling down on the state of these and what other analysts and SMEs think.
It is generally agreed that the majority of 5G-based edge networks will be in highly populated areas running on the mmWave spectrum. The variety of edge cells is virtually unlimited – campuses, malls, city centers, ad hoc emergency communications, venues of all types, the enterprise space, and consumer locations to name a few. There will also be edge networks appearing in manufacturing, transportation, agriculture, mining, and other infrastructures, and, of course, the Internet of Anything/Everything (IoX) (I did not mention telecom because that is a given). With all of these applications (and their subsets), one can see why there is thought that 5G and the edge will be the solution for ubiquitous communications for the majority of applications for nearly everything.
So, why is this such a promising platform? Let us drill down on a few major vectors that can shed some light on this.
Of course, the two most enabling and hottest 5G technologies are low latency and speed (bandwidth). Those are a given. They are the core technologies that will enable the benefits and applications. Dynamic spectrum management as well as frequency agility come in a close second.
The architecture of edge networks – the distributed model of computing – is why all of this works. Couple that with the components just mentioned, and the resultant networks will have a tremendous capability to move data.
MEC has several advantages that core networks do not. The main ones are concurrency of components, lack of a global clock, and independent failure of components. In some cases, it may have a common goal, such as a massive multiplayer online 3D gaming and streaming HD media. Or, in the case of wireless communications between users, the goal is to make the path of the data between users as fast as possible, even with extremely dense traffic. MEC is ideal for that.
Because most of the data stay within the network, users experience much better responsiveness for all real-time remote monitoring or streaming use cases. Such networks also advance real-time remote broadcasting, a collection of virtual realities (XR), machinery control, vehicle-to-everything (V2X), and similar applications making communications as lag-free as possible.
The 5G element of MEC also enhances security (although it does not get quite the same attention as latency or other features). MEC enhances security just by the nature of the topology. While the technical explanation is somewhat lengthy, in essence, when devices enter a defined area with the MEC enhanced service it essentially allows the service to run on a virtual private network (VPN) without the need for any VPN setup. It is transparent to the mobile user and traffic to the service never exists on the public internet. However, that does not mean that this condition is an excuse to get lax on security.
Of all the MEC apps the IoX is likely to be the most challenging use case. IoX devices will have virtually unlimited scenarios. Some might consume a lot of bandwidth. Others will have minimal periodic data transmissions. And there will be a plethora of platforms from low-data rate to mission-critical medical and life-safety to autonomous vehicles.
In most cases, the challenge for IoX MEC will be to aggregate and analyze the data before it becomes actionable. And count on this being big data for many applications. For this AI/MI/ML (artificial intelligence/machine intelligence/machine learning) will be the underlying technologies and systems MEC will use to accomplish this. All of this in a concentrated area will allow for unprecedented functionality and capabilities.
One of the more visible use cases for MEC are sports venues. For some time now, stadiums, particularly football, have been test beds for accelerated and aggregated data and real-time streaming. In case you missed it, at this year’s Super Bowl, one of the Verizon commercials promoted having multiple, real-time camera views of the game via their 5G stadium platform. And there was a lot of 5G smack talk from both Verizon and T-Mo about 5G.
An early example of that was the 2019 Madrid Open tennis matches. MEC was used to offer fans, both inside and outside the stadium access to dozens of cameras, offering a VX-type of experience. Viewers were able to flip, seamlessly, between cameras and add elements such as digital scoreboards, TV, and social feeds as they watched the matches.
This has actually been going on for a few years now. The nuclear networking of sports venues to be able to offer over-the-top content within the confines of the venue. Until 5G came on the scene there was a Brundlefly of Wi-Fi, BT, and other technologies along with licensed spectrum to improve the fan experience. Now, 5G takes that to a new height.
Since then, this vector has picked up a great deal of traction, but only recently has 5G entered the scene. Frankly, this use case is a pretty easy challenge to overcome. The area is contained, all the users are on the same page and the environment has no real restrictions for hardware deployment (private grounds). However, other, more challenging scenarios, particularly where diverse technologies, topologies, and platforms exist, will experience higher levels of difficulty against increased complexities.
For example, for smart city deployments MEC will have to be augmented by things like massive machine-type-communications (mMTC), high-bandwidth media, AI, and its derivatives, big data and more to enable cities to aggregate and analyze data from millions of connected devices close to, and within, the confines of the deployment. This will require this advanced AI, enabled by 5G, to manage the diverse ecosystem of street lighting, parking and traffic control, personal safety, air and water quality, first responders, weather, and more.
Edge computing will be a game-changer, especially when married to 5G. It is the key component to enable and deliver a wide range of news and existing end-user services challenged by high bandwidth demands and latency.
But it is still evolving. For example, IDC predicts that by 2023 more than 50 percent of new enterprise IT infrastructures will be at the edge vs. traditional corporate datacenters. Today that number is around 10 percent. However, this again is not one of the more challenging scenarios.
We have a good grasp on what edge computing can do. However, we have a bit of a stretch ahead before we get all the details and bugs worked out and deploy multitudes of edge networks across all of the ecosystems that can benefit from it.