One of the discussion points that takes place in the vehicle-to-vehicle connected device, is that vehicle-to-vehicle communications, you've got multiple devices or cars if you will, that are literally interacting with each other, or you've got the device the vehicle communicating back to some infrastructure, where the vehicle communicating into some type of a data center into that type of cloud. If we look at the flow of information into that network as it moves along, that device obviously moving at a relatively fast speed, if you think about the velocity of a human using a cell phone walking around in a metropolitan area. It will remain of magnitude less than a car that's traveling down some interstate at highway speeds. They're going to be significant hand-offs of that device as it moves along. Similarly, if you've got platooning going on in a connected device type of an area, where these devices themselves. If you think about transportation and a fleet of trucks, where platooning may come into play or be interesting. Those devices are cooperatively communicating with each other about their route, about the conditions, about their velocity, about the traffic patterns that they're experiencing these are moving along and they may be doing this actually through a macro network and communicating, out into the larger macro network and then back in order for that control aspect. You're going to want some of that information to be closely managed, rather than driving a deep into the core, into an area that we're going to talk about as edge computing. You may in fact take some of that compute resources in the platooning example and drive some of that functionality closer to those roadside edge units, to minimize that delay and to improve the performance actually of the network. So, that if you've got multiple devices in here, you're not congesting the network carrying that information deep into it, when you know that you've got a cooperative set of platforms that are in close proximity to each other. But they're still using that macro network for that communication, through to each other in that environment. So, that we've got a single management point if you will, and consolidation point of those devices as we flow along. Another use case for the connected car is the car-to-cloud architecture. Here, we're interested in the functionality of virtualization, reconfigurability, software-defined management of it, and automation of those functional controls and this is a bi-directional flow. So, for everything that flows from the left, we've got an equal amount of information that's flowing to the right. That's a little bit different than what we think about from the control plane center aspect, where typically that's a heavy unidirectional type of flow of information. But here, we may be looking at not always center collection and sensor fusion, but storage and processing capability taking place on the platform itself, transferring that information then in a consolidated way over, through some macro network, in some cases that may be a 4G LTE style network, we'll talk about that in a moment. In an environment that we've not upgraded to the 5G space, because when we roll 5G out, it's not going to be a flashcard, it's not going to be ubiquitously deployed simultaneously, maybe some short period of time in which it's enabled and if we've got devices that are out there they're 5G enabled, those devices we'll need to interoperate with a 4G network in addition to a 5G network, and that consolidation harmonies take place onto that platform. Again, information flowing into those roadside units, where we may be able to provide some type of edge compute capability in those devices deeper into that network, we can perform analytics or location services on their. Interesting areas of functionality that we're still talking about and there's been some debate going on inside the industry itself, is to how much augmented reality services for fastener awareness we might be able to introduce into this network into these devices, as well as virtual reality type of services for, again, if you think about entertaining the passengers and driver type usage. Ultimately, these functionalities are going to be driven by the use cases that the devices themselves and the users of those devices are going to drive any industry. So, we're going to build this 5G network and we're going to enable these cars and we're going to discover that, what we thought was going to be the greatest use cases is likely different than the actual one that develops. Highly likely, that Smart City services are going to come into play here again, so when we think about very dense metropolitan areas, the ability to control traffic flow and communicate effectively to those drivers with examples like that smart parking example that we've given you, are going to come into play here. Then, all the same time a lot of this information is going to flow deeper into the network. So, for example, if you've got an autonomous vehicle, that's able to provide a lot of sensory information that the manufacturer in fact, may be interested in how that platform is performing certain collect analytics. Not just for maintenance activities, but also to improve the quality of their platform as it moves forward. So, if they can collect that information, they can actually act, either can perform analytics based on that information.
