Wi-Fi Direct provides a method for close-range device-to-device communication. A method for similar direct device-to-device communication has been developed for LTE handsets called “LTE Direct”.
The term Ambient Awareness may be used with virtually any wireless technology used for either short range communication, however, it is arguably most closely associated with LTE Direct (LTE-D). More specifically in an LTE-D context, the ability to continuously and passively monitor and identify services for relevant value in the mobile applications proximity is known as Ambient Awareness.
By way of Ambient Awareness, Direct Services will enable a whole new class of services to emerge that will include social networking, advertising, navigation, public safety, friend finder/affinity, and more all being ambient aware. Ambient awareness allows the auto-discovery of people or services or interest in proximity to the user.
Ambient Awareness is all about Discovery and it depends upon something that its inventor, Qualcomm, refers to as an “Expression”.
An Expression is either a 64-bit or 128-bit message that originates from the physical layer (PHY) of one device and is received at the PHY of a receiving device. LTE Direct expressions can be either public or private and discreet.
A public Expression can be pushed passively, meaning that there is no recognition of a message being completed from the perspective of the broadcasting device. In the public forum this is important as consumers have reservations about personal anonymity.
Public Expressions are a common language available to any application. When a LTE Direct expression is broadcast, it is “discovered” by an end-user device. An end-user could pre-program their device to queue the discoveries and recognize certain terms that are called “affinities.”
With LTE Direct, Direct Devices would regularly broadcast expressions advertising what services are being offered. Other direct devices and services in range can receive those expressions and if a device detects a service that it wants to use, that application can then go into action, responding to the requestor. For example, if two friends have devices that are sending out expressions announcing a social-networking app that both of them use, and then each friend will receive a notification that the other friend is nearby.
Unlike current location-based services, which rely on a central database of location data, LTE Direct finds nearby devices directly over the air. Finding a match between one user and other people or services nearby is also quicker, because “service layer” information is contained in the 128-bit expression. The service layer information contains the information to determine if services are of interest. To determine these things with LTE Direct, it’s not necessary to query a central server over the Internet or even to establish a dedicated connection with the nearby device.
Ambient Awareness allows the auto-discovery of people or services or interest in proximity to the user. Mind Commerce sees this extending beyond people-to-people to people-to-machines (also machines-to-people) as well as machine-to-machine as is the case today with the historical M2M solutions.
With these self-ware systems there is a notion of embedded intelligence that will benefit from autonomous communications in an open, many-to-many fashion in which artificial intelligence and machine learning becomes a ubiquitous aspect of non-human communications.
LTE-D and Discovery of People
Person-to-Person (P2P) communications fundamentally speaking is all about the discovery of things. Sometimes the discovery of things is for the direct benefit of people and sometimes it’s for the indirect benefit.
When we speak of the discovery of people what we really mean is the use of P2P system assets for purposes of identifying people that have certain interests in common.
In other sections of this report we talk about something called an Expression. This is the data element that is used to define preferences and interests which from a person-to-person perspective translates into discovery of other people that have the same interests.
We see the ecosystem evolving as a social network and having a great impact on both existing social networks as well as new social networks that will spring up. These social networks will be underpinned by the fact that people have common interests and are willing and able to engage with one another either through conversation, messaging, or multimedia when they come within range of one another.
LTE-D and Discovery of Machines
Machine-to-Machine (M2M) communications is nothing new. Mind Commerce has written many reports about M2M. However now with P2P communications M2M takes on a whole new meaning has low bandwidth applications now can leverage LTE-D for subsequent P2P communications. High bandwidth telematics applications can take advantage of many new multimedia features. This will lead too many value added service applications for both of consumer retail customers as well as enterprise.
LTE-D and Discovery of Applications
In today’s environment, discovery of applications is all about searching with a smartphone to find apps that somebody wants to use in the future. With LTE-D this all changes as the discovery of apps can have more to do with one’s location then they do about a static decision about what to download onto the phone. Apps can also be transient in the sense that they’re only used my certain location and do not necessarily need to reside on the phone and instead are only used when the end-user happens to be in a certain location and/or within the proximity of another specific person or object.
LTE-D Use Case Types
This type of use case scenario involves the end-user making a decision in an on-demand basis to in essence pull information. A discreet request is made for information. This may occur as a result of some trigger such as being alerted of another person or application being in the area. Therefore it may have a lot to do with the proximity of the person relative to other people or things. However, it also may be just at the whim of the end user has to decide to engage in something.
Ambient Awareness and User/Object Push
While the preceding scenario may be triggered by ambient awareness in the sense that it causes an end-user to do something, the push scenario is completely autonomous. In other words ambient awareness is the tool that is used to cause a trigger for autonomous action whereby the application, or information, or content finds the end user rather than the other way around. This is why it is so important to have a database for end-users to manage their preferences and also establish and maintain who and what they want to interact with and engage in P2P.
Use Case Scenarios
We see different LTE-D Use Case Scenarios based on variations of Private and Public Communications.
This use case scenario is all about ambient awareness alerting two or more people that somebody is near them that may have an interest to them. This allows them to choose to engage with one another or not and to have some level of communications and or sharing a car.
By definition, this scenario is private in so much as the end-users have predetermined what (specific) people and/or specific interests they want to engage.
This scenario involves someone communicating with the machine. Since this is the private example the type of machine including this communication would be something under the control of the end user. Machine types could include home or office electronics or something else at the end user is familiar with and have some sense of control over.
This scenario is very similar to the last one. However it is different in the sense that the machine is initiating communications with the person rather than the other way around. This again will be based on ambient awareness and some level of preference and control on the part of the end user. Therefore once again the machine will be some object that is under at least partial control of the end-user.
The common element of this scenario with the preceding examples is that once again the two machines in question are under control of an end-user or some other entity such as a business that has a vested interest in the two machines.
One example of how this could work in practice would be an automobile communicating with a garage to let it know that it is within the proximity. The end-user may or may not program the garage door automatically open or perhaps they may be prompted to open the garage. That is just a detail, however, as the main point is that one machine is communicating with another machine for the benefit of some end user which may be either a consumer customer or an enterprise.
Person to Machine (public)
Perhaps the best example of this scenario would be a person being alerted that they are within the proximity of an object such as a public kiosk. The person would subsequently pull information, or pull something else such as content, or engage in some type of commerce (initiated by the person, not the machine). The main point here is that it is a public situation where the kiosk can be used by many people and in this situation it is a person that is in the person initiating the P2P and not the machine.
Machine to Person (public)
This scenario is very similar to the last one with the significant difference that the machine is initiating the communications. Another significant difference is that the communications occurs completely autonomously and any engagement that occurs after the ambient awareness event may be completely automatic but yet controlled from preference perspective by the end user.
Perhaps one of the best examples for machine-to-machine communications on a public is collision prevention. LTE direct could be used in vehicles to prevent cars from colliding with one another because they are alerted when they are within the proximity of one another.