IEEE Future Directions
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Context Awareness
A first area of awareness technologies relates to Context Awareness.
Sensors embedded in the artefact, able to detect the shape of the environment, its characteristics and of its various components, are becoming more sensitive, performant and affordable. Smart materials (like sensitive skin for robots) will be playing an increasing role in sensing. Indirect sensing, such as the one provided by safety cameras, is also relevant in several situations and in the future the exchange of sensed data by components will take an important role (as an example in providing data through exchange among autonomous vehicles in a given area).
The detection of the shape of the environment and of its components can be done in several ways:
- by scanning the ambient, e.g. using laser based technologies like LIDAR, sonar
- by “looking” at the ambient, e.g. using digital cameras (usually more than one or cameras with several lenses to ease the 3D recognition)
- by identifying the objects, e.g using identification methods like tagging, patterns recognition, sound signature…
- by interacting with the objects and sharing knowledge.
Depending on the situation one or another type of sensors can be used. In many cases several types are used in parallel. In general the more data can be harvested the better. Sensors data basically respond to the “what is happening” question.
To respond to the “why it is happening” other technologies take the stage. The aim is to understand the meaning of the harvested data. The semantic extraction correlates data and makes use of a knowledge base (like knowing that a a table is supposed to stand still whilst a dog moves around…). Data correlation may occur across different sources, including social networks when the data relates to people. In case of people, context aware technologies are already widely applied to deliver customized services. Banks are also using context awareness to contain improper use of credit cards….
Context awareness is a fundamental characteristics of autonomous systems and so far these have been designed to have it. In the future as systems will be created through the interaction of autonomous components (each potentially context aware) the overall context awareness may become and emergent property of the whole system, with its individual component sharing individual awareness to generate a global awareness. This is a matter of research for the coming years.
Also notice the shift in the definition of context that is very relevant in the area of symbiotic autonomous systems:
“… The focus moves from seeing a context-aware system as an artefact “sensing” information, to seeing it as an interactive system with a physical user interface. This makes the distinction between foreground and background interaction a property not of the system, but of the situation. A consequence of this philosophical standpoint is that context can never be a property of the world, but that context rather is the horizon within which the user makes sense of the world.”
Context aware computing is clustering a set of technologies and approaches, more and more based on artificial intelligence (deep neural networks) used to extract meaning and in symbiotic autonomous systems it will make increasing use of interaction with other entities to get more data. The data are used to create virtual models of the context on which simulation can be performed. This is crucial to support the analyses of “what will happen”.
The digital twin approach can be used in the simulation, enacting (in the virtual world) several strategies to evaluate the outcome and select the most appropriate one.
