Teaching computers the meaning of sensor names in smart home

Normally, the information generated by these sensors is processed using data analysis methods, and the most successful systems are based on supervised learning techniques (i.e., knowledge), with someone supervising the data and an algorithm automatically learning the meaning. Nevertheless, one of the main problems with smart homes is that a system trained in one environment is not valid in another one: 'Algorithms are usually closely linked to a specific smart environment, to the types of sensor existing in that environment and their configuration, as well as to the concrete habits of one individual. The algorithm learns all this easily, but is then unable to transfer it to a different environment,' explains Gorka Azkune, a member of the UPV/EHU's IXA group.

Giving sensors names

To date, sensors have been identified using numbers, meaning that 'they lost any meaning they may have had,' continues Dr Azkune. 'We propose using sensor names instead of identifiers, to enable their meaning, their semantics, to be used to determine the activity to which they are linked. Thus, what the algorithm learns in one environment may be valid in a different one, even if the sensors are not the same, because their semantics are similar. This is why we use natural language processing techniques.'

The researcher also explains that the techniques used are totally automatic. 'At the end of the day, the algorithm learns the words first and then the representation that we develop using those words. There is no human intervention. This is important from the perspective of scalability, since it has been proven to overcome the aforementioned difficulty.' Indeed, the new approach has achieved similar results to those obtained using the knowledge-based method.