Section: New Results

Extracting usage patterns of home IoT devices

Participants: Vassilis Christophides, Gevorg Poghosyan (Insight Centre for Data Analytics), Ioannis Pefkianakis (Hewlett Packard Labs), Pascal Le Guyadec (Technicolor)

We have initially investigated how data analytics for Machine-to-Machine (M2M) data (connectivity, performance, usage) produced by connected devices in residential Intranet of Things, could support novel home automation services that enrich the living experience in smart homes. We have investigated new data mining techniques that go beyond binary association rule mining for traditional market basket analysis, considered by previous works. We design a multidimensional pattern mining framework, which collects raw data from operational home gateways, it discretizes and annotates the raw data, it produces traffic usage logs which are fed in a multidimensional association rule miner, and finally it extracts home residents' habits. Using our analysis engine, we extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a n-play service. Such fine-grained device usage patterns provide valuable insights for emerging use cases, such as adaptive usage of home devices (aka horizontal integration of things). Such use cases fall within the wider area of human-cognizant Machine-to-Machine communication aiming to predict user needs and complete tasks without users initiating the action or interfering with the service. While this is not a new concept, according to Gartner cognizant computing is a natural evolution of a world driven not by devices but collections of applications and services that span across multiple devices, in which human intervention becomes as little as possible, by analyzing past human habits. To realize this vision, we are interested in co-usage patterns featuring spatio-temporal information regarding the context under which devices have been actually used in homes. For example, a network extender which is currently turned off, could be turned on at a certain day period (e.g., evening) when it has been observed to be highly used along with other devices (e.g., a laptop or a tablet). Alternatively, the identification of frequent co-usage of particular devices at a home (say iPhone with media player), could be used by a things’ recommender to advertise the same set of devices at another home (say another iPhone user could be interested in a media player).