Mining Usage Patterns in Residential Intranet of Things

Abstract : Ubiquitous smart technologies gradually transform modern homes into Intranet of Things, where a multitude of connected devices allow for novel home automation services (e.g., energy or bandwidth savings, comfort enhancement, etc.). Optimizing and enriching the Quality of Experience (QoE) of residential users emerges as a critical differentiator for Internet and Communication Service providers (ISPs and CSPs, respectively) and heavily relies on the analysis of various kinds of data (connectivity, performance, usage) gathered from home networks. In this paper, we are interested in new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous works, to analyze individual device logs of home gateways. Based on multidimensional patterns mining framework, we extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Such fine-grained device usage patterns provide valuable insights for emerging use cases such as an adaptive usage of home devices, and also “things” recommendation.
Type de document :
Communication dans un congrès
The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops, May 2016, Madrid, Spain. 2016
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https://hal.inria.fr/hal-01317634
Contributeur : Vassilis Christophides <>
Soumis le : mercredi 18 mai 2016 - 16:30:47
Dernière modification le : jeudi 19 mai 2016 - 01:14:57

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  • HAL Id : hal-01317634, version 1

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Vassilis Christophides. Mining Usage Patterns in Residential Intranet of Things. The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops, May 2016, Madrid, Spain. 2016. 〈hal-01317634〉

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