Extracting usage patterns of home IoT devices
| Autor: | Ioannis Pefkianakis, G. Poghosyan, Pascal Le Guyadec, Vassilis Christophides |
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| Přispěvatelé: | Insight Centre for Data Analytics [Dublin], Dublin City University [Dublin] (DCU), Hewlett Packard Labs [Palo Alto], Hewlett Packard Enterprise (Hewlett Packard) (HPE), Technicolor [Cesson Sévigné], Technicolor, Middleware on the Move (MIMOVE), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Rok vydání: | 2017 |
| Předmět: |
IoT
Intranet Service (systems architecture) Computer science business.industry 020206 networking & telecommunications 02 engineering and technology Troubleshooting home networks association rules World Wide Web [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] Network management [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Home automation Analytics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Internet of Things business Efficient energy use |
| Zdroj: | ISCC ISCC 2017-22nd IEEE Symposium on Computers and Communications ISCC 2017-22nd IEEE Symposium on Computers and Communications, Jul 2017, Heraklion, Crete, Greece. pp.1-7, ⟨10.1109/ISCC.2017.8024707⟩ |
| DOI: | 10.1109/iscc.2017.8024707 |
| Popis: | International audience; Ubiquitous connectivity and smart technologies gradually transform homes into Intranet of Things, where a multitude of connected, intelligent devices allow for novel home automation services. Providing new services for home users (e.g., energy saving automations) and Internet Service Providers (e.g., network management and troubleshooting) requires an in-depth analysis of various kinds of data (connectivity, performance, usage) collected from home networks. In this paper, we explore new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous studies, to analyze individual device logs of home gateways. We introduce a multidimensional patterns mining framework, to extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Our results show that our analytics engine provides valuable insights for emerging use cases such as monitoring for energy efficiency, and “things” recommendation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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