Edinburgh Research Explorer

New Domestic Locations: Reconfiguring the home through the Internet of Things

Research output: Contribution to conferencePaper

Related Edinburgh Organisations

Open Access permissions

Open

Documents

  • Download as Adobe PDF

    Rights statement: ©Speed, C., & Barker, C. (2014). New Domestic Locations: Reconfiguring the home through the Internet of Things.. Paper presented at 20th International Symposium on Electronic Art, Dubai, United Kingdom.

    Accepted author manuscript, 12 MB, PDF-document

Original languageEnglish
Publication statusPublished - Nov 2014
Event20th International Symposium on Electronic Art - Dubai, United Kingdom
Duration: 3 Nov 20146 Nov 2014

Conference

Conference20th International Symposium on Electronic Art
CountryUnited Kingdom
CityDubai
Period3/11/146/11/14

Abstract

This paper reflects on the reconstruction of the home as it becomes filtered through that data that is streamed from smart objects. Retrofitting a home for The Internet of Things involves the placement of multiple sensors that record changes in conditions in order to construct a simulacrum of the actual house from which to analyse and form understandings of behaviour and in turn opportunities for connection.

This domestic data shadow (as it might be called) is not just a record of one inhabitants activities within the house, but the sum of all of the activities of all parties. The single routines that constituted patterns of behaviour of personal habit and ownership become mixed in a single database that, without individual signatures, are lost and the house loses it’s cognitive architectures.

The paper explores the implications upon the occupants sense of location as their model of home become reconfigured through the lens of a database. The paper draws upon findings of the Hub of All Things (HAT) project funded by the Research Council’s UK Digital Economy Programme.

Event

20th International Symposium on Electronic Art

3/11/146/11/14

Dubai, United Kingdom

Event: Conference

Download statistics

No data available

ID: 17513722