Abstract
| Language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Publisher | IOS Press |
| Pages | 157-164 |
| Number of pages | 8 |
| ISBN (Print) | 9781586036232 |
| Publication status | Published - 2006 |
| Event | Smart Homes and Beyond: ICOST2006 - Belfast, Northern Ireland, UK Duration: 1 Jan 2006 → … |
Conference
| Conference | Smart Homes and Beyond: ICOST2006 |
|---|---|
| Period | 1/01/06 → … |
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A Self-Managing Infrastructure for Ad-Hoc Situation Determination. / Thomson, G; Stevenson, G; Terzis, Sotirios; Nixon, Patrick.
Unknown Host Publication. IOS Press, 2006. p. 157-164.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A Self-Managing Infrastructure for Ad-Hoc Situation Determination
AU - Thomson, G
AU - Stevenson, G
AU - Terzis, Sotirios
AU - Nixon, Patrick
PY - 2006
Y1 - 2006
N2 - Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long training periods before the system can be used. This paper presents a novel approach to situation determination that attempts to overcome these issues by providing a reusable library of general situation specifications that can be easily extended to create new specific situations, and immediately deployed without the need of an environment expert. The architecture of an accompanying situation determination infrastructure is provided, which autonomously optimises and repairs itself in reaction to changes or failures in the environment.
AB - Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long training periods before the system can be used. This paper presents a novel approach to situation determination that attempts to overcome these issues by providing a reusable library of general situation specifications that can be easily extended to create new specific situations, and immediately deployed without the need of an environment expert. The architecture of an accompanying situation determination infrastructure is provided, which autonomously optimises and repairs itself in reaction to changes or failures in the environment.
KW - n/a
M3 - Conference contribution
SN - 9781586036232
SP - 157
EP - 164
BT - Unknown Host Publication
PB - IOS Press
ER -