Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||E-pub ahead of print - 29 Jun 2015|
|Event||The 2nd International Workshop on Smart Environments: Closing the Loop, 2015 - St. Louis, MO, USA|
Duration: 29 Jun 2015 → …
|Workshop||The 2nd International Workshop on Smart Environments: Closing the Loop, 2015|
|Period||29/06/15 → …|
Bibliographical noteReference text:  UnitedNations, “World population ageing 2009,” United Nations, Economics
and Social Affairs, Tech. Rep., 2009.
 J. Synnott, L. Chen, C. Nugent, and G. Moore, “The creation of
simulated activity datasets using a graphical intelligent environment
simulation tool,” in Engineering in Medicine and Biology Society
(EMBC), 2014 36th Annual International Conference of the IEEE, Aug
2014, pp. 4143–4146.
 S. Helal, J. W. Lee, S. Hossain, E. Kim, H. Hagras, and D. Cook,
“Persim - simulator for human activities in pervasive spaces,” in
Intelligent Environments (IE), 2011 7th International Conference on,
July 2011, pp. 192–199.
 M. Weiser, “The computer for the 21st century,” Scientific american,
vol. 265, no. 3, pp. 94–104, 1991.
 X. Hong, C. Nugent, M. Mulvenna, S. McClean, B. Scotney, and
S. Devlin, “Evidential fusion of sensor data for activity recognition
in smart homes,” Pervasive and Mobile Computing, vol. 5, no. 3, pp.
 S. S. Yau, S. K. Gupta, F. Karim, S. I. Ahamed, Y. Wang, and B. Wang,
“Smart classroom: Enhancing collaborative learning using pervasive
computing technology,” II American Society of Engineering Education
 H. Schaffers, N. Komninos, M. Pallot, B. Trousse, M. Nilsson, and
A. Oliveira, “Smart cities and the future internet: towards cooperation
frameworks for open innovation,” in The future internet. Springer,
2011, pp. 431–446.
 X. H. S. D. C.D. Nugent, M.D. Mulvenna, “Experiences in the development
of a smart lab,” International Journal of Biomedical Engineering
and Technology, vol. 2, no. 4, pp. 319–331, 2009.
 M. Buchmayr, W. Kurschl, and J. Kng, “A simulator for generating
and visualizing sensor data for ambient intelligence environments,”
Procedia Computer Science, vol. 5, no. 0, pp. 90 – 97, 2011, the
2nd International Conference on Ambient Systems, Networks and
Technologies (ANT-2011) / The 8th International Conference on Mobile
Web Information Systems (MobiWIS 2011). [Online]. Available:
 M. P. Poland, C. D. Nugent, H. Wang, and L. Chen, “Development of
a smart home simulator for use as a heuristic tool for management of
sensor distribution,” Technology and Health Care, vol. 17, no. 3, pp.
 K. McGlinn, E. O’Neill, A. Gibney, D. O’Sullivan, and D. Lewis, “Simcon:
A tool to support rapid evaluation of smart building application
design using context simulation and virtual reality.” J. UCS, vol. 16,
no. 15, pp. 1992–2018, 2010.
 A. Mendez-Vazquez, A. Helal, and D. Cook, “Simulating events to
generate synthetic data for pervasive spaces,” in Workshop on Developing
Shared Home Behavior Datasets to Advance HCI and Ubiquitous
Computing Research. Citeseer, 2009.
 H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization
for spoken word recognition,” Acoustics, Speech and Signal Proces
- Intelligent sensors
- Smart homes
- Data models