NIST Smart Space: Pervasive Computing Initiative
WETICE '00 Proceedings of the 9th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
DECLARE: Full Support for Loosely-Structured Processes
EDOC '07 Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
Multimodal identification and tracking in smart environments
Personal and Ubiquitous Computing
An Agent-Based Data-Generation Tool for Situation-Aware Systems
IE '11 Proceedings of the 2011 Seventh International Conference on Intelligent Environments
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
UbiREAL: realistic smartspace simulator for systematic testing
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Learning Setting-Generalized Activity Models for Smart Spaces
IEEE Intelligent Systems
DIVAs 4.0: a framework for the development of situated multi-agent based simulation systems
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In the recent years there has been a growing interest in the design and implementation of smart homes, and smart buildings in general. The evaluation of approaches in this area typically requires massive datasets of measurements from deployed sensors in real prototypes. While a few datasets obtained by real smart homes are freely available, they are not sufficient for comparing different approaches and techniques in a variety of configurations. In this work, we propose a smart home dataset generation strategy based on a simulated environment populated with virtual autonomous agents, sensors and devices which allow to customize and reproduce a smart space using a series of useful parameters. The simulation is based on declarative process models for modeling habits performed by agents, an action theory for realizing low-level atomic actions, and a 3D virtual execution environment. We show how different configurations generate a variety of sensory logs that can be used as input to a state-of-the-art activity recognition technique in order to evaluate its performance under parametrized scenarios, as well as provide guidelines for actually building real smart homes.