User Mobility Model in an Active Office

Abstract

User mobility in an Active Office represents human activity in a context awareness and ambient intelligent environment. This paper describes user mobility by detecting their changing locations. We have explored precise, proximate and predicted user location using a variety of sensors (e.g. WiFi and Bluetooth) and investigated how the sensors fit in an Active Office to provide interoperability to detect them. We developed a model to predict and proximate user location using wireless sensors in the Merino layering architecture, i.e. the architecture for scalable context processing in an Intelligent Environment.