An Intensive Location-Aware Framework for Device-Involved Human Tasks

The daily human tasks are usually involved in various physical devices deployed in local environments. However, it is still painful for users to find the appropriate device to complete these tasks. This becomes a critical issue when massive numbers of devices are increasingly emerging in the people´s daily life, although it is not significant now. Location is a crucial factor for device selection, but most of existing location aware systems ignore the limitation of the real world. In this paper, we propose an intensive location-aware framework, which can automatically find the devices in the vicinity to help users complete the device-involved tasks quickly and easily. A space model and interactive range model are defined to measure the location information in the real world. Based on the proposed models, an intensive location-aware algorithm of device selection is proposed to find out the appropriate device for a given task. A prototype of proposed framework is implemented and several experiments are conducted to verify its performance and usability.