AtLAS: An Activity-Based Indoor Localization and Semantic Labeling Mechanism for Residences

Currently, indoor localization technology and indoor location-based services are becoming increasingly important in the area of mobile and ubiquitous computing. However, the design of an indoor location-based system confronts two challenges: 1) achieving high-precision location recognition and 2) identifying what indoor objects actually are (which is called semantic labeling). In this article, we propose AtLAS, an activity-based indoor localization and semantic labeling mechanism. The key idea is that some objects in an indoor environment, such as doors and toilets, determine predictable human behaviors in small areas, which can be reflected in unique sensor readings. AtLAS leverages this idea to determine a user’s accurate location by identifying users’ activities. Furthermore, we leverage the topological structure of indoor objects to mine the semantic knowledge and label the objects through gained knowledge automatically. To the best of our knowledge, AtLAS is the first attempt to build a system that leverages users’ activities to conduct a high-precision indoor localization and semantic labeling system for the case of residences. The experimental results show that AtLAS can achieve a median localization accuracy of 0.57 m, and the system can localize the landmarks with a median accuracy of 0.43 m on average without 5% worst errors. AtLAS can label the objects semantically with a 5.7% false-positive rate and a 5.8% false-negative rate on average.