Abstract
Location-based service (LBS) has aroused general interest in both industrial and academic fields. Various scenarios require different localization techniques. Outdoor localization usually needs GPS as the core means and draws support from other sensors or methods. When the situation comes to indoor localization, WiFi received signal strength (RSS), channel state information (CSI), and bluetooth form the mainstream trends. However, there is an obvious gap between these two localization architectures. In this study, we devise an “IOS” (I ndoor, O utdoor, and S emi-open) detection system. For the lightweight need, a WiFi-based sub-detector is implemented. Using only WiFi sensor and a weak learner, the sub detector is easy to use and energy saving. For the precision demand, an aggregated IOS detector based on a semi-CRF (semi-Markov conditional random fields) algorithm is designed. The attribute of semi-CRF uncovers the interdependency between IOS states, so that it guarantees the accuracy of the detection. The evaluation results show that IOS detector can achieve an over 96% accuracy in tested environments. This detection technique holds the potential to realize the seamless localization from indoor to outdoor, and vice versa.
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