Adaptive Indoor Localization System for Large-Scale Area

Abstract

Generally, fingerprint-based indoor localization works inefficiently when deployed in a large-scale area. This is because it consumes massive resources and takes long processing time for searching the exact location in the large fingerprint database. Moreover, the changing environment can degrade overall performance. To tackle these problems, we propose an adaptive indoor localization system for a large-scale area. Our system consists of three main parts. First, our area classification algorithm is the key to overcome the problem caused by the large-scale area. It identifies an area of the user's queries whether they are outdoor or located in a specific building. Specifically, the algorithm can filter out the queries sent from outdoor or out-of-scope areas. Then, the information of this part is sent to the next part. Second, our fingerprint-based indoor localization algorithm can utilize the information from the first part by searching only the fingerprint in the specific building. This can significantly reduce searching space and processing time in order to localize the exact location. Third, our missing-BSSID detector algorithm detects the missing Basic Service Set Identifiers (BSSIDs) in the incoming query and updates a sampling database. This part is for our system to quickly adapt to the changing environment. We evaluated and deployed our system in a large-scale exhibition including 37 multi-floor buildings, covering 486,000 m2 and generating approximately 600,000 records of queries from users. In addition, we created a simulation to evaluate our system in the critically-changing environment. Our proposed system achieves high accuracy. More importantly, our area classification algorithm can significantly reduce the overall processing time compared to the previous work. Also, we showed that when applying our missing-BSSID detector algorithm to our system as well as other existing systems, the overall system performance can be significantly improved.