An adaptive K-NN based on multiple services set identifiers for indoor positioning system with an ensemble approach

Location-Based Systems (LBS) for indoor positioning have earned the attention of a large number of researchers in the last decade. The significance of these systems comes from their applications in various fields such as tracking service for elderly people or a patient within large living communities, mobile robot localization, and several security purposes. The Received Signal Strength (RSS)-based fingerprint method has been recognized as one of the promising techniques for Indoor Positioning Systems (IPS.) The K-nearest neighbors (K-NN) is selected for its significant performance with ease of realization. However, the static nature of K-NN, that is, in using a constant number of the nearest neighbors, leads to a serious shortcoming in its accuracy. Also, the nature of the RSS-IPS challenges such as fading due to the multipath of electromagnetic waves inside buildings would mislead the solution of nearest neighbors. These reasons often result in lower perform than expected because of the increase in the distant neighbors’ biasing error. In this paper, we address these challenges by proposing a new method based on multiple services set identifiers (MSSID) to select adaptively the appropriate nearest neighbors, and reject undesired ones. The ensemble technique is also used to enhance the performance by combining the outputs of three adaptive K-NN estimators. The experimental results demonstrate the superiority of the proposed method over the static K-NN-based methods.