Optimal Granularity Selection for Indoor Localization Detection with Wireless IoT Networks

Abstract

Indoor localization detection acts as an important issue and has wide applications with wireless Internet of Things (IoT) networks. In recent years, the WiFi-based localization by using the latest artificial intelligence methods for improving the detection accuracy has attracted attention of many researchers. Granular computing is a newly emerged computing paradigm in artificial intelligence, which focuses on the structured thinking based on multiple levels of granularity. Thus, we introduce granular computing approaches to the task of wireless indoor localization detection, and a novel heuristic data discretization method is proposed based on the binary ant colony optimization and rough set (BACORS) for the selection of optimal granularity. For BACORS, the global optimal cut point set is searched based on the binary ant colony optimization to simultaneously discretize multiple attributes. Meanwhile, the accuracy of approximation classifications coined from rough sets is used to determine the consistent of multiple attribute data. To validate the effectiveness of BACORS, it is applied to a wireless indoor localization data set, and the experimental results indicate that it has promising performance.