A Novel High Precision and Low Consumption Indoor Positioning Algorithm for Internet of Things

Abstract

Internet of Things (IoT) is digitizing the world, and indoor positioning is one of the important applications of them. Indoor positioning refers to the realization of positioning in the indoor environment. The recent research on indoor positioning focuses on Wi-Fi-based methods since GPS cannot achieve the desired effect. A core algorithm in those methods is the {K} nearest neighbor (KNN) search. In this paper, we proposed an improved indoor positioning algorithm named IpKNN with better accuracy and efficiency. The IpKNN mainly includes two parts. The first part is to use the proposed clustering algorithm to classify the data set, which can improve the computational efficiency. The second part is to improve the positioning accuracy by using the proposed KNN algorithm. The proposed algorithm can achieve high precision and low consumption, and the experiment results also proved it.