Combination of DNN and Improved KNN for Indoor Location Fingerprinting

• Published in: Wireless communications and mobile computing Vol. 2019; no. 2019; pp. 1 - 9
• Main Authors: Yan, Ruqiang, Wang, Manyi, Yang, Yuan, Dai, Peng
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; John Wiley & Sons, Inc
• Subjects: Access control; Accuracy; Advantages; Algorithms; Angle of arrival; Artificial neural networks; Classification; Clustering; Decision trees; Fingerprinting; Global positioning systems; GPS; K-nearest neighbors algorithm; Lagrange multiplier; Localization; Machine learning; Methods; Network adapters; Neural networks; Researchers; Signal strength; Support vector machines; Wireless access points; Wireless networks
• ISSN: 1530-8669; 1530-8677; 1530-8677
• DOI: 10.1155/2019/4283857

Fingerprinting based on Wi-Fi Received Signal Strength Indicator (RSSI) has been widely studied in recent years for indoor localization. While current algorithms related to RSSI Fingerprinting show a much lower accuracy than multilateration based on time of arrival or the angle of arrival techniques, they highly depend on the number of access points (APs) and fingerprinting training phase. In this paper, we present an integrated method by combining the deep neural network (DNN) with improved K-Nearest Neighbor (KNN) algorithm for indoor location fingerprinting. The improved KNN is realized by boosting the weights on K-nearest neighbors according to the number of matching access points. This will overcome the limitation of the original KNN algorithm on ignoring the influence of the neighboring points, which directly affect localization accuracy. The DNN algorithm is first used to classify the Wi-Fi RSSI Fingerprinting dataset. Then these possible locations in a certain class are also classified by the improved KNN algorithm to determine the final position. The proposed method is validated inside a room within about 13⁎9 m2. To examine its performance, the presented method has been compared with some classical algorithms, i.e., the random forest (RF) based algorithm, the KNN based algorithm, the support vector machine (SVM) based algorithm, the decision tree (DT) based algorithm, etc. Our real-world experiment results indicate that the proposed method is less dependent on the dense of access points and indoor radio propagation interference. Furthermore, our method can provide some preliminary guidelines for the design of indoor Wi-Fi test bed.