Abstract
Wi-Fi received signal strength (RSS) fingerprint-based indoor positioning has been widely used because of its low cost and universality advantages. However, the Wi-Fi RSS is greatly affected by multipath interference in indoor environments, which can cause significant errors in RSS observations. Many methods have been proposed to overcome this issue, including the average method and the error handling method, but these existing methods do not consider the ever-changing dynamics of RSS in indoor environments. In addition, traditional RSS-based clustering algorithms have been proposed in the literature, but they make clusters without considering the nonlinear similarity between reference points (RPs) and the signal distribution in ever-changing indoor environments. Therefore, to improve the positioning accuracy, this paper presents an improved RSS measurement technique (IRSSMT) to minimize the error of RSS observation by using the number of selected RSS and its median values, and the strongest access point (SAP) information-based clustering technique, which groups the RPs using their SAP similarity. The performance of this proposed method is tested by experiments conducted in two different experimental environments. The results reveal that our proposed method can greatly outperform the existing algorithms and improve the positioning accuracy by 89.06% and 67.48%, respectively.
Highlights
The rapid development of wireless technology leads people to pay more attention to location-based services (LBS)
It is the most common metric for calculating runtime complexity. It describes the execution time of a task concerning the number of operations required to complete it. All these traditional and proposed methods are based on the W k nearest neighbor (NN) algorithm and the prediction time complexity of W nearest neighbor (k NN) is expressed by: O(k × n × d) where k is the number of neighbors that we considered in the algorithm (k = 4), n is the number of reference points (RPs) in the training dataset, and d is the data dimensionality (RSS vectors received from different access points (APs))
We presented an efficient Wi-Fi indoor positioning method based on the improved RSS measurement technique (IRSSMT) Wi-Fi signal extraction method and the strongest access point (SAP) information-based clustering algorithm
Summary
The rapid development of wireless technology leads people to pay more attention to location-based services (LBS). The GNSS is not applicable for indoor positioning systems due to its signal becoming weaker after penetrating through indoor environments; it fails to produce reliable position information [5]. Different wireless technologies such as Bluetooth (BLE beacon) [6], radio frequency identification (RFID) [7], ultra-wideband (UWB) [8], geomagnetism [9], visible light [10], and Wi-Fi [11]
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