Selecting Critical WiFi APs for Indoor Localization Based on a Theoretical Error Analysis

Abstract

The radio map built during the offline phase of any WiFi fingerprint-based localization system usually scales with the number of WiFi access points (APs) that can be detected at a single location by any mobile device, but this number in practice can be as large as 100, only a few of which essentially contribute to localization. Simply involving all the APs in location fingerprints and thus in the radio map not only wastes excessive storage but also leads to a large computational overhead. In this paper, a theoretical analysis of WiFi-based location determination is conducted to investigate the resulting localization errors and reveals that some critical APs contribute significantly more to localization than the others, implying that it is unnecessary to include every AP in localization. Consequently, a heuristic AP selection algorithm based on the error analysis is proposed to efficiently select a subset of APs for use in localization. Finally, extensive experiments are carried out by using both the UJI dataset available online and the dataset collected in our laboratory, and it is shown that the proposed algorithm not only significantly reduces the redundancy of APs in WiFi fingerprint-based localization but also substantially improves the localization accuracy of the k nearest neighbor (KNN) method.

Indoor Localization Enhancement Based on Time of Arrival Using Sectoring Method

“…Where γ is path loss exponent can be assigned to 2 in LOS and 6 in harsh environments. The variable S is lognormal shadowing between (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) assigned based on environments after trying many values to select the suitable value. Finally, m is random variable use in TOA method based on expectation error.…”

“…The regrisive RSS increse error range, so the position three is better one. To confirm effeteness of propos technique, Table 6., listed the results obtained in [2] compared with our technique. The comparison confirm that the select of Wi-Fi technology is prefer chose according to performance and availability.…”

“…The electronic accessories proliferate and become a part of our daily life, so the position can be estimated by using mobile phones, laptops, tablets, wearable devices, or other smart devices. The Global Position System (GPS) is splendid in outdoor environment but indoor environment is different, so the GPS doesn't work so good [1,2]. The concept widely uses for indoor localization is Wireless Fidelity (Wi-Fi) like in [3][4][5][6] or Wi-Fi-finger-print for more accuracy [7][8][9].…”

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

“…Lin et al [30] proposed a group discrimination-based access point selection method to improve the positioning accuracy and to reduce the computational overhead. Jia et al [31] proposed a heuristic AP selection algorithm based on an error analysis. It was demonstrated that this method significantly reduced the redundancy of APs in fingerprintbased localization and considerably improved the localization accuracy.…”

Indoor Localization Based on Time of Arrival Using Multi-Zone Technique

“…In this situation, indoor localization systems are used to estimate the position in any area that GPS doesn't work so well in it. For each environment, several methods are used such as, Time of Arrival (ToA) [2], [3], [4], Time Deference of Arrival (TDoA) [5], Angle of Arrival (AoA) [6], and Receive signal strength (RSS) [7], [8], [9], [10], [11]. Also, there are hybrid methods such as, ToA/AoA [12], RSS/ToA [13], [14], and TDoA/RSS [15].…”