The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

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IEEE Wireless Communications and Networking Conference

Location positioning in indoor environments is a major challenge. Various algorithms have been developed over years to address the problem of indoor positioning. One of the most cost effective choice for indoor positioning is based on received signal strength indicator (RSSI) using existing Wi-Fi networks in commercial and/or public areas. This solution is infrastructure-free and offers meter-range accuracy. In this paper, machine learning approaches including k-nearest neighbor (k-NN), a rules-based classifier (JRip), and random forest have been investigated to estimate the indoor location of a user or an object using RSSI based fingerprinting method. Experimental measurements were carried out using 1500 reference points with received RSSIs of 86 installed APs in the second floor of Centre for Engineering Innovation (CEI) building at the University of Windsor. The results indicate that the random forest classifier presents the best performance as compared to k-NN and JRip classifiers with positioning accuracy higher than 91%.

Wi-Fi based indoor location positioning employing random forest classifier

This paper proposes a soft range limited K-nearest neighbors (SRL-KNNs) localization fingerprinting algorithm. The conventional KNN determines the neighbors of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user’s previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of 0.66 m with 80% of the errors under 0.89 m, which outperforms conventional KNN algorithms by 45% under the same test environment.

A Soft Range Limited K-Nearest Neighbors Algorithm for Indoor Localization Enhancement

The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

https://www.mdpi.com/2076-3417/11/1/279/pdf

Review of Indoor Positioning: Radio Wave Technology

Advances in mobile technologies and devices has changed the way users interact with devices and other users. These new interaction methods and services are offered by the help of intelligent sensing capabilities, using context, location and motion sensors. However, indoor location sensing is mostly achieved by utilizing radio signal (Wi-Fi, Bluetooth, GSM etc.) and nearest neighbor identification. The most common algorithm adopted for Received Signal Strength (RSS)-based location sensing is K Nearest Neighbor (KNN), which calculates K nearest neighboring points to mobile users (MUs). Accordingly, in this paper, we aim to improve the KNN algorithm by enhancing the neighboring point selection by applying k-means clustering approach. In the proposed method, k-means clustering algorithm groups nearest neighbors according to their distance to mobile user. Then the closest group to the mobile user is used to calculate the MU's location. The evaluation results indicate that the performance of clustered KNN is closely tied to the number of clusters, number of neighbors to be clustered and the initiation of the center points in k-mean algorithm.

Improving RSS-Based Indoor Positioning Algorithm via K-Means Clustering

The interaction between devices and users has changed dramatically with the advances in mobile technologies. User friendly devices and services are offered by utilizing smart sensing capabilities and using context, location and motion sensor data. However, indoor location sensing is mostly achieved by measuring radio signal (WiFi, Bluetooth, GSM etc.) strength and nearest neighbor identification. The algorithm that is most commonly used for Received Signal Strength (RSS) based location detection is the K Nearest Neighbor (KNN). KNN algorithm identifies an estimate location using the K nearest neighboring points. Accordingly, in this paper, we aim to improve the KNN algorithm by integrating a short term memory (STM) where past signal strength readings are stored. Considering the limited movement capabilities of a mobile user in an indoor environment, user's previous locations can be taken into consideration to derive his/her current position. Hence, in the proposed approach, the signal strength readings are refined with the historical data prior to comparison with the environment's radio map. Our evaluation results indicate that the performance of enhanced KNN outperforms KNN algorithm.

Indoor location detection with a RSS-based short term memory technique (KNN-STM)

In the last decade, penetration of mobile technologies has changed our daily lives and the way we interact with each other and the rest of the world. With the latest wireless and mobile technologies, today, mobile users can find out about the congestion levels of motorways and route their trip accordingly. They can access public transport timetables on-the-move and have the data tailored dynamically based on their location. In this work, not only to improve lifestyles but increase life standards, we aim to combine the paradigms above with healthcare and hospital patient records systems. Accordingly, this paper describes a location-aware electronic health record system that can sense the location of the physician by utilizing fingerprinting technique, and retrieve the relevant patient's medical data on to the physician's mobile device. Furthermore, the system also enables medical personnel to transcribe post-it-like, audio-notes, and facilitate communication among physicians on other shifts by posting location-based notes. The prototype system's location precision and usability evaluation results indicate that the proposed system is conceived as easy to use, accurate, and efficient tool.

Bulut Altintas

Papers

Improving RSS-Based Indoor Positioning Algorithm via K-Means Clustering

Indoor location detection with a RSS-based short term memory technique (KNN-STM)

Location-aware patient record access: Patient identification using fingerprinting technique