The widespread proliferation of smartphones during the last decade commenced and accelerated location-based technologies to uphold Location-Based Services (LBS). LBS are offered for both outdoor and indoor applications, and precise location information serves are the backbone of such services. An accurate outdoor location can be accomplished with a Global Positioning System (GPS), which can provide an accurate position of up to a few meters [1
]. Even so, it is unable to compute an accurate indoor position, on account of its frequency limitation to move through walls, roofs, and other solid objects. Even if GPS can localize a user in the indoor especially when the user is close to windows, that location information may not be accurate enough to serve the standards of indoor localization. This is in view of the fact that signals from satellites are attenuated from interfering objects including walls, roofs, and other similar items, and in many scenarios the location error may be larger than the indoor area itself. Such GPS limitations for indoor positioning necessitated the emergence of alternative indoor positioning and localization technologies.
A number of suitable indoor positioning technologies can be hand-picked including Radio Frequency IDentification (RFID) [2
], Vision [3
], Pedestrian Dead Reckoning (PDR), geomagnetic field-based positioning [4
], Bluetooth, and Wi-Fi, which can serve as a prospective alternative to GPS. However, even these chosen technologies are not without merits. Four potential elements of an Indoor Positioning System (IPS) to define its wide applicability are: deployment cost, ease of use, reliability, and accuracy. Although RFID technology is accurate, it operates at a short distance of a few tens of meters. Even if it less expensive, it requires additional hardware in the form of RFID tags and receivers [8
]. Vision-based IPSs do not require any infrastructure and are scalable and cheap. However, IPSs require substantial computational resources to carry out the image matching. Although with modern Graphics Processing Units (GPUs) today’s computers possess enough computational power to do the image matching in real-time, the image matching has its inherent limitations, e.g., image quality, varying light conditions, etc., which limits the wide use of vision-based IPS [9
]. PDR systems leverage the data from Inertial Measurement Unit (IMU) and track the user’s position by estimating the traveled distance and heading. However, PDR systems cannot work alone and always require a starting position that they use to predict the next relative position of the user. The user’s movement in complex environments and varying orientations of the smartphone make it even harder to estimate the accurate position of the user. Furthermore, the error in position estimation is accumulated over time if it is not corrected periodically [10
IPSs, which exploit the Earth’s magnetic (geomagnetic) field, have emerged as an important research area during the past few years. We found a large body of work [11
] that utilizes the Earth’s natural phenomenon of magnetism to locate a user in the indoor. Such systems utilize the magnetic disturbances in the magnetic field caused by synthetic structures, such as the signatures to locate a user. Despite the fact that these systems are pervasive and require no additional cost, this research area is still in its infancy and substantial effort is required to investigate the attitude of such systems on multifarious devices and in complex environments [13
]. Bluetooth opened new possibilities for indoor positioning recently. The lower energy power consumption and higher throughput could be very fruitful, especially in the Internet of Things (IoT)-based LBS. Bluetooth IPSs, however, follow a discovery phase where a Bluetooth device scans for other available devices by executing its inquiry protocol [15
]. The continuous Bluetooth inquiries drain the battery and increase latency in real-time IPS. Some works to reduce the inquiry time have been presented where the delay is reduced to approximately 4 s [16
], but it is still time-consuming and bad for battery life. Additionally, the installation of a large number of Bluetooth devices is required, depending upon the desired level of accuracy and indoor environment complexity.
Wi-Fi-based positioning has been investigated for two decades at least and portrays itself as a strong candidate for indoor positioning. Wi-Fi-based indoor positioning technologies are classified as calibration-free and calibration-based techniques. Calibration-free techniques comprise Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), and their variant modifications, which are formed on triangulation and require Wi-Fi signal transmission time and angle. The coexistence of Line-of-Sight (LOS), and Non-Line-of-Sight (NLOS) in hybrid indoor environments, however, restricts the positioning accuracy of calibration-free techniques. Additionally, calibration-free techniques require time synchronization of the receiver/sender side, additional hardware to calculate the angle, and are prone to multipath propagation [17
]. The use of Wi-Fi Received Signal Strength (RSS) simplifies the aforementioned problems of time synchronization and angle calculation. Furthermore, as Wi-Fi Access Points (APs) have already been deployed in almost every indoor environment, no cost in terms of additional hardware and infrastructure is incurred. These Wi-Fi IPS are calibration-based systems and are called Wi-Fi fingerprinting systems. The fingerprint pioneered by Bahl in [18
] is one of the most popular Wi-Fi positioning approaches today. Fingerprinting refers to the association of various locations in an environment to a particular fingerprint which is unique for that location. In Wi-Fi fingerprinting, a vector which contains the Basic Service Set Identifier (BSSID) and RSS values serves as the fingerprint for a location. Calibration-based Wi-Fi fingerprint IPSs comprise of offline (training) and online (positioning) phases. The offline phase involves the collection of fingerprints at designated locations, normalizing RSS values, and storing them into the database. The online phase, on the other hand, is the positioning process where the user-collected fingerprint is matched against the database to find the user’s approximated position. The positioning phase, however, is affected by many factors including the user’s phone orientation, user’s direction, and the presence of people and obstacles in the environment. Moreover, the use of disparate phones causes huge fluctuations in measured RSS even for the same location at the same time. The above-mentioned factors cause variations in RSS values, resulting in elevated positioning errors. This research aims to solve the pointed out limitations by making the following contributions:
An indoor positioning approach is presented which solves the problem of device dependence. The proposed approach is tested with three different smartphones including Galaxy S8, LG G6, and LG G7.
The proposed approach is tested in a static as well as dynamic environment with human presence. The impact of human body loss is evaluated at a public place with low, medium, and high human presence.
The proposed approach works with different phone orientations in a similar fashion. Also, the testing is performed with user data while walking in different directions.
The rest of the paper is structured as follows. Section 2
describes the limitations of Wi-Fi-based positioning systems. Section 3
overviews the research works related to the current study. Section 4
provides the details of the proposed approach. Section 5
is about the experiment setup, whereas the results are discussed in Section 6
. The conclusions are given in Section 7
3. Related Work
Fingerprint-based Wi-Fi positioning technology is undoubtedly one of the most widely used approaches for indoor positioning, due to its cost-effectiveness over other technologies that operate on additionally installed hardware. Pioneered by [18
], recent years have seen many improvements to the initial fingerprinting method. However, research is underway to mitigate its various limitations, including the impact of dynamic factors, changes of indoor environment, device dependency, etc. RSS, which is the most popular location fingerprint in fingerprint-based Wi-Fi positioning, is pointed out to vary substantially across different devices even for the same indoor settings. A significant RSS variation is observed due to indoor layout, human mobility, and weather conditions [24
]. Two approaches may be applied to reduce the influence of RSS variation on positioning accuracy, whether it is caused by mobility and similar dynamic factors or the inherent limitations of wireless communication. One approach is to enhance and improve the fingerprinting process such that the discrepancy between the offline-collected RSS and user-collected RSS can be minimized. The second approach utilizes other assistive technologies to improve the positioning performance, e.g., the use of pedestrian dead reckoning (PDR), magnetic field positioning, etc.
The authors of [34
], for example, investigated the impact of RSS variation on the positioning accuracy and proposed an improved approach to reducing the positioning error. They propose a two-stage positioning algorithm, which works with Wi-Fi fingerprinting. The segment similarity of Wi-Fi APs is used which considers both the RSS value as well as APs. The fingerprint is made of a feature consisting of three segments. Wi-Fi APs are grouped together or split according to the similarity of their RSS value. Experiment output shows promising results. Similarly, the authors of [35
] introduced the use of Kullback–Leibler Divergence to calculate the RSS similarity with the Euclidean distance between two location fingerprints to reduce the RSS variance effect of different devices. The results show improved positioning accuracy with various phones.
The authors of [36
] investigate the root causes in fingerprint-based positioning responsible for changing the fingerprint of a specific location and propose a method called DorFin. Especially, the RSS inconsistency caused by signal fluctuations and different orientations of commercial smartphones is studied in detail. A discrimination factor is devised that can detect the outlier measurements and reassemble fingerprints to tolerate the transitions. The performance of DorFin is tested in static as well as mobile environments and results are promising. Another noteworthy approach is proposed in [24
], which minimizes the impact of various smartphones on positioning accuracy with RSS weights. The weight RSS (WRSS) approach sorts the collected RSS from stronger to weaker and assigns them weights according to their position in the sorted list. Hereby, it stores the fingerprint of a location as a pair where the first element is the RSS value, and the second element is its weight. The WRSS is made in a similar fashion from the user scan and the Euclidean distance is calculated using RSS value and its associated weight when online positioning is performed. The results show that the approach can minimize the impact of different devices on positioning accuracy. The authors of [37
] suggest the use of additional parameters for RSS matching to enhance the positioning performance. The features of APs order according to the RSS value, max match count, centroid distance, and entropy error are used.
Despite the fact the improved fingerprinting, as well as positioning algorithm, can substantially lower the Wi-Fi positioning error, researchers tend to work with hybrid approaches to overcome such limitations. For example, the authors of [23
] proposed a hybrid approach of radiofrequency and pyroelectric infrared (PIR) sensors. PIR sensors help to identify the zone of the target person and limit the search space. Later a K-nearest neighbor (KNN) approach is applied on Wi-Fi fingerprinting to calculate the exact position of the user.
Similarly, the authors of [38
] propose the concept of pairwise signal strength differences (PSSD) to improve Wi-Fi positioning accuracy. The fingerprints are collected as signal strength difference (SSD), rather than RSS fingerprints, to decrease the effect of different devices. Later the topological position of the user is estimated with PDR which helps to further refine the user position.
The process of fingerprinting in Wi-Fi-based IPS is time-consuming and laborious, therefore various research works focus on finding alternatives to traditional surveys. For example, the authors of [39
] propose a crowdsensing framework called Bayesian Compressive CrowdSensing (BCCS), which predicts the signal map. The signal map is determined using the correlation between spatial, signal, and temporal dimensions of Wi-Fi APs. The signal map construction is jointly carried out using crowdsensing and Bayesian Compressive Sensing. The proposed framework is able to achieve higher accuracy by 20% to 30% and reduces the data collection cost. BCCS is based on iterative process and accuracy of signal map can be increased with more iterations.
Similarly, the authors of [40
] present a fingerprint-based localization system, which can reduce the fingerprinting overhead. The system partitions the area into small regions, where each region is identifiable by the unique AP sequence. After partitioning, each region is assigned a unique AP sequence according to its relative position. Experiment results demonstrate the accuracy of 7.6 m, but sites with thick walls and irregular RSS variation may lead to high error. Similarly, the number of available APs in an area may affect the localization accuracy and lower number of APs lead to worst performance.
Also, Channel State Information (CSI)-based localization systems are investigated to reduce the data collection time and computational resources required for localization. For example, the authors of [41
] present a localization system, which is based on Random Forest-based fingerprinting (RFFP), and utilized the CSI information from different Wi-Fi routers. The impact of the various number of features for RF training and testing is also investigated which determines the optimal number of features to achieve higher accuracy. Experiments show that the RFFP system achieves very similar accuracy for LOS and NLOS cases.
The above-mentioned research works are lacking in multiple ways. First, the research works may not have been tested in a dynamic environment, e.g., human mobility and similar other dynamic factors prevalent in real environments. Second, signal depletion over time has not been tested. Third, hybrid systems may require additional infrastructures like the Bluetooth and PIR sensors. Fourth, the fusion of PDR module requires an accurate starting position and longer data from smartphone sensors are needed as well, which increases latency in real-time systems. We aim to build a method that works with Wi-Fi APs alone and can mitigate the impact of device dependency, time-based RSS variation, and dynamic environments.
5. Experiment Setup
This section describes the experiment set-up and details of the scenarios used in the experiment. The experiment is conducted in Yeungnam University, Korea, using four different buildings with multiple scenarios. Initially, the experiment is performed in a room with an area of 9 × 7 m2
, as shown in Figure 10
shows the experiment path followed in the CRC building (Figure 11
a) and Regional Innovation Cooperation (RIC) building (Figure 11
b), whose dimensions are 70 × 48 m2
and 35 × 15 m2
, respectively. In the same way, Figure 12
shows the testbeds for the Information Technology (IT) building (Figure 12
a) and Textile Engineering (TE) building (Figure 12
b). The area is 92 × 32 m2
and 92 × 42 m2
for IT and TE buildings, respectively. Four smartphones are used to conduct the experiments including two Samsung Galaxy S8, an LG G6, and an LG G7 for the evaluation of the proposed approach. One Galaxy S8 is used to survey the environments to prepare the fingerprint database, and the other three phones are used for real-time testing. The fingerprints are collected in a grid, where each point is located at a distance of 1 m. The small circles shown in Figure 11
, and Figure 12
represent the points where fingerprints are collected. A total of five Wi-Fi scans at each point are collected to build the fingerprinting database.