Location Fingerprinting Technique Research Articles

Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning

This article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting technique. The fingerprint of a location, in this case, is a set of Wi-Fi ToFs between the target device and an access point (AP). Therefore, the number of APs in the area dictates the set size. The location fingerprinting technique requires a collection of fingerprints of various locations in the area to build a reference database or map. This database or map contains the information used to carry out the main task of the location fingerprinting technique, namely, estimating the position of a device based on its location fingerprint. For that task, we propose using a fully connected deep neural network (FCDNN) model to act as a positioning engine. The model is given a location fingerprint as its input to produce the estimated location coordinates as its output. We conduct an experiment to analyze the impact of the available AP pair in the dataset, from 1 unique AP pair, 2 AP pairs, and more, using WKNN and FCDNN to compare their performance. Our experimental results show that our IPS, DeepIndoor, can achieve an average positioning error or mean square error of 0.1749 m, and root mean square error of 0.5740 m in scenario 3, where 1–10 AP pairs or the raw dataset is used.

Monitoring the Health of the Alzheimers Patients and Geofencing their Location

Abstract: The aim of this project is to help the care takers of the Alzheimer patients who losses their memory and have minimal sense to take care of themselves. So it is sensitive to handle them without anyone’s external guidance and presence, which is not always possible in this busy world and patients may not feel comfortable under the watch out of external guidance always. So there is need for a device which will make the job of the care taker much easier by replacing that external guidance with an electronic device. This project is aiming to give the guardian, the patient’s location (using Geofencing System) along with their health parameters. This will fix the boundary for the patients movement and when if they move out their boundary location, an alert message will be given to the concerned guardian. The health parameters of the patients are also lively updated in the App. Bluetooth Low Energy (BLE) location fingerprinting technique is used for Geofencing system. As our project mainly aims for the Indian busy streets, this geofencing system mainly deals with the indoor localization.

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Most existing wireless indoor positioning systems have only success performance requirements in normal operating situations whereby all wireless equipment works properly. There remains a lack of system reliability that can support emergency situations when there are some reference node failures, such as in earthquake and fire scenarios. Additionally, most systems do not incorporate environmental information such as temperature and relative humidity level into the process of determining the location of objects inside the building. To address these gaps, we propose a novel integrated framework for wireless indoor positioning systems based on a location fingerprinting technique which is called the Robust and low Complexity indoor positioning systems framework (RoC framework). Our proposed integrated framework consists of two essential indoor positioning processes: the system design process and the localization process. The RoC framework aims to achieve robustness in the system design structure and reliability of the target location during the online estimation phase either under a normal situation or when some reference nodes (RNs) have failed. The availability of low-cost temperature and relative humidity sensors can provide additional information for the location fingerprinting technique and thereby reduce location estimation complexity by including this additional information. Experimental results and comparative performance evaluation revealed that the RoC framework can achieve robustness in terms of the system design structure, whereby it was able to provide the highest positioning performance in either fault-free or RN-failure scenarios. Moreover, in the online estimation phase, the proposed framework can provide the highest reliability of the target location under the RN-failure scenarios and also yields the lowest computational complexity in online searching compared to other techniques. Specifically, when compared to the traditional weighted k-nearest neighbor techniques (WKNN) under the 30% RN-failure scenario at Building B, the proposed RoC framework shows 74.1% better accuracy performance and yields 55.1% lower computational time than the WKNN.

CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

The existence of mobile devices as a location pointing device using Global Positioning System (GPS) is a very common thing nowadays. The use of GPS as a tool to determine the location of course has a shortage when used indoors. Therefore, the used of indoor location-based services in a room that leverages the use of Access Point (AP) is very important. By using the information of the Received Signal Strength (RSS) obtained from AP, then the location of the device can be determined without the need to use GPS. This technique is called the location fingerprint technique using the characteristics of received RSS’s fingerprint, then use it to determine the position. To get a more accurate position then authors used the K-Nearest Neighbor (KNN) method. KNN will use some of the data that obtained from some AP to assist in positioning the device. This solution of course would be able to determine the position of the devices in a storied building.

Dealing with Insufficient Location Fingerprints in Wi-Fi Based Indoor Location Fingerprinting

The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.

Radial Reference Map-Based Location Fingerprinting Technique

In this paper, we propose a radial reference map-based location fingerprinting technique with constant spacing from an access point (AP) to all reference points by considering the minimum dynamic range of the received signal strength indicator (RSSI) obtained through an experiment conducted in an indoor environment. Because the minimum dynamic range, 12 dBm, of the RSSI appeared every 20 cm during the training stage, a cell spacing of 80 cm was applied. Furthermore, by considering the minimum dynamic range of an RSSI in the location estimation stage, when an RSSI exceeding the cumulative average by ±6 dBm was received, a previously estimated location was provided. We also compared the location estimation accuracy of the proposed method with that of a conventional fingerprinting technique that uses a grid reference map, and found that the average location estimation accuracy of the conventional method was 21.8%, whereas that of the proposed technique was 90.9%.

Location Fingerprinting Technique for WLAN Device-Free Indoor Localization System

Device-free indoor localization (DFIL) system can locate the position of human body in the indoor environment by observing the changes in the received signal strength indicator (RSSI) of the wireless local area network (WLAN). The accuracy of a DFIL system is depreciated, as the change in the indoor environment due to furniture and other infrastructure movement. This paper investigates the development of testbed of the WLAN network for measuring the RSSI in various indoor environment, as the initial step for designing the fingerprinting-based algorithms for WLAN network. The database of RSSI fingerprint is created initially and then a fingerprint-based algorithm is developed for locating the position of a human body in the indoor environment. The localization algorithm tests the minimum distance in the RSSI values related to the different test points in the indoor environment. This work further demonstrates that how the fingerprints of RSSI are collected and which network configurations generate the most reliable RSSI measurement. For the first phase of designing the testbed, the configurations of different equipment and various tools are elaborated in the indoor environment. For the second phase the RSSI is measured in different propagation indoor environment. The extensive experiments were performed that allow quantification of how changes in an environment affect accuracy. Thus, it is demonstrated that each link offers a viable approach to developing a more robust system for device-free localization that is less susceptible to changes in the environment.

Extreme learning machine for indoor location fingerprinting

With the rapid growing market of wireless devices, positioning systems that make use of the signal strength of wireless devices are gaining more interest nowadays. Being able to track the location of a Wi-Fi or Radio Frequency Identification device could improve the quality of services in various sectors, including security, warehouse, logistic management, and healthcare. As compared with outdoor environment, positioning systems face a greater challenge in indoor environment because wireless signal is significantly influenced by building layout and surrounding objects, for which a location fingerprinting approach is needed. Moreover, the signal strength of a wireless device may also change over time, which is known as temporal variation, and therefore a reliable location estimation system must have the ability to learn and adapt with temporal changes. However, if the learning process is highly complex and requires long processing time, deploying the system into a larger scale would not be feasible. In recent years, Extreme Learning Machine (ELM) has surfaced as a viable alternative that challenged the norm of iterative and progressive learning. ELM has also been considered as a solution for indoor location fingerprinting. However, there has not been a comprehensive review on how the ELM-based approaches are linked with existing location fingerprinting techniques. Here we discuss some major location fingerprinting techniques, which are nearest-neighbor, LANDMARC, and LEMT, and formulate a new framework for systematically translating the techniques into ELM-based methods.

Robust Floor Determination Algorithm for Indoor Wireless Localization Systems under Reference Node Failure

One of the challenging problems for indoor wireless multifloor positioning systems is the presence of reference node (RN) failures, which cause the values of received signal strength (RSS) to be missed during the online positioning phase of the location fingerprinting technique. This leads to performance degradation in terms of floor accuracy, which in turn affects other localization procedures. This paper presents a robust floor determination algorithm called Robust Mean of Sum-RSS (RMoS), which can accurately determine the floor on which mobile objects are located and can work under either the fault-free scenario or the RN-failure scenarios. The proposed fault tolerance floor algorithm is based on the mean of the summation of the strongest RSSs obtained from the IEEE 802.15.4 Wireless Sensor Networks (WSNs) during the online phase. The performance of the proposed algorithm is compared with those of different floor determination algorithms in literature. The experimental results show that the proposed robust floor determination algorithm outperformed the other floor algorithms and can achieve the highest percentage of floor determination accuracy in all scenarios tested. Specifically, the proposed algorithm can achieve greater than 95% correct floor determination under the scenario in which 40% of RNs failed.

A survey of calibration-free indoor positioning systems

Last decade observed a significant research effort directed towards indoor localization utilizing location fingerprinting techniques. Fingerprinting solutions generally require a pre-deployment site survey procedure during which a radio-map is constructed by laboriously collecting signal strength samples (e.g., Wi-Fi) over the whole localization area. However, such localization efforts have certain shortcomings. For example, it is time consuming, labor intensive, vulnerable to environmental changes, and the process requires certain pedigree on the surveyor that may deem the fingerprinting techniques impractical to be deployed over large areas (e.g., shopping malls, multi-storey offices/residences, etc.). Newer emerging techniques try to bypass this expensive pre-deployment effort of fingerprinting solutions altogether. They may build the radio-map through the implicit participation of the building occupants, office employee, shoppers, visitors, etc. Apart from the traditional performance comparison criteria like accuracy, precision, robustness, scalability, algorithmic complexity based on which the localization techniques were evaluated, these newer approaches warrant some additional ones. For example, whether they require an actual geographical map of the localization area, the percentage of occasional location fix to ensure reasonable accuracy, the usage of explicit/implicit user participation to construct the radio map, the usage of building landmarks (e.g., entrance, conference room, elevator, escalator, etc.) or additional sensors (e.g., accelerometer, gyroscope, compass, etc.), whether they address device heterogeneity, etc. In this article, we survey the newer emerging fingerprinting solutions that try to relieve the pre-deployment woes. We also identify some newer performance comparison criteria based on these solutions’ inherent characteristics, and apply them together with the traditional ones in order to evaluate a number of such proposed systems.

Self-mapping radio maps for location fingerprinting

Localisation of a mobile device in Wi-Fi based indoor environments has received much attention and is an important area of research, considering the uptake of devices with built-in Wi-Fi and often blanket coverage in enterprise. Most localisation techniques in this area require an off-line calibration phase where a radio map is manually built using the laborious efforts of administrators. This radio map is then subject to becoming inaccurate with time and requires refreshing as the equipment is upgraded and replaced. We propose a system that can automatically build and calibrate a radio map for use with location fingerprinting techniques to provide indoor and outdoor localisation with Wi-Fi. This system first uses a self-mapping technique that makes use of user roaming patterns to build a weighted undirected graph of Access Point locations. We then combine this initial map with a map built by users of smartphones with built-in Wi-Fi and GPS to generate anchor nodes. We show that the unique combination of these two techniques provides a zero-configuration calibration map for use with location fingerprinting techniques, which not only saves time and effort in the calibration phase, but provides a constantly fine-tuning and self-healing map.

Analysis of WLAN’s received signal strength indication for indoor location fingerprinting

An indoor positioning system that uses a location fingerprinting technique based on the received signal strength of a wireless local area network is an enabler for indoor location-aware computing. Data analysis of the received signal strength indication is very essential for understanding the underlying location-dependent features and patterns of location fingerprints. This knowledge can assist a system designer in accurately modeling a positioning system, improving positioning performance, and efficiently designing such a system. This study investigates extensively through measurements, the features of the received signal strength indication reported by IEEE 802.11b/g wireless network interface cards. The results of the statistical data analysis help in identifying a number of phenomena that affect the precision and accuracy of indoor positioning systems.

Signal Subspace Interpolation from Discrete Measurement Samples in Constructing a Database for Location Fingerprint Technique

In this paper, a method of the signal subspace interpolation to constructing a continuous fingerprint database for radio localization is proposed. When using the fingerprint technique, enhancing the accuracy of location estimation requires very fine spatial resolution of the database, which entails much time in collecting the data to build up the database. Interpolated signal subspace is presented to achieve a fine spatial resolution of the fingerprint database. The angle of arrival (AOA) and the measured signal subspace at known locations are needed to obtain the interpolated signal subspaces. The effectiveness of this method is verified by an outdoor experiment and the estimated location using this method was compared with those using the geometrically calculated fingerprint and the measured signal subspace fingerprint techniques.