Wi-Fi Positioning Systems Research Articles

Wasserstein Metric-Based Location Spoofing Attack Detection in WiFi Positioning Systems

WiFi positioning systems (WPS) have been introduced as parts of 5G location services (LCS) to provide fast positioning results of user devices in urban areas. However, they are prominently threatened by location spoofing attacks. To end this, we present a Wasserstein metric-based attack detection scheme to counter the location spoofing attacks in the WPS. The Wasserstein metric is used to measure the similarity of each two hotspots by their signal’s frequency offset distribution features. Then, we apply the clustering method to find the fake hotspots which are generated by the same device. When applied with WPS, the proposed method can prevent location spoofing by filtering out the fake hotspots set by attackers. We set up experimental tests by commercial WiFi devices, which show that our method can detect fake devices with 99% accuracy. Finally, the real-world test shows our method can effectively secure the positioning results against location spoofing attacks.

Analyzing large-scale human mobility data: a survey of machine learning methods and applications

Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis.

Deciphering Egyptian Hieroglyphs: Towards a New Strategy for Navigation in Museums.

This work presents a novel strategy to decipher fragments of Egyptian cartouches identifying the hieroglyphs of which they are composed. A cartouche is a drawing, usually inside an oval, that encloses a group of hieroglyphs representing the name of a monarch. Aiming to identify these drawings, the proposed method is based on several techniques frequently used in computer vision and consists of three main stages: first, a picture of the cartouche is taken as input and its contour is localized. In the second stage, each hieroglyph is individually extracted and identified. Finally, the cartouche is interpreted: the sequence of the hieroglyphs is established according to a previously generated benchmark. This sequence corresponds to the name of the king. Although this method was initially conceived to deal with both high and low relief writing in stone, it can be also applied to painted hieroglyphs. This approach is not affected by variable lighting conditions, or the intensity and the completeness of the objects. This proposal has been tested on images obtained from the Abydos King List and other Egyptian monuments and archaeological excavations. The promising results give new possibilities to recognize hieroglyphs, opening a new way to decipher longer texts and inscriptions, being particularly useful in museums and Egyptian environments. Additionally, devices used for acquiring visual information from cartouches (i.e., smartphones), can be part of a navigation system for museums where users are located in indoor environments by means of the combination of WiFi Positioning Systems (WPS) and depth cameras, as unveiled at the end of the document.

Evaluation of Two WiFi Positioning Systems Based on Autonomous Crowdsourcing of Handheld Devices for Indoor Navigation

Current WiFi positioning systems (WPSs) require databases – such as locations of WiFi access points and propagation parameters, or a radio map – to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of automatically generated databases, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors’ solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.

Real-Time Precise IOT Positioning System Based on Chirp Spread Spectrum Technology for Transportation

Real-time precise IOT positioning system based on CSS uses the way of bus or Ethernet with wireless CSS network. With its unique CSS technology to measure the distance, the console can achieve real-time communication and position monitoring of personnel and goods. The system has better comprehensive performance than active RFID, Zigbee and WiFi positioning systems and other technology on the market currently. The functional independence of its communication system makes it easy for the practical application and extensions, and the system has wide application value.

Selective RF Fingerprint Scanning for Large-Scale Wi-Fi Positioning Systems

Rapid growth in mobile communication and the proliferation of smartphones have drawn significant attention to location-based services (LBSs). One of the most important factors in the vitalization of LBSs is the accurate position estimation of a mobile device. The traditional global positioning system method has a critical weakness in terms of its limited availability, e.g., in indoor environments. The Wi-Fi positioning system is an attractive method that measures received signal strength indication data from all Wi-Fi access points (APs) and stores them in a huge database in the form of a radio frequency (RF) fingerprint map. Because of the millions of APs in urban areas, RF fingerprints are easily contaminated and transfigured. For this reason, a frequent rescanning of fingerprints is essential for fingerprint data integrity. However, rescanning fingerprints for an entire area requires a considerable amount of cost and effort. Therefore, we present a selective fingerprint rescan method for efficient fingerprint map management. All fingerprint data used in the developed test-bed are harvested from actual radio fingerprint measurements taken throughout Seoul, Korea, to demonstrate the practical usefulness of the proposed methodology.

A coherent data filtering method for large scale RF fingerprint Wi-Fi Positioning Systems

The rapid growth of mobile communication and the proliferation of smart phones have drawn significant attention to location-based services (LBS). The Wi-Fi positioning system (WPS) is a newly attractive method as a widely applicable positioning technique in LBS. In WPS, the received signal strength indication (RSSI) data of all Wi-Fi access points (APs) are measured, and stored in a huge database, as a form of radio fingerprint map. Because of the millions of APs in urban areas, radio fingerprint data are seriously contaminated. Therefore, we present a coherent filtering method for radio fingerprint data. All fingerprints used in the developed test bed are harvested from actual radio fingerprint measurements taken throughout Seoul, Korea. This demonstrates the practical usefulness of the proposed methodology.

Enhanced Weighted K-Nearest Neighbor Algorithm for Indoor Wi-Fi Positioning Systems

Location-based systems for indoor positioning have been studied widely owing to their application in various fields. The fingerprinting approach is often used in Wi-Fi positioning systems. The K-nearest-neighbor fingerprinting algorithm uses a fixed number of neighbors, which reduces positioning accuracy. Here, we propose a novel fingerprinting algorithm, the enhanced weighted K-nearest neighbor (EWKNN) algorithm, which improves accuracy by changing the number of considered neighbors. Experimental results show that the proposed algorithm gives higher accuracy.

Comparison of WiFi positioning on two mobile devices

Metropolitan WiFi positioning is widely used to complement GPS on mobile devices. WiFi positioning typically has very fast time-to-first-fix and can provide reliable location information when GPS signals are too weak for a position fix. Several commercial WiFi positioning systems have been developed in recent years and most newer model smart phones have the technology embedded. This study empirically determined the performance of WiFi positioning system on two different mobile devices. Skyhook's system, running on an iPhone and a laptop, was selected for this study. Field work was carried out in three cities at a total of 90 sites. The positional accuracy of WiFi positioning was found to be very similar on the two devices with no statistically significant difference between the two error distributions. This suggests that the replicability of WiFi positioning on different devices is high based on aggregate performance metrics. Median values for positional accuracy in the three study areas ranged from 43 to 92 m. These results are similar to earlier independent evaluations of Skyhook's system. The number of access points (APs) observed on the iPhone was consistently lower than that on the laptop. This lower number of APs, however, was not found to reduce positional accuracy. In general, no relationship was found between the number of APs and positional accuracy for either device, counter to earlier findings. The results indicate, however, that WiFi positioning can be achieved with a small number of APs (5–10), but that increased numbers of APs do not contribute to improved positional accuracy. Despite the agreement in aggregate performance metrics between the two devices, the replicability of WiFi positioning using Skyhook's system in terms of getting the same location by using two different devices at approximately the same place and time was relatively poor. Implications for location-based services on mobile devices are discussed.

Location determination using WiFi fingerprinting versus WiFi trilateration

Many applications in the area of location-based services and personal navigation require nowadays the location determination of a user not only in an outdoor environment but also an indoor. Typical applications of location-based services (LBS) mainly in outdoor environments are fleet management, travel aids, location identification, emergency services and vehicle navigation. LBS applications can be further extended if reliable and reasonably accurate three-dimensional positional information of a mobile device can be determined seamlessly in both indoor and outdoor environments. Current geolocation methods for LBS may be classified as GNSS-based, cellular network-based or their combinations. GNSS-based methods rely very much on the satellite visibility and the receiver-satellite geometry. This can be very problematic in dense high-rise urban environments and when transferring to an indoor environment. Especially, in cities with many high-rise buildings, the urban canyon will greatly affect the reception of the GNSS signals. Moreover, positioning in the indoor/outdoor transition areas would experience signal quality and signal reception problems, if GNSS systems alone are employed. The authors have proposed the integration of GNSS with wireless positioning techniques such as WiFi and UWB. In the case of WiFi positioning, the so-called fingerprinting method based on WiFi signal strength observations is usually employed. In this article, the underlying technology is briefly reviewed, followed by an investigation of two WiFi-positioning systems. Testing of the system is performed in two localisation test beds, one at the Vienna University of Technology and another one at the Hong Kong Polytechnic University. The first test showed that the trajectory of a moving user could be obtained with a standard deviation of about ±3–5 m. The main disadvantage of WiFi fingerprinting, however, is the required time consuming and costly signal strength system calibration in the beginning. Therefore, the authors have investigated if the measured signal strength values can be converted to the corresponding range to the access point. A new approach for this conversion is presented and analysed in typical test scenarios.