Showing papers in "Journal of Location Based Services in 2012"

Molé: a scalable, user-generated WiFi positioning engine

Abstract: We describe the design, implementation, and evaluation of Mole, a mobile organic localisation engine. Unlike previous work on crowd-sourced WiFi positioning, Mole uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Mole aims for a more flexible and scalable point in the design space of localisation systems. Mole employs several new techniques, including a new statistical positioning algorithm to differentiate between neighbouring places, a motion detector to reduce update lag, and a scalable ‘cloud’-based fingerprint distribution system. Mole's localisation algorithm, called Maximum Overlap MAO, accounts for temporal variations in a place's fingerprint in a principled manner. It also allows for aggregation of fingerprints from many users and is compact enough for on-device storage. We show through end-to-end experiments in two deployments that MAO is significantly more accurate than state-of-the-art Bayesian-based localisers. We also show that non-experts can use Mole to quickly survey a building, enabling room-grained location-based services for themselves and others.

Probability kernel regression for WiFi localisation

Abstract: Various methods have been developed for indoor localisation using WLAN signals. Algorithms that fingerprint the received signal strength indicators RSSI of WiFi for different locations can achieve tracking accuracies of the order of a few metres. RSSI fingerprinting suffers from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; second, it has been reported that, in practice, certain devices record more complex e.g bimodal distributions of WiFi signals, precluding algorithms based on the mean RSSI. Mirowski et al . [2011. KL-divergence kernel regression for non-Gaussian fingerprint based localization. In : International conference on indoor positioning and indoor navigation , Guimaraes, Portugal] have recently introduced a simple methodology that takes into account the full distribution for computing similarities among fingerprints using the Kullback–Leibler KL divergence, and then performs localisation through kernel regression. Their algorithm provides a natural way of smoothing over time and motion trajectories and can be applied directly to histograms of WiFi connections to access points, ignoring RSSI distributions, hence removing the need for fingerprint recalibration. It has been shown to outperform nearest neighbours or Kalman and particle filtres, achieving up to 1 m accuracy in office environments. In this article, we focus on the relevance of Gaussian or non-Gaussian distributions for modelling RSSI distributions by considering additional probabilistic kernels for comparing Gaussian distributions and by evaluating them on three contrasting datasets. We discuss their limitations and formulate how the KL-divergence kernel regression algorithm bridges the gap with other WiFi localisation algorithms, notably Bayesian networks, support vector machines and K nearest neighbours. Finally, we revisit the assumptions on the fingerprint maps and overview practical WiFi localisation software implementation.

Improved heuristic drift elimination with magnetically-aided dominant directions MiHDE for pedestrian navigation in complex buildings

Abstract: The main problem of pedestrian dead-reckoning PDR using only a body-attached inertial measurement unit is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable and therefore it is commonly not used. Recently, a new method was proposed called heuristic drift elimination HDE that minimises the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of the four possible directions. In this article we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings, and also, how severe errors can appear in the case of false matches with the building's dominant directions. Although magnetic compassing indoors has a chaotic behaviour, in this article we analyse large data-sets in order to study the potential use that magnetic compassing has to estimate the absolute yaw angle of a walking person. Apart from these analysis, this article also proposes an improved HDE method called Magnetically-aided Improved Heuristic Drift Elimination MiHDE, that is implemented over a PDR framework that uses foot-mounted inertial navigation with an extended Kalman filter EKF. The EKF is fed with the MiHDE-estimated orientation error, gyro bias corrections, as well as the confidence over that corrections. We experimentally evaluated the performance of the proposed MiHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and MiHDE outperforms HDE for non-ideal trajectories e.g. curved paths and also makes it robust against potential false dominant direction matchings.

Comparison of WiFi positioning on two mobile devices

Abstract: 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...

Fault detection and mitigation in WLAN RSS fingerprint-based positioning

Abstract: Fingerprint-based positioning methods that utilise WLAN received signal strength RSS measurements are becoming increasingly popular for inferring terminal location in indoor environments. They are very attractive mainly because of the low deployment cost, as they exploit the ubiquity of WLAN access points AP and the worldwide availability of mobile devices featuring WLAN adapters for wireless connectivity. They provide reliable and accurate location estimates, however their performance can be compromised when faults occur in the overall positioning system. Such faults are injected in different ways, including the removal or relocation of one or several APs, and are caused either accidentally or deliberately due to malicious attacks. These faults corrupt the RSS fingerprints and may lead to accuracy degradation, unless countermeasures are taken. In this work, we focus on positioning under faults and construct detection schemes using fault indicators that are applicable to standard fingerprint-based methods and achieve high fault detection rates. In addition, we incorporate our detection mechanisms into hybrid positioning algorithms that greatly improve fault tolerance in the presence of different types of faults. Finally, we present experimental results to verify the effectiveness of the proposed approach.

Magnetic perturbations detection and heading estimation using magnetometers

Abstract: With the advent of smart phones equipped with Micro-Electro-Mechanical Systems, it has become possible to utilise one's orientation information along with location for Location-Based Services applications. Although gyroscopes are considered to be the primary sensors for orientation estimation, associated errors require periodic updates from other sources to mitigate them. Accelerometers can be used for estimating roll and pitch angles and magnetometers for estimating heading. However, the presence of man-made magnetic anomalies caused by electronic devices, ferrous materials, mechanical and electrical infrastructures contaminates the measurements since only the Earth's magnetic field is useful for estimating magnetically derived heading. Without proper magnetic disturbances detection and mitigation technique, heading estimates become erroneous and can corrupt the entire location estimation process. Because heading is the primary source of error in pedestrian navigation and the magnetic field is available everywhere, an exhaustive analysis of magnetic anomalies and their impact on heading estimation has been conducted using data collected in six different pedestrian environments. Based on the statistical outcomes, a magnitude-and angle-based detector of magnetic disturbances has been developed. This information is also used for estimating the accuracy of the magnetically derived heading, which can further assist sensor fusion algorithms. An experimental assessment of the proposed detector was conducted in a shopping mall. A theoretical analysis and experimental results show that the proposed detector is capable of identifying the effects of perturbations on the Earth's magnetic field, which provides users with a better estimate of magnetic heading in different pedestrian navigation environments.

PLBSD: a platform for proactive location-based service discovery

Abstract: We introduce a platform for the rapid prototyping of proactive location-based service discovery, proactive location-based services are conceptualised along three broad categories: location-triggered services chain-triggered services , proximity-triggered services , and illustrated through a number of usage scenarios We report on a workshop with designers and researchers in the area of location-based services that resulted in a set of initial requirements for the platform We describe how the platform aims at addressing these requirements, and illustrate the implemented features through the development of a proactive location-based application

Collective intelligence-based route recommendation for assisting pedestrian wayfinding in the era of Web 2.0

Abstract: Mobile pedestrian navigation systems are one of the most popular Location-Based Services. In the era of Web 2.0, current mobile navigation systems often suffer from the following problems: the lack of social navigation support (utilizing other people's experiences) and the challenge of making user-generated content (UGC) useful. In this article, some collective intelligence-based route recommendation methods are designed to address these problems. The proposed methods can make use of UGC (reflecting other users' navigation experiences), and provide users with the least complex and the length-complexity-optimised (LCO) routes. Some simulations using the street network of the first district of Vienna (Austria) are designed to evaluate the proposed methods. The results show that compared to the shortest route, the collective intelligence-based routes (i.e. the least complex and the LCO routes) have a significant improvement of the route quality (with lower complexity ratings), thereby more effectively supporting users' navigation tasks (more chances of reaching the destination, fewer mistakes made and shorter distance traveled).

Acceptance of real-time location-based advertising service: a conceptual examination

Abstract: The rapid growth of mobile phone value-added services has increasingly attracted marketing attention. The real-time location-based advertising service RTLBAS being developed by VIETMAP, a leading mapping company in Vietnam, employs positioning technology to locate a user and provide advertising information. An empirical case study in Ho Chi Minh City, Vietnam, was conducted to examine the acceptance of RTLBAS. Data were collected by 12 VIETMAP salespersons who conducted a questionnaire-based interview of the subjects. The sample consisted of 315 respondents. Data analysis revealed three important findings. First, perceived compatibility is the main concern, and will make mobile phone users more likely to use RTLBAS. Customers become increasingly function-sensitive and function-compatible as the number of service types increases. Second, mobile phone users are uncertain about adopting VIETMAP's RTLBAS, preferring to try it first even if they think it is useful. Finally, a privacy policy should be developed as a concern of the social relevance of RTLBAS development. Managerial implications were also addressed.

Performance and limits of KNN-based positioning methods for GSM networks over leaky feeder in underground tunnels

Abstract: Localisation techniques have long been of major importance for safety systems and a lot of research has been conducted in the distributed computing field regarding its functionality and reliability. In the specific scenario of long yet narrow tunnels existing at CERN, localisation methods will enable a number of applications and processes to substantially reduce human intervention. In this article, we evaluate the use of fingerprinting techniques with GSM signal available throughout the LHC tunnel via a radiating cable and compare some methods to estimate the location. In the tests, 16 variants of the K-Nearest Neighbour algorithm, employing different distance weighting methods and fingerprint grouping functions, are taken into consideration and their performance is assessed with a specific rating algorithm. The existing GSM infrastructure and tunnel conditions seem to be favourable to the adoption of these fingerprinting methods. Nevertheless, significant variations in the signal have been observed which might be traced back to the presence of bulky equipment and different operational states of the accelerator. The performance limits of these fingerprinting methods are discussed for the current scenario and, based on that, an outlook for future research is given aiming at improving the system's accuracy under such challenging conditions.

O brother, where art thou located?: raising awareness of variability in location tracking for users of location-based pervasive applications

Abstract: There is a diversity of ways to determine a user's location in a pervasive environment today. On a large scale, this diversity often results in variability of location tracking conditions throughout the environment. For an important class of pervasive applications, which often rely on the ubiquitous availability of location tracking – location-based pervasive applications, the consistency of their behaviour under this variability cannot be guaranteed. This type of limitation raises a need for the adaptation of the application's behaviour that would reflect this variability. We investigated empirically how visualising different aspects of uncertainty about location and of the behaviour of localisation systems affects users’ impressions about a location-based application. The two components – an ontology that models properties of localisation systems and a set of mapping rules that define how these properties should be visualised in a user interface – are at the core of our approach to providing awareness. The results of the investigation show that the additional visual demand, intended for raising users’ awareness of uncertainty about their location tracking conditions, is perceived to be beneficial by users. We also reveal that different characteristics of this awareness are of different importance to users. Furthermore, we conclude that the particular importance depends on users’ personal profiles e.g. their eyesight level, on the distance between the users e.g. knowing about someone else's state is less important if they are far and on the quality of tracking the importance increases in problematic areas. On the basis of the obtained results and observations, we suggest a set of guidelines and visualisations, which could be used in designing pervasive applications that require location tracking.

An empirical investigation of the accuracy of location-based services in South Africa

Abstract: This article describes an empirical investigation of factors influencing the accuracy of location-based services (LBS) on GSM phones in South Africa The experimental settings were three distinct environments in Johannesburg, South Africa The results indicated that some parameters such as geographical location and environment of an LBS request have a significantly high impact on the accuracy of LBS In addition, there was also a huge difference between the predicted and provided accuracies by mobile location providers This article concludes with a discussion of solutions on bridging the gap between the predicted and provided accuracies to LBS users with the view of improving LBS user dependability on the service provided

Cortina: collaborative indoor positioning using low-power sensor networks

Abstract: Cortina is a distributed real-time location system designed to track assets or people moving indoors. Our solution leverages a low-cost, low-power wireless sensor network based on the IEEE 802.15.4 radio standard. The network, which consists of wall-plugged nodes, is designed to be self-configuring, self-healing and self-calibrating, thus reducing deployment and maintenance costs. Assets and people are tracked using small battery operated wireless tags that collect received signal strength measurements from nearby nodes. The tags also include an accelerometer for activity recognition, and a barometric pressure sensor to detect the floor plan. We have conducted experiments over a 2000 m2 area instrumented with 18 sensor nodes. Our initial results show that the system can track people in real-time with an average error of 2.8 m.