Crowdsourcing radio signal fingerprints to build a radio map for indoor positioning system is an emerging alternative to conventional labour-costly manual survey. However, existing crowdsourced systems heavily rely on ground-truth location inputs or unrealistic constraints on the contributors, deterring a wider adaption of crowdsourced systems. Our work exploits three generic constraints of mobile data to retrieve the locations of the crowdsourced fingerprints and builds a completely passive indoor positioning system that assumes no manual intervention or unnatural constraints on the contributors. The proposed system was further evaluated in the Museum of Archaeology and Anthropology (MAA) with passively crowd- sourced data contributed by actual visitors while visitors can behave naturally without catering to crowdsourcing. Results show that the proposed system can achieve positioning accuracy comparable to traditional manual survey-based system with essentially no extra manual effort.

Human gait is a widely used biometric trait for user identification and recognition. Given the wide-spreading, steady diffusion of ear-worn wearables (Earables) as the new frontier of wearable devices, we investigate the feasibility of earable-based gait identification. Specifically, we look at gait-based identification from the sounds induced by walking and propagated through the musculoskeletal system in the body. Our system, EarGate, leverages an in-ear facing microphone which exploits the earable's occlusion effect to reliably detect the user's gait from inside the ear canal, without impairing the general usage of earphones. With data collected from 31 subjects, we show that EarGate achieves up to 97.26% Balanced Accuracy (BAC) with very low False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 3.23% and 2.25%, respectively. Further, our measurement of power consumption and latency investigates how this gait identification model could live both as a stand-alone or cloud-coupled earable system.

Earables (in-ear wearables) are a new frontier in wearables. Acting both as leisure devices, providing personal audio, as well as sensing platforms, earables could collect sensor data for the upper part of the body, subject to fewer vibrations and random movement variations than the lower parts of the body, due to inherent damping in the musculoskeletal system. These data may enable application domains such as augmented/virtual reality, medical rehabilitation, and health condition screening. Unfortunately, earables have inherent size, shape, and weight constraints limiting the type and position of the sensors on such platforms. For instance, lacking a magnetometer in all earables reference platforms, earables lack reference points. Thus, it becomes harder to work with absolute orientations. Embedding magnetometers in earables is challenging, as these rely heavily on radio (mostly Bluetooth) communication (RF) and contain magnets for magnetic-driven speakers and docking. We explore the feasibility of adding a built-in magnetometer in an earbud, presenting the first comprehensive study of the magnetic interference impacting the magnetometer when placed in an earable: both that caused by the speaker and by RF (music streaming and voice calls) are considered. We find that appropriate calibration of the magnetometer removes the offsets induced by the magnets, the speaker, and the variable interference due to BT. Further, we present an automatic, user-transparent adaptive calibration that obviates the need for alternative, expensive, and error-prone manual, or robotics, calibration procedures. Our evaluation shows how our calibration approach performs under different conditions, achieving convincing results with errors below 3° for the majority of the experiments.

Signal fingerprinting is considered to be potential as the general indoor positioning solution since it does not require extra infrastructure or hardware modification to current customer smart devices. However, due to the complex nature of radio signal propagation, fingerprints are highly susceptible to both temporal and spatial indoor dynamics, rendering the positioning outcomes unreliable. Most recent works dedicated to efficient ways of building radio maps during the offline phase yet overlooked the localisability of fingerprints collected rather opportunistically at the online phase. In this paper, we introduce the use of pseudo-measurements and propose a fingerprint anomaly detection method that effectively evaluates the localisability of fingerprints while positioning. Empirical evaluations demonstrate that filtering out untrustworthy fingerprints can significantly improve the positioning accuracy.

Proximity beacons provide simple, low-cost location data. However, beacon deployments remain rare. In this paper we introduce Bellrock, a framework that repurposes static personal devices (phones, laptops, etc.) as proximity beacons without revealing the location of the device owners, and provides conventional beacons with access control. This is done by using mutable pseudo-anonymous identifiers that can be unmasked by a cloud service.We develop Bellrock as a general framework, describing the repurposing scheme and the anonymisation techniques, before applying it to Bluetooth Low Energy beacons. We implement and demonstrate the scalability of the de-anonymisation server, which uses a series of heuristics. We implement a Bellrock client on Android and demonstrate negligible impact on battery lifetime. We evaluate Bellrock using extensive real-world office worker movements. We find that Bellrock was able to provide proximity locations for 8,542 of the 21,796 failed locations that would have occurred without it. We further find that office workers were in range of one or more of their co-workers over 90% of the time, indicating Bellrock can provide relative proximity information even in the absence of a conventional beacon deployment. Overall, we find that Bellrock is both feasible and practical, providing a beacon deployment where there was none, or supplementing existing deployments.

Location fingerprinting locates devices based on pattern matching signal observations to a pre-defined signal map. This paper introduces a technique to enable fast signal map creation given a dedicated surveyor with a smartphone and floorplan. Our technique (PFSurvey) uses accelerometer, gyroscope, and magnetometer data to estimate the surveyor's trajectory post-hoc using Simultaneous Localization and Mapping and particle filtering to incorporate a building floorplan. We demonstrate conventional methods can fail to recover the survey path robustly and determine the room unambiguously. To counter this, we use a novel loop closure detection method based on magnetic field signals and propose to incorporate the magnetic loop closures and straight-line constraints into the filtering process to ensure robust trajectory recovery. We show this allows room ambiguities to be resolved. An entire building can be surveyed by the proposed system in minutes rather than days. We evaluate in a large office space and compare to state-of-the-art approaches. We achieve trajectories within 1.1 m of the ground truth 90 percent of the time. Output signal maps well approximate those built from conventional, laborious manual survey. We also demonstrate that the signal maps built by PFSurvey provide similar or even better positioning performance than the manual signal maps.

Simultaneous Localisation and Mapping (SLAM) systems that recover the trajectory of a robot or mobile device are characterised by a front-end and back-end. The front-end uses sensor observations to identify loop closures; the back-end optimises the estimated trajectory to be consistent with these closures. The GraphSLAM framework formulates the back-end problem as a graph-based optimisation on a pose graph. This paper describes a back-end system optimised for very dense sequence-based loop closures. This arises when the frontend generates magnetic loop closures, among other things. Magnetic measurements are fast varying, which is good for localisation, but the requirement for high sampling rates (50 Hz+) produces many more loop closures than conventional systems. To date, however, there is no study optimising GraphSLAM back-end for sequence-based magnetic loop closures. Hence we introduce a novel variant of the Stochastic Gradient Descent-based SLAM algorithm called MSGD (Magnetic-SGD). We use high-accuracy groundtruth system and extensive real datasets to evaluate MSGD against state-of-the-art back-end algorithms. We demonstrate MSGD is at least as good as the best competitor algorithm in terms of quality, while being faster and more scalable.

Bluetooth Low Energy (BLE) beacons are attractive for indoor location due to their ease of deployment, wide support on consumer devices and low cost. Co-ordinate location can be estimated using radio fingerprinting techniques applied to Received Signal Strength (RSS) values from BLE beacons. Earlier work on BLE fingerprinting has noted that the separate BLE advertising channels can exhibit different RSS values, although modern consumer devices do not provide channel information and therefore report a composite RSS with artificially inflated variance that can lead to reduced positioning accuracy. In this work we quantify how often this channel dispersion occurs in typical environments, what the extent of the dispersion is, and what impact it has on positioning. Furthermore we explore how to obtain channel information when using today's consumer devices. We find that the dispersion is both common and significant and leads to significantly reduced positioning accuracy; in our experiments only 10% of composite variances were within 1 dBm of an individual channel's variance, and 40% of composite variances were at least 4 dBm greater. We also show that the behaviour of the channels is sufficiently distinct that a fingerprinting scheme that uses a signal map for each achieves significantly increased positioning accuracy (up to 3 m).

Indoor positioning through signal fingerprinting is a popular choice since it requires little or no additional infrastructure. However, the initial creation and subsequent maintenance of the signal maps remains a challenge since traditional manual surveying is not scalable. In this work we look at the use of path surveys, which attempt to construct the signal maps from a sparse set of fingerprints collected while a person walks through a space. As such, the survey points rarely provide a uniform coverage of the space of interest. We quantitatively evaluate path surveys with reference to a detailed manual survey using smartphone-grade equipment. We compare both the individual maps (generated using Gaussian Process regression) and their collective positioning performance. Our results are for both WiFi and Bluetooth Low Energy signals. We show that a path survey can provide maps of equivalent quality to a manual survey if a series of guidelines we provide are followed.

Ubiquitous indoor positioning remains a challenge for academia and industry alike. Techniques based on signal surveys (fingerprint maps) are arguably the most prevalent today, but these rely on time-consuming, complex manual surveys of the signal propagation. Recently progress has been made in semi-automated surveying, where Pedestrian Dead Reckoning (PDR) algorithms are combined with signal measurements using Simultaneous Localisation and Mapping (SLAM) techniques first developed in robotics.

The complexity of indoor radio propagation has resulted in location-awareness being derived from empirical fingerprinting techniques, where positioning is performed via a previously-constructed radio map, usually of WiFi signals. The recent introduction of the Bluetooth Low Energy (BLE) radio protocol provides new opportunities for indoor location. It supports portable battery-powered beacons that can be easily distributed at low cost, giving it distinct advantages over WiFi. However, its differing use of the radio band brings new challenges too. In this work, we provide a detailed study of BLE fingerprinting using 19 beacons distributed around a ~600 m2testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading, show how to mitigate this, and quantify the true power cost of continuous BLE scanning. We further investigate the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency. We also provide quantitative comparison with WiFi fingerprinting. Our results show advantages to the use of BLE beacons for positioning. For one-shot (push-to-fix) positioning we achieve2), compared to2) and <; 8.5 m for an established WiFi network in the same area.

Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use.

Location-aware computing is a fast emerging area in mobile computing. A plethora of approaches to indoor localisation have been demonstrated, but almost all rely on extensive infrastructure. A popular alternative is to use dead reckoning to track inertial sensors. However, sensor drift must be addressed by incorporating external constraints such as the building layout. This dictates the use of computationally expensive particle filters that hinder scalability, especially during localisation phases where the system does not have any estimate of where the user is within a building. In this paper, we address the scalability problem by exploiting the latent parallelism in the algorithm and adapting it for execution on commodity Graphical Processing Units (GPUs). We describe how to parallelise the particle filter and evaluate different filter architectures. We find that our GPU implementation can iterate 8.8 times faster than the fastest CPU variant. We also show how to handle multiple filters using a novel memory paging scheme and an adaptable particle number. We find that between 17 and 101 users can be localised in real-time using only a mid-range GPU installed in a standard desktop machine, compared with at most one using a previous sequential approach.

This paper explores the use of location-awareness to dynamically optimise the energy consumption of an office. It makes use of high-accuracy location data collected over 60 days randomly selected from a year in a commercial environment to evaluate the potential for energy savings and to motivate techniques that might be used.

Ubiquitous computing systems aim to assist us as we go about our daily lives, whilst at the same time fading into the background so that we do not notice their presence. To do this they need to be able to sense their surroundings and infer context about the state of the world. Location has proven to be an important source of contextual information for such systems. If a device can determine its own location then it can infer its surroundings and adapt accordingly. Of particular interest for many ubiquitous computing systems is the ability to track people in indoor environments. This interest has led to the development of many indoor location systems based on a range of technologies including infra-red light, ultrasound and radio. Unfortunately existing systems that achieve the kind of sub-metre accuracies desired by many location-aware applications require large amounts of infrastructure to be installed into the environment. This thesis investigates an alternative approach to indoor pedestrian tracking that uses on-body inertial sensors rather than relying on fixed infrastructure. It is demonstrated that general purpose inertial navigation algorithms are unsuitable for pedestrian tracking due to the rapid accumulation of errors in the tracked position. In practice it is necessary to frequently correct such algorithms using additional measurements or constraints. An extended Kalman filter is developed for this purpose and is applied to track pedestrians using foot-mounted inertial sensors. By detecting when the foot is stationary and applying zero velocity corrections a pedestrian’s relative movements can be tracked far more accurately than is possible using uncorrected inertial navigation. Having developed an effective means of calculating a pedestrian’s relative movements, a localisation filter is developed that combines relative movement measurements with environmental constraints derived from a map of the environment. By enforcing constraints such as impassable walls and floors the filter is able to narrow down the absolute position of a pedestrian as they move through an indoor environment. Once the user’s position has been uniquely determined the same filter is demonstrated to track the user’s absolute position to sub-metre accuracy. The localisation filter in its simplest form is computationally expensive. Furthermore symmetry exhibited by the environment may delay or prevent the filter from determining the user’s position. The final part of this thesis describes the concept of assisted localisation, in which additional measurements are used to solve both of these problems. The use of sparsely deployed WiFi access points is discussed in detail. The thesis concludes that inertial sensors can be used to track pedestrians in indoor environments. Such an approach is suited to cases in which it is impossible or impractical to install large amounts of fixed infrastructure into the environment in advance.

We describe a method for using Nintendo Wii controllers as a stereo vision system to perform 3D tracking or motion capture in real time. Commodity consumer hardware allows a wireless, portable tracker to be created that obtains accurate results for a fraction of the cost of conventional setups. Consequently, tracking becomes viable in situations where cost or space were previously prohibitive. Initial results show an accuracy of plusmn2 mm over a large tracking volume.

As location systems provide increasingly fine-grained locations for mobile entities, location-aware systems that react appropriately and autonomously to location events will be in demand. Although much research has been devoted to indexing schemes for very large scale GIS applications (where the systems are predominantly query-based), comparably little attention has been given to the use of spatial indexing within location-aware systems leveraging local positioning systems (predominantly event-based). This paper reviews the notion of spatial indexing (the representation of a tracked user's space to facilitate spatial event generation based on incoming locations) for event-based systems. It establishes the principles and requirements of a wide-area spatial indexer, reviews the R-tree, Quadtree and RQ-tree methods proposed before for indoor location-awareness, and significantly adapts the latter to meet the requirements. The proposed indexing method uses a combination of R-trees (for high level spatial information, down to structural level) and Quadtrees (for lower level representation of objects). It proposes the use of linear Quadtrees to exploit the ease of direct node movement rather than the re-rasterisation of polygons used in systems to date. This approach is compared in simulation with other approaches.

A key component of intelligent transportation is the provision of adequate network infrastructure to support vehicle-to-vehicle and vehicle-to-roadside communication. In this paper we report on performance evaluations carried out using the IEEE 802.11a protocol at 5.2 GHz between a moving vehicle and a fixed base station. We concentrate our evaluation on realistic urban speeds and environments, observing that performance at very low speeds is degraded due to the presence of null zones. We vary the modulation scheme and analyse the spread of resulting throughputs. Our results have implications for multimedia and other real-time applications that will utilise vehicle-to-roadside connectivity.

Pervasive computing research has highlighted a need for location systems and input devices that are easy to deploy ubiquitously, are inexpensive, reliable, and practically invisible. This paper describes a novel tracking platform that attempts to meet all of these goals. The system exploits the static charge that naturally accumulates with movement through typical environments to create an inexpensive system that has potential both as a passive location system and as an input device. We demonstrate the physical principles, develop hardware and software to reliably detect interactions, and extensively evaluate a prototype implementation. We find the prototype to have a low false negative rate and a zero false positive rate. The power draw lies in the milliwatt-range and the cost per room is significantly lower than comparable systems

This paper presents Cantag, an open source software toolkit for building marker-based vision (MBV) systems that can identify and accurately locate printed markers in three dimensions. The extensibility of the system makes it ideal for dynamic location and poses determination in pervasive computing systems. Unlike prior MBV systems, Cantag supports multiple fiducial shapes, payload types, data sizes and image processing algorithms in one framework. It allows the application writer to generate a custom tag design and associated optimised executable for any given application. The system includes a test harness which can be used to quantify, compare and contrast the performance of different designs. This paper explores the design space of tags within the Cantag system, and describes the design parameters and performance characteristics which an application writer can use to select the best tag system for any given scenario. It presents quantitative analysis of different markers and processing algorithms, which are compared fairly for the first time

Location-aware systems are typically deployed on a small scale and evaluated technically, in terms of absolute errors. In this paper, the authors present their experience of deploying an indoor location system (the Bat system) over a larger area and running it for a period exceeding two years.A number of technical considerations are highlighted: a need to consider aesthetics throughout deployment, the disadvantages of specialising sensors for location only, the need for autonomous maintenance of the computational world model, the dangers in coinciding physical and symbolic boundaries, the need to design for space usage rather than space and the need to incorporate feed-back mechanisms and power management. An evaluation of long term user experiences is presented, derived from a survey, logged usage data, and empirical observations. Statistically, it is found that 35% wear their Bat daily, 35% characterise their Bat as useful, privacy concerns are rare for almost 90% of users, and users cite the introduction of more applications and the adoption of the system by other users as their chief incentives to be tracked.Thia paper aims to highlight the need to evaluate large-scale deployments of such systems both technically and through user studies.

Context-aware computing systems demand an accurate and up-to-date world model which computationally represents the environment they oversee. Systems to date tend to have small-scale implementations with hand-programmed world models. In real environments, manual creation and maintenance of such models is infeasible. This work presents a method of using signals propagating in a multilateration positioning system to assist in creating and maintaining models of a dynamic world. It builds on a previous method for discovering objects in static environments. The methods are implemented and evaluated using a real positioning system. They are shown to build three-dimensional occupancy grids of indoor volumes, and have the capability of modifying those grids as time proceeds and the environment is reconfigured.

Article Share on Single Reflection Spatial Voting: A Novel Method for Discovering Reflective Surfaces Using Indoor Positioning Systems Authors: R. K. Harle View Profile , A. Ward View Profile , A. Hopper View Profile Authors Info & Claims MobiSys '03: Proceedings of the 1st international conference on Mobile systems, applications and servicesMay 2003 Pages 1–14https://doi.org/10.1145/1066116.1189035Published:05 May 2003Publication History 12citation168DownloadsMetricsTotal Citations12Total Downloads168Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We present a novel method of extracting topological and metric geographical data using only positional data sensed from personnel movements. We extend research from the field of robotics to cope with the gross nonuniformity of sightings that is characteristic of real people in an indoor environment, and any unintentional obstruction of positioning by the user. We use real data collected using the Bat positioning system installed in the Laboratory for Communication Engineering to present the results of implementing the method. We successfully derive useful information from the data, and suggest further ways in which the techniques described are useful in a ubiquitous, sensor-driven computing environment.