Thomas Toftkjær

EnTracked: energy-efficient robust position tracking for mobile devices

An important feature of a modern mobile device is that it can position itself. Not only for use on the device but also for remote applications that require tracking of the device. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, tracking has to robustly deliver position updates when faced with changing conditions such as delays due to positioning and communication, and changing positioning accuracy. This work proposes EnTracked - a system that, based on the estimation and prediction of system conditions and mobility, schedules position updates to both minimize energy consumption and optimize robustness. The realized system tracks pedestrian targets equipped with GPS-enabled devices. The system is configurable to realize different trade-offs between energy consumption and robustness. We provide extensive experimental results by profiling how devices consume power, by emulation on collected data and by validation in several real-world deployments. Results from this profiling show how a device consumes power while tracking its position. Results from the emulation indicate that the system can estimate and predict system conditions and mobility. Furthermore they provide evidence for that the system can lower the energy consumption considerably and remain robust when faced with changing system conditions. By validation in several real-world deployments we provide evidence that the real system works as predicted by the emulation.

Indoor positioning using GPS revisited

It has been considered a fact that GPS performs too poorly inside buildings to provide usable indoor positioning. We analyze results of a measurement campaign to improve on the understanding of indoor GPS reception characteristics. The results show that using state-of-the-art receivers GPS availability is good in many buildings with standard material walls and roofs. The measured root mean squared 2D positioning error was below five meters in wooden buildings and below ten meters in most of the investigated brick and concrete buildings. Lower accuracies, where observed, can be linked to either low signal-to-noise ratios, multipath phenomena or bad satellite constellation geometry. We have also measured the indoor performance of embedded GPS receivers in mobile phones which provided lower availability and accuracy than state-of-the-art ones. Finally, we consider how the GPS performance within a given building is dependent on local properties like close-by building elements and materials, number of walls, number of overlaying stories and surrounding buildings.

PerPos: a platform providing cloud services for pervasive positioning

This paper describes the PerPos platform and the services it provides for positioning and location-based applications. The services provided range from specific utility services to full applications that can be deployed in several ways, e.g. integrated in special purpose applications on mobile devices or as full applications running on ordinary Web-browsers. PerPos furthermore provides APIs for developing positioning utilities and location-based applications. An example of a utility service is a power reduction service for mobile devices, and an example of a SaaS application is a Web-based building model manager that allows users to manage building models stored in the PerPos cloud for annotation, logging, and navigation purposes. A core service in the PerPos platform is sensor fusion for positioning that makes it seamless and efficient to combine a rich set of position sensors to obtain more reliable position and movement data particularly in indoor environments. The PerPos platform and examples of its services are discussed together with the initial experiences with applying those services in application domains such as fire fighting, tracking the behavior of livestock, and indoor navigation support.

Improving pervasive positioning through three-tier cyber foraging

Cyber foraging is a pervasive computing technique where small, mobile devices offload resource intensive work to stronger, nearby surrogate computers in order to preserve energy and achieve better performance. The problem with relying only on local resources is, that the availability of such resources may be scarce in many environments. In this paper we therefore argue that a third tier should be added when considering cyber foraging; namely cloud computing. By considering the local device, nearby surrogates, and the cloud when scheduling, the mobile device may be able to continue using remote resources even when such resources are not available in its vicinity. An important challenge of pervasive computing is estimating the physical position of mobile devices. As the requirements increase for continuous and accurate positioning so does the computational requirements of positioning—even limiting the possible accuracy in many cases. In this paper we describe how a three tier cyber foraging approach can help improve the positioning capabilities of mobile devices. We demonstrate initial results for how such an approach applies to particle filtering-based GSM positioning.

The impact of sensor errors and building structures on particle filter-based inertial positioning

Positioning systems that do not depend on in-building infrastructures are critical for enabling a range of applications within pervasive computing. Particle filter-based inertial positioning promises infrastructure-less positioning, but previous research has not provided an understanding of how the positioning accuracy of such systems depends on the sensor errors and the building structure. This paper evaluates the impact of sensor errors and building structures on the positioning accuracy using a waist-mounted system named Pro-Position. We analyze results from deploying the system in regular and open spaced office buildings as well as in a shopping mall. The results show that differences in accuracy can be explained by error sources of the sensor and the constraints provided by building structures. Additionally, we present and evaluate methods for using GPS positioning with particle filter-based inertial positioning to improve accuracy in large open areas and to provide seamless handover when entering buildings.

Demonstrating EnTracked a system for energy-efficient position tracking for mobile devices

An important feature of a modern mobile device is that it can position itself. Not only for use on the device but also for remote applications that require tracking of the device. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. To address this challenge we have build a system named EnTracked that, based on the estimation and prediction of system conditions and mobility, schedules position updates to both minimize energy consumption and optimize robustness. In this demonstration we would like to show how the system can lower the energy consumption and remain robust as pedestrians targets move around in the city center of Copenhagen.

PerPos: a translucent positioning middleware supporting adaptation of internal positioning processes

A positioning middleware benefits the development of location aware applications. Traditionally, positioning middleware provides position transparency in the sense that it hides low-level details. However, many applications require access to specific details of the usually hidden positioning process. To address this problem this paper proposes a positioning middleware named PerPos that is translucent and adaptable, i.e., it supports both high- and low-level interaction. The PerPos middleware provides translucency with respect to the positioning process and allows programmatic definition of application specific features that can be applied to the internal position processing of the middleware. To evaluate these capabilities we extend the internal position processing of the middleware with functionality supporting probabilistic position tracking and strategies for minimization of the energy consumption. The result of the evaluation is that using only the proposed capabilities we can, in a structured manner, extend the internal positioning processing.

Exposing position uncertainty in middleware

Traditionally, the goal for positioning middleware is to provide developers with seamless position transparency, i.e., providing a connection between the application domain and the positioning sensors while hiding the complexity of the positioning technologies in use. A key part of the hidden complexity is the uncertainty associated to positions caused by inherent limitations when using sensors to convert physical phenomena to digital representations. We propose to use the notion of seamful design for developers to design a positioning middleware that provides transparent positioning and still allows developers some control of the uncertainty aspects of the positioning process. The design presented in this paper shows how uncertainty of positioning can be conceptualized and internalized into a positioning middleware. Furthermore, we argue that a developer who is interacting with uncertainty concepts is best supported when provided with a programming method with declarative constructs.

The Use of GPS for Handling Lack of Indoor Constraints in Particle Filter-Based Inertial Positioning

Particle filter-based inertial positioning promises infrastructure-less positioning, but previous research have not provided an understanding of, how the positioning accuracy of such systems depends on the layout of building structures. This poster presents initial result for the impact of the layout of building structures on the positioning accuracy using a particle filter-based inertial positioning system named Pro-Position. We also consider methods for using GPS positioning with particle filter-based inertial positioning to improve accuracy in areas, where positioning is poor because of lack of constraints from the layouts of the building structures.