Carl Fischer

Location and Navigation Support for Emergency Responders: A Survey

As this overview of products and projects shows, preinstalled location systems, wireless sensor networks, and inertial sensing all have benefits and drawbacks when considering emergency response requirements.

Supporting device discovery and spontaneous interaction with spatial references

The RELATE interaction model is designed to support spontaneous interaction of mobile users with devices and services in their environment. The model is based on spatial references that capture the spatial relationship of a user’s device with other co-located devices. Spatial references are obtained by relative position sensing and integrated in the mobile user interface to spatially visualize the arrangement of discovered devices, and to provide direct access for interaction across devices. In this paper we discuss two prototype systems demonstrating the utility of the model in collaborative and mobile settings, and present a study on usability of spatial list and map representations for device selection.

Tutorial: Implementing a Pedestrian Tracker Using Inertial Sensors

Shoe-mounted inertial sensors offer a convenient way to track pedestrians in situations where other localization systems fail. This tutorial outlines a simple yet effective approach for implementing a reasonably accurate tracker. This Web extra presents the Matlab implementation and a few sample recordings for implementing the pedestrian inertial tracking system using an error-state Kalman filter for zero-velocity updates (ZUPTs) and orientation estimation.

A comparative study of DECT and WLAN signals for indoor localization

While there is more to context than location, localization and positioning must continue to be improved. Location-aware applications, such as Google Latitude, are enjoying great popularity. Location-based applications and services are widely used on platforms such as the iPhone. The localization itself thus remains an issue, especially in indoor scenarios.

Virtual lifeline: Multimodal sensor data fusion for robust navigation in unknown environments

We present a novel, multimodal indoor navigation technique that combines pedestrian dead reckoning (PDR) with relative position information from wireless sensor nodes. It is motivated by emergency response scenarios where no fixed or pre-deployed global positioning infrastructure is available and where typical motion patterns defeat standard PDR systems. We use RF and ultrasound beacons to periodically re-align the PDR system and reduce the impact of incremental error accumulation. Unlike previous work on multimodal positioning, we allow the beacons to be dynamically deployed (dropped by the user) at previously unknown locations. A key contribution of this paper is to show that despite the fact that the beacon locations are not known (in terms of absolute coordinates), they significantly improve the performance of the system. This effect is especially relevant when a user re-traces (parts of) the path he or she had previously travelled or lingers and moves around in an irregular pattern at single locations for extended periods of time. Both situations are common and relevant for emergency response scenarios. We describe the system architecture, the fusion algorithms and provide an in depth evaluation in a large scale, realistic experiment.

Collaborative PDR Localisation with Mobile Phones

We investigate how an ad hoc collaboration between devices which happen to be physically close to each other can improve the quality of pedestrian dead-reckoning (PDR). The general idea is that whenever two users come close to each other, their devices use the proximity information to improve their PDR location estimates. In a public space the improvement will not only affect the two involved users, but also all the other people that they will meet and collaborate with in the future. On data collected from the mobile phones of 12 users over a course of three days during an open air festival in Malta (a total of 60 walked kilometres) we demonstrate that such collaboration can improve the localisation accuracy by a factor of four and prevent unbounded PDR error. The results imply that collaboration in crowded public spaces enables even simple smart phone-based PDR systems to provide effective localisation over long time periods and distances.

iCOP: Automatically Identifying New Child Abuse Media in P2P Networks

The increasing levels of child sex abuse (CSA) media being shared in peer-to-peer (P2P) networks pose a significant challenge for law enforcement agencies. Although a number of P2P monitoring tools to detect offender activity in such networks exist, they typically rely on hash value databases of known CSA media. Such an approach cannot detect new or previously unknown media being shared. Conversely, identifying such new previously unknown media is a priority for law enforcement - they can be indicators of recent or on-going child abuse. Furthermore, originators of such media can be hands-on abusers and their apprehension can safeguard children from further abuse. The sheer volume of activity on P2P networks, however, makes manual detection virtually infeasible. In this paper, we present a novel approach that combines sophisticated filename and media analysis techniques to automatically flag new previously unseen CSA media to investigators. The approach has been implemented into the iCOP toolkit. Our evaluation on real case data shows high degrees of accuracy while hands-on trials with law enforcement officers highlight iCOPs usability and its complementarity to existing investigative workflows.

iCOP: Live forensics to reveal previously unknown criminal media on P2P networks

publisher: Elsevier articletitle: iCOP: Live forensics to reveal previously unknown criminal media on P2P networks journaltitle: Digital Investigation articlelink: http://dx.doi.org/10.1016/j.diin.2016.07.002 content_type: article copyright:

SLAM for Pedestrians and Ultrasonic Landmarks in Emergency Response Scenarios

Providing ad hoc solutions for positioning and tracking of emergency response teams is an important and safety-critical challenge. Although solutions based on inertial sensing systems are promising, they are subject to drift. We address the problem of positional drift by having the responders themselves deploy sensor nodes capable of sensing range and angle-of-arrival, as they progress into an unknown environment. Our research focuses on a sensor network approach that does not rely on preexisting infrastructure. This chapter targets two important aspects of such a solution: how to locate the deployed static sensor nodes, and how to track the responders by using a combination of ultrasound and inertial measurements. The main contributions of this chapter are: (i) a characterization of the errors encountered in inertial-based pedestrian dead-reckoning as well as ultrasound range and bearing measurements in a mobile setting, (ii) the formulation of an extended Kalman filter for simultaneously locating sensor nodes and tracking a pedestrian using a combination of ultrasound range/bearing measurements and inertial measurements, and (iii) the validation of the presented algorithms using data collected from real deployments.