Johannes Schmid

Smartphone pedestrian navigation by foot-IMU sensor fusion

Determining ones own position by means of a smartphone is an important issue for various applications in the fields of personal navigation or location-based services. Places like large airports, shopping malls or extensive underground parking lots require personal navigation but satellite signals and GPS connection cannot be obtained. Thus, alternative or complementary systems are needed. In this paper a system concept to integrate a foot-mounted inertial measurement unit (IMU) with an Android smartphone is presented. We developed a prototype to demonstrate and evaluate the implementation of pedestrian strapdown navigation on a smartphone. In addition to many other approaches we also fuse height measurements from a barometric sensor in order to stabilize height estimation over time. A very low-cost single-chip IMU is used to demonstrate applicability of the outlined system concept for potential commercial applications. In an experimental study we compare the achievable accuracy with a commercially available IMU. The evaluation shows very competitive results on the order of a few percent of traveled distance. Comparing performance, cost and size of the presented IMU the outlined approach carries an enormous potential in the field of indoor pedestrian navigation.

An approach to infrastructure-independent person localization with an IEEE 802.15.4 WSN

In this paper a new concept for an infrastructure independent approach to indoor person localization is outlined. It is proposed to deploy an ad-hoc wireless sensor network (WSN) by means of a pedestrian dead reckoning (PDR) unit. The deployed nodes are initialized with a PDR-determined position estimation. Furthermore, a subset of the nodes has a GPS connection and thus an accurate estimation of the own position. Within this infrastructure, persons that carry on-body sensor nodes can then be localized based on received signal strength (RSS) evaluation and extended Kalman filter (EKF) data fusion. An experimental setup with a 65 node ZigBee sensor network is described and the collected data are evaluated off-line. It is analyzed how RSS person localization performs under experimental real-world conditions. The proposed approach results in a localization accuracy on the order of a couple of meters for realistic parameter settings. Initial experiments on node deployment and a simulative evaluation of the influence of biased anchor node position estimations are presented. It is concluded that the proposed system can obtain an accuracy on the order of 3 meters without necessitating map knowledge or previously deployed infrastructure. This accuracy is sufficient for a range of person localization applications.

On the fusion of inertial data for signal strength localization

Localization in wireless sensor networks (WSN) based on the evaluation of received signal strength (RSS) values of communication packets has received a considerable amount of research interest in the last years. In spite of the number of approaches that have been presented, the performance of most systems remains rather poor due to the unpredictable behavior of RSS values and the resulting fluctuating position estimations. In this paper, an approach to cope with these fluctuations for the purpose of ad-hoc person localization in WSN is presented. A hip-mounted inertial measurement unit (IMU), carried by the person to localize, is used to collect data on the current movement. This data is used to stabilize the RSS position estimation by means of a Kalman filter. The experimental evaluation shows that the proposed method carries the potential to improve RSS localization methods. We present results of experiments in in- and outdoor environments and quantify the improvements that can be achieved by fusing inertial data with RSS position estimation schemes for person localization. It can be concluded that fusing inertial data can improve the performance of RSS-based localization systems.

An experimental evaluation of position estimation methods for person localization in wireless sensor networks

In this paper, the localization of persons by means of aWireless Sensor Network (WSN) is considered. Persons carry on-body sensor nodes and move within a WSN. The location of each person is calculated on this node and communicated through the network to a central data sink for visualization. Applications of such a system could be found in mass casualty events, firefighter scenarios, hospitals or retirement homes for example. n nFor the location estimation on the sensor node, three derivatives of the Kalman filter and a closed-form solution (CFS) are applied, compared, and evaluated in a real-world scenario. A prototype 65-node ZigBeeWSN is implemented and data are collected in in- and outdoor environments with differently positioned on-body nodes. The described estimators are then evaluated off-line on the experimentally collected data. n nThe goal of this paper is to present a comprehensive real-world evaluation of methods for person localization in a WSN based on received signal strength (RSS) range measurements. It is concluded that person localization in in- and outdoor environments is possible under the considered conditions with the considered filters. The compared methods allow for sufficiently accurate localization results and are robust against inaccurate range measurements.

A bi-modal ad-hoc localization scheme for wireless networks based on RSS and ToF fusion

Localization of persons or equipment in ad-hoc scenarios, e.g., after an earthquake still poses many challenges. The popular received signal strength (RSS) based localization methods usually cannot provide the needed accuracy especially if sensor equipment or tools are to be localized in a static environment with non line of sight conditions. On the other hand, time-of-flight (ToF) measurements have shown more accurate results but have difficulties especially if low power hardware or narrow-band wireless systems are used. Inadequate timing capabilities of common low power clocks make synchronization between different nodes in a network difficult. To solve this problem, usual ToF methods use bilateral round trip measurements and high numbers of packets. Compared to one-way RSS measurements, this approach results in a much higher channel load and thus poorer scalability if a large network is assumed. In this paper we present a bi-modal scheme for hybrid RSS/ToF localization in order to tackle this problem. The main idea of the developed approach is to find an equilibrium between inaccurate but efficient RSS measurements on the one hand and precise but more resource-consuming ToF measurements on the other. The goal is to achieve superior localization accuracy in comparison to the use of one method alone. The proposed concept is outlined and achievable results are shown based on experimental data and simulative evaluation. We conclude that the proposed scheme offers good possibilities for improved localization accuracy by keeping the channel usage low.

Improving sparse organic WiFi localization with inertial sensors

Personal location discovery and navigation within buildings has become an important research topic in the last years. One method to determine ones current position based on mobile-devices is to compare the set of available WiFi access points (APs), i.e. the fingerprint of a given space, to a previously collected database. In this context, this paper addresses the inherent problem of such systems that this fingerprint database needs to be established beforehand. Thus, situations can occur where a building is only partially represented in the database and localization can only be provided in a subset of the spaces of the building. This problem occurs especially in crowd-sourcing (organic) approaches where users consecutively contribute location-binds. In these situations an additional system is needed to provide localization. We present a first study on the fusion of pedestrian dead reckoning (PDR) from inertial sensors with position estimates from a WiFi localization system. We outline a possible design of particle filter and analyze its behavior on experimental data. We conclude that the outlined method can help to improve WiFi localization and is especially useful within crowd-sourcing environments.

Force-directed tracking in wireless networks using signal strength and step recognition

Force-directed approaches, also known as spring embedders, are widely used in the context of graph drawing and network embedding. In this paper, we study the application of these methods to signal-strength based tracking in wireless networks. The performance of the presented algorithms is evaluated based on pedestrian tracking experiments in a 60-node wireless sensor network (WSN). Additionally, we compare the outlined approach with implementations of Extended Kalman Filters (EKF) and examine similarities and distinctions between both approaches. The algorithms are developed in a 3-step process. First, we take a brief look at the trilateration problem, where a position is estimated based on a set of noisy signal strength measurements. From this we conclude how signal strengths can be translated into adequate spring forces. Subsequently, we establish a movement model by introducing additional forces between consecutive position estimates. Finally, we show how step information from a pedestrian can be used to improve the localization. Our experimental results indicate that force-directed methods offer an interesting and competitive approach to the tracking problem. Especially the possibility to easily include further information by introducing additional forces makes them very attractive. As modeled forces are not limited to linear functions, non-linear aspects such as distance estimates can be effortlessly modeled. We conclude that the application of force-directed approaches to the tracking problem offers a worthwhile direction for future research.

Component-based models for runtime control and monitoring of embedded systems

Nowadays, more and more developments in the embedded systems domain are based on components and abstract models. However, while the design becomes more abstract, control and monitoring during runtime are often performed on low abstraction levels. In contrast to this low level access we present a seamless design flow for adjustment and error identification using abstract component-based models. We develop an extended metamodel to describe components and their platforms and the connection between the model and the real hardware. Furthermore, we integrate on model level platform abilities for control and especially debugging to support for example real-time recording. From a users perspective the system is designed, controlled and monitored on model level. We discuss different methods concerning runtime control and monitoring of resource constraint systems. We demonstrate the concepts applicability based on two exemplary use cases: wireless sensor network application engineering and reconfigurable hardware development.

A wireless MEMS-sensor network concept for the condition monitoring of ball screw drives in industrial plants

An important factor for the success of industrial manufacturers is a cost-efficient, robust and thus failsafe production. In this context a topic of increasing interest is the continuous monitoring of machine tools to be able to proactively react to signs of degeneration of single components and ensure the technical availability of the whole plant.n The focus of this paper lies on the condition monitoring of ball screw drives (BSD) that are one of the main components of modern feed axes in machine tools. A low cost and easy installable wireless sensor network (WSN) concept for the monitoring of solid borne sound via MEMS acceleration sensors is proposed for this application.

A simplified protocol for energy self-sufficient sensors in an IEEE 802.15.4/ZigBee WSN

For the application of wireless sensor network technology in an energy self-sufficient condition monitoring appliance, we propose a new hybrid network topology to combine the advantages of the ZigBee standard with the need for lowest power consumption when applying an energy harvesting solution for a subset of the sensor nodes. For a research project on a wireless condition monitoring system, we developed a prototype implementation of a partly energy self-sufficient wireless sensor network. We adapted the ZigBee network protocols so that the energy consumption of the self-sufficient nodes is reduced albeit a ZigBee conform communication with the rest of the network is still ensured. In this paper, the prototype system is presented and the problem when applying a full ZigBee stack is stated. We propose to shift parts of the network functionality from the end-devices to the (mains- or battery powered) routers and show actual measurement results and calculated values to indicate the improvements of this adapted hybrid topology.