During the last decades, a great shift to processing on mobile and ubiquitous devices has been taken place. With this movement also the integration of different sensors, especially inertial sensors, has gained strong interest. One approach is to combine inertial sensors with other localization systems. However, this needs real-time processing capabilities and low power consumption, which is usually a problem when performing this task on the main processor with the operating system on top. In this paper, we present an adaptive localization processor called SmartLoCore including an intuitive development flow that allows hardware/software co-design trade-offs during design-time. These options include running the algorithm in software, adding new additional instructions to the core instruction-set and off-loading complex mathematics onto dedicated hardware. We show the applicability with a complex localization application example and highlight the benefits of the proposed concept. We conclude that the SmartLoCore concept shows a very promising potential for a large variety of mobile systems.

To allow for rapid abstract development of algorithms, model-based tools are often used nowadays. These tools support design using predefined blocks, adaptation using parameters, simulation in a virtual environment and also generation of code for programming embedded devices. However, there is usually no direct connection between the embedded system and the model for programming and runtime control and monitoring. In this paper, we present a seamless tool chain for modelbased algorithm development, simulation and integration on an embedded system as well as runtime control and monitoring from model level. Based on our concept, the development of algorithms can largely be separated from the execution platform and its control and monitoring possibilities. Yet, it is possible to feedback monitored data to support the simulation. The concept is integrated and evaluated and its applicability for the development of an algorithm is demonstrated by means of an example from the field of wireless senor networks localization. We conclude that the outlined approach offers a simple opportunity to reduce development overhead.

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.

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.