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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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Localization in previously unknown and GPS-denied areas is challenging, especially, when no infrastructure can be assumed. In this paper, we present a hybrid person localization approach for ad-hoc scenarios. Example applications are search and rescue operations, but also on construction sites or during maintenance tasks of large industrial plants. The outlined scheme is based on received signal strength localization in a wireless sensor network (WSN) and data fusion of inertial sensors. However, the complexity of such systems poses a challenge to non-technicians to operate them. To improve operability, the model-based design principle has been a rigorous change to systems design methodology. In this paper, we adopt the strengths of model-based methods to a heterogeneous distributed and networked system. The advantage of our approach is that the same model-based design environment is later used for control and monitoring of the network in the actual deployment. This allows easy reconfiguration of parameters during an operation. We present some localization results and highlight advantages and experiences gained from our evaluations. We conclude that model-based development on one hand and also model-based runtime control on the other hand are important aspects for future WSN deployments. [ABSTRACT FROM PUBLISHER]

Personal location discovery and navigation within buildings has become an important research topic in the last years. One method to determine one's 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. [ABSTRACT FROM PUBLISHER]