Building Blocks for Adaptive Modular Sensing Systems

This thesis contributes towards the development of systems and strategies by which sensor and actuator components can be combined to produce flexible and robust sensor systems for a given application. A set of intelligent modular blocks (building blocks) have been created from which composite sensors (made up of multiple sensor and actuator components) can be rapidly reconfigured for the construction of Adaptive Modular Sensing Systems. The composite systems are expected to prove useful in several application domains including industrial control, inspection systems, mobile robotics, monitoring and data acquisition. The intelligent building blocks, referred to as transducer interface modules, contain embedded knowledge about their capabilities and how they can interact with other modules. These modules encapsulate a general purpose modular hardware architecture that provides an interface between the sensors, the actuators, and the communication medium. The geometry of each transducer interface module is a cube. A connector mechanism implemented on each face of the module enables physical connection of the modules. Each module provides a core functionality and can be connected to other modules to form more capable composite sensors. Once the modules are combined, the capabilities (e.g., range, resolution, sample rate, etc.) and functionality (e.g., temperature measurement) of the composite sensor is determined and communicated to other sensors in the enviornment. For maximum flexibility, a distributed software architecture is executed on the blocks to enable automatic acquisition of configuration-specific algorithms. This logical algorithm imparts a collective identity to the composite group, and processes data based on the capabilities and functionalities of the transducers present in the system. A knowledge representation scheme allows each module in the composite group to store and communicate its functionality and capabilities to other connected modules in the system

Basissoftware für drahtlose Ad-hoc- und Sensornetze

Mit dem Titel "Basissoftware für selbstorganisierende Infrastrukturen für vernetzte mobile Systeme" vereint das Schwerpunktprogramm 1140 der DFG Forschungsvorhaben zum Thema drahtloser Ad-hoc- und Sensornetze. Durch die Konzeption höherwertiger Dienste für diese aufstrebenden Netztypen leistet das Schwerpunktprogramm einen essentiellen Beitrag zur aktuellen Forschung und erschafft gleichzeitig ein solides Fundament zur Entwicklung zahlreicher Anwendungen

Ressourcenarme und dezentrale Lokalisierung autonomer Sensorknoten in Sensornetzwerken

Diese Dissertation behandelt Distanz- und Lokalisierungsverfahren in ressourcenarmen Sensornetzwerken. Den Kern der Arbeit bildet die ressourcenarme Lokalisierung von Sensorknoten. In Sensornetzwerken mit geringen Genauigkeitsanforderungen kann die einfache Schwerpunktbestimmung (CL) als Lokalisierungsverfahren eingesetzt werden. In den Randgebieten eines Netzwerkes erreicht der CL-Algorithmus jedoch nur eine sehr geringe Genauigkeit. Das neu vorgestellte Verfahren Centroid Localization with Edge Correction (CLwEC) reduziert diesen Lokalisierungsfehler bei geringfügig höherem Ressourcenaufwand deutlich. Eine weitere Verbesserung der Lokalisierung wird durch ein gewichtete Schwerpunktverfahren (WCL) erreicht.This thesis describes algorithms to determine distances and positions in resource-constrained sensor networks. In sensor networks with low accuracy requirements, node’s positions can be calculated by the centroid localization algorithm (CL) provided that all reference nodes can be configured optimal regarding the presented method. In borderlands of a sensor network, the accuracy of the centroid localization decreases enormously. The new presented algorithm centroid localization with edge correction (CLwEC) reduces this positioning error significantly and requires, in comparison the CL, only slightly increased resources. A further improvement of the positioning error was achieved by the new introduced weighted centroid localization algorithm (WCL)