Fault-tolerant wireless sensor networks using evolutionary games

This dissertation proposes an approach to creating robust communication systems in wireless sensor networks, inspired by biological and ecological systems, particularly by evolutionary game theory. In this approach, a virtual community of agents live inside the network nodes and carry out network functions. The agents use different strategies to execute their functions, and these strategies are tested and selected by playing evolutionary games. Over time, agents with the best strategies survive, while others die. The strategies and the game rules provide the network with an adaptive behavior that allows it to react to changes in environmental conditions by adapting and improving network behavior. To evaluate the viability of this approach, this dissertation also describes a micro-component framework for implementing agent-based wireless sensor network services, an evolutionary data collection protocol built using this framework, ECP, and experiments evaluating the performance of this protocol in a faulty environment. The framework addresses many of the programming challenges in writing network software for wireless sensor networks, while the protocol built using the framework provides a means of evaluating the general viability of the agent-based approach. The results of this evaluation show that an evolutionary approach to designing wireless sensor networks can improve the performance of wireless sensor network protocols in the presence of node failures. In particular, we compared the performance of ECP with a non-evolutionary rule-based variant of ECP. While the purely-evolutionary version of ECP has more routing timeouts than the rule-based approach in failure-free networks, it sends significantly fewer beacon packets and incurs statistically fewer routing timeouts in both simple fault and periodic fault scenarios

A biologically inspired architecture for self-managing sensor networks

Abstract—This paper describes a sensor network architecture, called BiSNET, which addresses several key issues in wireless sensor networks such as autonomy, adaptability and self-healing. Based on the observation that various biological systems have developed mechanisms necessary to overcome these issues, BiS-NET follows certain biological principles such as decentralization, food gathering/storage and natural selection to design sensor networks. This paper describes and evaluates the biologicallyinspired mechanisms in BiSNET. Preliminary simulation results show that BiSNET allows sensor nodes to autonomously adapt their duty cycles for power efficiency and responsiveness of data transmission and to collectively self-heal (i.e., detect and eliminate) false positives in their sensor readings. 1

Effiziente Algorithmen der Positionsbestimmung und positionsbasierte Kontextgewinnung zur Selbstorganisation in drahtlosen Sensornetzwerken

Als zentrales Thema der Arbeit wird die Positionsbestimmung einzelner Knoten innerhalb drahtloser Sensornetzwerke betrachtet. Im zweiten Themenkomplex, der Clusterbildung, wird zum einen ein auf Lokalisierung aufbauendes Verfahren betrachtet. Zum anderen wird ein Algorithmus vorgestellt, welcher nicht auf die Ermittlung konkreter Positionen angewiesen ist. Das im dritten Themenkomplex betrachtete Verfahren zur Erkennung von Fehlern innerhalb drahtloser Sensornetzwerke bietet eine Möglichkeit, um Fehler innerhalb ermittelter Informationen in drahtlosen Sensornetzwerken zu erkennen.As its main topic this thesis deals with positioning of single nodes within wireless sensor networks. Clustering in wireless sensor networks forms the second part of this work. Two newly developed algorithms will be presented. One of them is based on location information. The other uses coarse grained localization technique but without the need for location information. The third topic of this thesis is about an algorithm newly developed to detect erroneous data at a sensor node