Resource-Efficient Processing and Communication in Sensor/Actuator Environments

The future of computer systems will not be dominated by personal computer like hardware platforms but by embedded and cyber-physical systems assisting humans in a hidden but omnipresent manner. These pervasive computing devices can, for example, be utilized in the home automation sector to create sensor/actuator networks supporting the inhabitants of a house in everyday life.

The efficient usage of resources is an important topic at design time and operation time of mobile embedded and cyber-physical systems. Therefore, this thesis presents methods which allow an efficient use of energy and processing resources in sensor/actuator networks. These networks comprise different nodes cooperating for a smart joint control function. Sensor/actuator nodes are typical cyber-physical systems comprising sensors/actuators and processing and communication components. Processing components of today’s sensor nodes can comprise many-core chips.

This thesis introduces new methods for optimizing the code and the application mapping of the aforementioned systems and presents novel results with regard to design space explorations for energy-efficient and embedded many-core systems. The considered many-core systems are graphics processing units. The application code for these graphics processing units is optimized for a particular platform variant with the objectives of minimal energy consumption and/or of minimal runtime. These two objectives are targeted with the utilization of multi-objective optimization techniques. The mapping optimizations are realized by means of multi-objective design space explorations. Furthermore, this thesis introduces new techniques and functions for a resource-efficient middleware design employing service-oriented architectures. Therefore, a service-oriented architecture based middleware framework is presented which comprises a lightweight service orchestration. In addition to that, a flexible resource management mechanism will be introduced. This resource management adapts resource utilization and services to an environmental context and provides methods to reduce the energy consumption of sensor nodes.