Energy-efficient sentient computing

In a bid to improve the interaction between computers and humans, it is becoming necessary to make increasingly larger deployments of sensor networks. These clusters of small electronic devices can be embedded in our surroundings and can detect and react to physical changes. They will make computers more proactive in general by gathering and interpreting useful information about the physical environment through a combination of measurements. Increasing the performance of these devices will mean more intelligence can be embedded within the sensor network. However, most conventional ways of increasing performance often come with the burden of increased power dissipation which is not an option for energy-constrained sensor networks. This thesis proposes, develops and tests a design methodology for performing greater amounts of processing within a sensor network while satisfying the requirement for low energy consumption. The crux of the thesis is that there is a great deal of concurrency present in sensor networks which when combined with a tightlycoupled group of small, fast, energy-conscious processors can result in a significantly more efficient network. The construction of a multiprocessor system aimed at sensor networks is described in detail. It is shown that a routine critical to sensor networks can be sped up with the addition of a small set of primitives. The need for a very fast inter-processor communication mechanism is highlighted, and the hardware scheduler developed as part of this effort forms the cornerstone of the new sentient computing framework by facilitating thread operations and minimising the time required for context-switching. The experimental results also show that end-to-end latency can be reduced in a flexible way through multiprocessing. This thesis is dedicated to my loving parents.