Towards discrete-pulse-based networking and event detection architectures for resource-constrained applications

"In this dissertation thesis, we develop a scalable and energy-efficient discrete-pulse-based networking architecture along with a Spiking-Neuron-based low-power detection framework for use in resource-constrained settings. Applications such as Structural Health Monitoring (SHM) using wireless sensor networks powered by ambient energy harvesting are particularly suited for such a framework. The key idea in pulse-based networking is to eschew unnecessary overhead as incurred in traditional packet-based networking and encode only the essential information using small number of discrete pulses and their positions with respect to a synchronized time frame structure. The baseline pulse networking does not scale well with increase in network size. In order to ameliorate this, we develop a scalable time frame structure for use in applications with large network size while preserving the energy advantages of pulse networking. In addition, we stress the importance of judicious use of erratic energy availability in ambient energy harvesting powered systems. To that effect, we build energy-awareness syntaxes within the pulse networking framework for better utilization of energy resources in such systems. We also demonstrate the feasibility of pulse networking over a through-substrate ultrasonic link layer and the advantages thereof in terms of utilizing existing infrastructure and removing the need for radio retrofits. We explore how the protocol performance varies for an airplane stabilizer monitoring application powered by ambient vibration energy harvesting in different energy availability scenarios. Beyond this, we also develop a Spiking-Neuron-based low-power event pattern detection architecture and illustrate how this can be incorporated within a pulse-networked SHM system. The Spiking Neuron based architecture is evidenced to be simpler in terms of implementation but more efficient in terms of computation and energy usage, thus enabling in-situ detection even at intermediate nodes in the network and robust low-power event pattern detection immune to pulse drifts and errors."--Pages ii-iii.