Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

Analysis of cumulative effects of multiple stressors on saltmarshes and consideration of management options

Natural systems face pressures exerted by natural physical-chemical forcings and a myriad of co-occurring human stressors that may interact to cause larger than expected effects, thereby presenting a challenge to ecosystem management. This thesis aimed to develop new information that can contribute to reduce the existing knowledge gaps hampering the holistic management of multiple stressors. I undertook a review of the state-of-the-art methods to detect, quantify and predict stressor interactions, identifying techniques that could be applied in this thesis research. Then, I conducted a systematic review of saltmarsh multiple stressor studies in conjunction with a multiple stressor mapping exercise for the study system in order to infer potential important synergistic stressor interactions. This analysis identified key stressors that are affecting the study system, but also pointed to data gaps in terms of driver and pressure data and raised issues for potentially overlooked stressors. Using field mesocosms, I explored how a local stressor (nutrient availability) affects the responses of saltmarsh vegetation to a global stressor (increased inundation) in different soil types. Results indicate that saltmarsh vegetation would be more drastically affected by increased inundation in low than in medium organic matter soils, and especially in estuaries already under high nutrient availability. In another field experiment, I examined the challenges of managing co-occurring and potentially interacting local stressors on saltmarsh vegetation: recreational trampling and smothering by deposition of excess macroalgal wrack due to high nutrient loads. Trampling and wrack prevention had interacting effects, causing non-linear responses of the vegetation to simulated management of these stressors, such that vegetation recovered only in those treatments simulating the combined prevention of both stressors. During this research I detected, using molecular genetic methods, a widespread presence of S. anglica (and to a lesser extent S. townsendii), two previously unrecorded non-native Spartinas in the study areas.