As sensor network technologies become more mature, they are increasingly being applied to a wide variety of environmental monitoring applications, ranging from agricultural sensing to habitat monitoring, oceanic and volcanic monitoring. In this dissertation two wireless sensor networks (WSNs) are presented. One for monitoring residential power usage and another for producing an image of a volcano's internal structure.The two WSNs presented address several common challenges facing modern sensor networks. The first is in-network processing and assigning the processing tasks across a heterogeneous network architecture. By efficiently utilizing in-network processing power consumption can be reduced and operational lifetime of the network can be extended. As nodes are embedded into various environments sensing accuracy is intrinsically affected by physical noise. The second challenge relates to how to deal with this noise in a way which increases sensing accuracy. The third challenge is ease of deployment. As WSNs become more common place they will be installed by non-experts.As a key technology of home area networks in smart grids, fine-grained power usage monitoring may help conserve electricity. Smart homes outfitted with network connected appliances will provide this capability in the future. Until smart appliances have wide adaption there is a serious gap in capabilities. To fill this gap an easy to deploy monitoring system is needed. Several existing systems achieve the goal of fine-grained power monitoring by exploiting appliances' power usage signatures utilizing labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in high installation cost. Supero is the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multi-sensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, non-professional users can quickly deploy Supero with considerable flexibility.There are a number of active volcanos around the world with large population areas located nearby. An eruption poses a significant threat to the adjacent population. During times of increased activity being able to obtain a real-time images of the interior would allow seismologists to better understand volcanic dynamics. Volcano tomography can provide this valuable information concerning the internal structure of a volcano. The second sensor network presented in this dissertation is a seismic monitoring sensor network featuring in-network processing of the seismic signals with the capability to perform volcano tomography in real-time. The design challenges, analysis of processing/network processing times in the information processing pipeline, the system designed to meet these challenges and the results from deploying a prototype network on two volcanoes in Ecuador and Chile are presented. The study shows that it is possible to achieve in-network seismic event detection and real-time tomography using a sensor network that is 2 orders of magnitude less expensive than traditional seismic equipment.
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