USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

Recurrent voluminous sector collapses at Volcán Barú, Panama

Two volcanic debris avalanche deposits (VDADs), both attributed to sector collapse at Volcán Barú, Panama, have been identified after an investigation of deposits that covered more than a thousand square kilometers. The younger Barriles Deposit is constrained by two radiocarbon ages that are ~9 ka; the older Caisán Deposit is at or beyond the radiocarbon range, >43,500 ybp. The total runout length of the Caisán Deposit was ~50 km and it covers 1190 km2. The Barriles Deposit extended to about 45 km and covered an area of 966 km2, overlapping most of the Caisán. The VDADs are blanketed by pyroclastic deposits and contain a predominance of andesitic material likely representing volcanic dome rock which accumulated above the active vent at Barú before collapsing. Despite heavy vegetation in the field area, over 4000 individual hummocks were digitized from aerial photography. Statistical analysis of hummock locations and geometries depict flow patterns of highly- fragmented material reflecting the effects of underlying topography and also help to define the limit of Barriles’ shorter termination. Barriles and Caisán are primarily unconfined, subaerial volcanic deposits that are among the world’s most voluminous. Calculated through two different geospatial processes, thickness values from field measurements and inferences yield volumes >30 km23 for both deposits. VDADs of comparable scale come from Mount Shasta, USA; Socompa, Chile/Argentina; and Shiveluch, Russia. Currently, the modern edifice is 200-400m lower than the pre-collapse Barriles and Caisán summits and only 16-25% of the former edifice has been replaced since the last failure.