A Distributed Intelligent Sensing Approach for Environmental Monitoring Applications

Scientific reports from around the world present us with the undeniable fact that the global ecosystem is undergoing severe change. As this shift accelerates, it is ever more critical that we are able to quantify the local effects of such changes, and further, their implications, from our daily life to the biological processes that put food on our tables. In this thesis, we study the application of sensor network technology to the observation and estimation of highly local phenomena---specifically at a scale between ten to several hundred square meters. Embedding knowledge about the observed process directly into the sensor nodes' behavior via dedicated resource management or control algorithms allows us to deploy dense networks with low power requirements. Ecological systems are notoriously complex. In our work we must thus be highly experimental; it is our highest goal that we construct an approach to environmental monitoring that is not only realistic, but practical for real-world use. Our approach is centered on a commercially available sensor network product, aided by an off-the-shelf quadrotor with minimal customization. We validate our approach through a series of experiments performed from simulation all the way to reality, in deployments lasting days to several months. We motivate the need for local data via two case studies examining physical phenomena. First, employing novel modalities, we study the eclosion of a common agricultural pest. We present our efforts to acquire data that is more local than commonly employed methods, culminating in a six month deployment in a Swiss apple orchard. Next, we apply a environmental fluid dynamics model to enable the estimation of sensible heat flux using an inexpensive sensor. We integrate the sensor with a wireless sensor network and validate its capabilities in a short-term deployment. Acquiring meaningful data on a local scale requires that we advance the state of the art in multiple aspects. Static sensor networks present a classical tension between resolution, autonomy, and accuracy. We explore the performance of algorithms aimed at providing all three, showing explicitly what is required to implement these approaches for real-world applications in an autonomous deployment under uncontrolled conditions. Eventually, spatial resolution is limited by network density. Such limits may be overcome by the use of mobile sensors. We explore the use of an off-the-shelf quadrotor, equipped with environmental sensors, as an additional element in system of heterogeneous sensing nodes. Through a series of indoor and outdoor experiments, we quantify the contribution of a such a mobile sensor, and various strategies for planning its path

Numerical Relativity Studies in Black Hole Astrophysics

Black holes have long fascinated both physicists and the general population alike. In an astrophysical context, black holes participate in interesting interactions with not only stars, but also other black holes. Moreover, the recent detections of gravitational waves from both black hole-black hole and black hole-neutron star systems have only served to amplify excitement in the field of black hole astrophysics. Over the last few decades, numerical relativity has come to be a versatile tool for studying both of these classes of encounter. In this thesis, I present a collection of numerical relativity studies of black holes in the context of binary black hole mergers and tidal disruption events.Ph.D

Coordinating measures to reduce fuel use and greenhouse gas emissions from U.S. light-duty vehicles

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 101-110).The challenges of energy security and climate change have prompted efforts to reduce fuel use and greenhouse gas emissions in light-duty vehicles within the United States. Failures in the market for lower rates of fuel consumption necessitate government involvement. But efforts have been weakened by a controversial regulatory system, and the need for perverse incentives that have contributed to a slight increase in the average rate of light-duty vehicle fuel consumption alongside a 70% increase in vehicle travel relative to the mid-80's. This research evaluates the role of fiscal policies in overcoming barriers to reducing fuel use and greenhouse gas emissions in U.S. light-duty vehicles. It conducts a survey of fiscal policies and their implementation internationally. A model of the U.S. light-duty vehicle fleet is used to assess a fuel tax in comparison to -- and in coordination with -- the recently legislated Corporate Average Fuel Economy (CAFE) standard legislated by the Energy Independence and Security Act. Engineering cost estimates of technology improvements and vehicle powertrains are used to evaluate the costs and benefits of a technology penetration scenario that approximates the new CAFE standard. Alongside CAFE, fiscal options can achieve reductions more effectively by: (i) acting on a broader range of stakeholders; (ii) influencing behavioral responses as well as technological changes; and (iii) by sending price signals across multiple stages of vehicle purchase, operation, and retirement. Using illustrative scenarios, the report demonstrates that fiscal policies align consumer demand for lower rates of fuel consumption with the requirements that CAFE imposes on manufacturers.(cont.) The costs of reducing fuel consumption are estimated to be 8 to 20% of the baseline cost if fuel consumption remained unchanged from today, corresponding to retail price increases of $1,500 to$4,500 for the average vehicle between 2020 and 2035. These significant costs are largely offset by fuel savings benefits within 2 to 4 years relative to no change.by Christopher W. Evans.S.M

Sher 25: pulsating but apparently alone

The blue supergiant Sher25 is surrounded by an asymmetric, hourglass-shaped circumstellar nebula, which shows similarities to the triple-ring structure seen around SN1987A. From optical spectroscopy over six consecutive nights, we detect periodic radial velocity variations in the stellar spectrum of Sher25 with a peak-to-peak amplitude of ~12 km/s on a timescale of about 6 days, confirming the tentative detec-tion of similar variations by Hendry et al. From consideration of the amplitude and timescale of the signal, coupled with observed line profile variations, we propose that the physical origin of these variations is related to pulsations in the stellar atmosphere, rejecting the previous hypothesis of a massive, short-period binary companion. The radial velocities of two other blue supergiants with similar bipolar nebulae, SBW1 and HD 168625, were also monitored over the course of six nights, but these did not display any significant radial velocity variations.Comment: 9 pages, 7 figures. Accepted for publication in MNRA

A Flexible In Situ Power Monitoring Unit for Environmental Sensor Networks

Wireless radios are a great consumer of energy in sensor networks. Retrieving data from a remote deployment in an energy-efficient fashion is a difficult problem, and while solutions have been proposed in literature, real-world systems typically implement robust though inefficient methods. In an effort to bring efficient monitoring techniques to real-world environmental sensor networks, we seek to now quantify the performance brought by these algorithms in practical terms, i.e., by their resulting reduction in overall station energy consumption. To this end, we have developed a power monitoring extension board that integrates seamlessly with a commercial environmental sensor network platform. The board is capable of measuring all incoming and outgoing power for a station, and can disconnect subsystems, such as the solar panel or sensor bus, as necessary. The board is currently deployed on an outdoor network and is undergoing extensive testing

Optoelectronic analysis of multijunction wire array solar cells

Wire arrays have demonstrated promising photovoltaic performance as single junction solar cells and are well suited to defect mitigation in heteroepitaxy. These attributes can combine in tandem wire array solar cells, potentially leading to high efficiencies. Here, we demonstrate initial growths of GaAs on Si_(0.9)Ge_(0.1) structures and investigate III-V on Si_(1-x)Ge_x device design with an analytical model and optoelectronic simulations. We consider Si_(0.1)Ge_(0.9) wires coated with a GaAs_(0.9)P_(0.1) shell in three different geometries: conformal, hemispherical, and spherical. The analytical model indicates that efficiencies approaching 34% are achievable with high quality materials. Full field electromagnetic simulations serve to elucidate the optical loss mechanisms and demonstrate light guiding into the wire core. Simulated current-voltage curves under solar illumination reveal the impact of a varying GaAs_(0.9)P_(0.1) minority carrier lifetime. Finally, defective regions at the hetero-interface are shown to have a negligible effect on device performance if highly doped so as to serve as a back surface field. Overall, the growths and the model demonstrate the feasibility of the proposed geometries and can be used to guide tandem wire array solar cell designs

Top-Down vs Bottom-Up Model-Based Methodologies for Distributed Control: A Comparative Experimental Study

Model-based synthesis of distributed controllers for multi-robot systems is commonly approached in either a top-down or bottom-up fashion. In this paper, we investigate the experimental challenges of both approaches, with a special emphasis on resource-constrained miniature robots. We make our comparison through a case study in which a group of 2-cm-sized mobile robots screen the environment for undesirable features, and destroy or neutralize them. First, we solve this problem using a top-down approach that relies on a graph-based representation of the system, allowing for direct optimization using numerical techniques (e.g., linear and non-linear convex optimization) under very unrealistic assumptions (e.g., infinite number of robots, perfect localization, global communication, etc.). We show how one can relax these assumptions in the context of resource-constrained robots, and explain the resulting impact on system performance. Second, we solve the same problem using a bottom-up approach, i.e., we build up computationally efficient and accurate models at multiple abstraction levels, and use them to optimize the robots' controller using evolutionary algorithms. Finally, we outline the differences between the top-down and bottom-up approaches, and experimentally compare their performance

Aggregation-mediated Collective Perception and Action in a Group of Miniature Robots

We introduce a novel case study in which a group of miniaturized robots screen an environment for undesirable cells, and destroy them. Because miniaturized robots are usually endowed with reactive controllers and minimalist sensing and actuation capabilities, they must collaborate in order to achieve their task successfully. In this paper, we show how aggregation can mediate both collective perception and action while maintaining the scalability of the algorithm. First, we demonstrate the feasibility of our approach by implementing it on a real group of Alice mobile robots, which are only two centimeters in size. Then, we use a combination of both realistic simulations and macroscopic models in order to find optimal parameters that maximize the number of undesirable cells destroyed while minimizing the impact on the healthy population. Finally, we discuss the limitations of these models, both in terms of accuracy, computational cost, and scalability, and we outline the importance of an appropriate multi-level modeling methodology to ensure the relevance and the faithfulness of such models