Graphical models and message-passing algorithms for network-constrained decision problems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 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. [201]-210).Inference problems, typically posed as the computation of summarizing statistics (e.g., marginals, modes, means, likelihoods), arise in a variety of scientific fields and engineering applications. Probabilistic graphical models provide a scalable framework for developing efficient inference methods, such as message-passing algorithms that exploit the conditional independencies encoded by the given graph. Conceptually, this framework extends naturally to a distributed network setting: by associating to each node and edge in the graph a distinct sensor and communication link, respectively, the iterative message-passing algorithms are equivalent to a sequence of purely-local computations and nearest-neighbor communications. Practically, modern sensor networks can also involve distributed resource constraints beyond those satisfied by existing message-passing algorithms, including e.g., a fixed small number of iterations, the presence of low-rate or unreliable links, or a communication topology that differs from the probabilistic graph. The principal focus of this thesis is to augment the optimization problems from which existing message-passing algorithms are derived, explicitly taking into account that there may be decision-driven processing objectives as well as constraints or costs on available network resources. The resulting problems continue to be NP-hard, in general, but under certain conditions become amenable to an established team-theoretic relaxation technique by which a new class of efficient message-passing algorithms can be derived. From the academic perspective, this thesis marks the intersection of two lines of active research, namely approximate inference methods for graphical models and decentralized Bayesian methods for multi-sensor detection.(cont)The respective primary contributions are new message-passing algorithms for (i) "online" measurement processing in which global decision performance degrades gracefully as network constraints become arbitrarily severe and for (ii) "offline" strategy optimization that remain tractable in a larger class of detection objectives and network constraints than previously considered. From the engineering perspective, the analysis and results of this thesis both expose fundamental issues in distributed sensor systems and advance the development of so-called "self-organizing fusion-layer" protocols compatible with emerging concepts in ad-hoc wireless networking.by O. Patrick Kreidl.Ph.D

Garduino: Using Image Processing to Measure Health in Plants

Honorable Mention Winner As the world population increases, so do the demands for more efficient (less energy-consuming) methods of food cultivation. The vision of “precision agriculture” strives for greater farmland efficiency through advances in technology (sensors, robots), promising capabilities beyond what is possible from only manual labor. The University of North Florida’s “Garduino” project, providing a “hands-on” garden bed within which customized automated solutions can be piloted, aims to prepare engineering and computing undergraduates for this precision agriculture vision. A particularly valuable data source in this vision is near-field crop imagery, as may be acquired via self-navigating ground robots with cameras, for example. We report on experiences with using the image processing toolbox in MATLAB to measure green pixel density to monitor growth and health in plants, assuming increasing green density is an indicator of plant growth. Our approach depends upon a two-step image enhancement algorithm, first transforming the RGB color input image into its HSV parameterization and then employing a threshold-based intensity transformation. Error is quantified by comparing the algorithm’s results to the manual determination of the plant’s growth and health. Preliminary results support the usefulness of our approach. Future research will generalize the algorithm and broaden the scope of its agricultural applications

Decentralized detection in sensor network architectures with feedback

We study a decentralized detection architecture in which each of a set of sensors transmits a highly compressed summary of its observations (a binary message) to a fusion center, which then decides on one of two alternative hypotheses. In contrast to the star (or “parallel”) architecture considered in most of the literature, we allow a subset of the sensors to both transmit their messages to the fusion center and to also broadcast them to the remaining sensors. We focus on the following architectural question: is there a significant performance improvement when we allow such a message broadcast? We consider the error exponent (asymptotically, in the limit of a large number of sensors) for the Neyman-Pearson formulation of the detection problem. We prove that the sharing of messages does not improve the optimal error exponent.National Science Foundation (U.S.) (grant ECCS-0701623

Distributed cooperative control architectures for automated manufacturing systems

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 163-165).by O. Patrick Kreidl.M.S

On the use of feedback in an introduction-based reputation protocol

Consider a network environment with no central authority in which each node gains value when transacting with behaving nodes but risks losing value when transacting with misbehaving nodes. One recently proposed mechanism for curbing the harm by misbehaving nodes is that of an introduction-based reputation protocol [1]: transactions are permitted only between two nodes who (i) consent to being connected through introduction via a third node and (ii) provide binary-valued feedback about one another to that introducer when the connection closes. This paper models probabilistically the decision processes by which this feedback is both generated and interpreted - the associated reputation management algorithms account for different modes of misbehavior, respect the inherent information decentralization and are consistent with the utility-maximizing decisions established previously for other parts of the protocol

Robot Navigation in Unknown Environments Using Priority Based Objective Scheduling

Robot navigation at its core is used to achieve some objective, and accurate traversal cost estimation is crucial to path planning in unknown environments. Dynamically predicting traversal cost can be computationally expensive, and the imposed limitations require that an efficient solution is realized. This research, a continuation of research conducted in 2017, proposes a way to navigate unknown environments while taking into consideration objective priority when path planning. By treating the objective prioritization as a priority task scheduling problem in terms of objective throughput, a solution can be derived