{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/145185"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/145185","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Decentralized Inference and its Application to Network Localization and Navigation","abstract":"Decentralized inference is important for complex networked systems and enables numerous applications such as network localization and navigation (NLN), Internet-ofThings (IoT), and smart cities. This thesis establishes a theoretical foundation of decentralized inference for networks with limited sensing and communication capabilities. In the considered network, each node aims to infer in real-time an evolving state based on local observations and on messages exchanged with its neighbors. The objectives of the thesis include: (i) designing message encoding strategies that maximize inference accuracy; (ii) establishing connections between information- and estimation-theoretical quantities; and (iii) characterizing the impact of the sensing and communication capabilities of the network on the inference accuracy. First, we investigate a system of two nodes connected via a Gaussian channel. For such a system, we design a real-time strategy for generating the encoded messages exchanged between the nodes and derive conditions under which such a strategy provides optimal inference accuracy. Building on an information-theoretic perspective of Kalman–Bucy filtering in centralized settings, we derive a relationship between Shannon information and Fisher information for decentralized inference. Then, based on results for two-node systems, we characterize the behavior of decentralized inference error in multi-node networks with general channel models. We establish both necessary and sufficient conditions on the sensing and communication capabilities of the network for the boundedness of the mean-square error over time. We show that, in addition to Shannon capacity, anytime capacity plays a critical role in characterizing the impact of the network’s communication capability on the inference accuracy. This thesis deepens the understanding of decentralized inference in complex networked systems; uncovers connections among estimation, information, and control theories; and provides guidelines for designing decentralized inference algorithms and network operation strategies in applications such as NLN and IoT.","abstract_html":"Decentralized inference is important for complex networked systems and enables numerous applications such as network localization and navigation (NLN), Internet-ofThings (IoT), and smart cities. This thesis establishes a theoretical foundation of decentralized inference for networks with limited sensing and communication capabilities. In the considered network, each node aims to infer in real-time an evolving state based on local observations and on messages exchanged with its neighbors. The objectives of the thesis include: (i) designing message encoding strategies that maximize inference accuracy; (ii) establishing connections between information- and estimation-theoretical quantities; and (iii) characterizing the impact of the sensing and communication capabilities of the network on the inference accuracy. First, we investigate a system of two nodes connected via a Gaussian channel. For such a system, we design a real-time strategy for generating the encoded messages exchanged between the nodes and derive conditions under which such a strategy provides optimal inference accuracy. Building on an information-theoretic perspective of Kalman–Bucy filtering in centralized settings, we derive a relationship between Shannon information and Fisher information for decentralized inference. Then, based on results for two-node systems, we characterize the behavior of decentralized inference error in multi-node networks with general channel models. We establish both necessary and sufficient conditions on the sensing and communication capabilities of the network for the boundedness of the mean-square error over time. We show that, in addition to Shannon capacity, anytime capacity plays a critical role in characterizing the impact of the network’s communication capability on the inference accuracy. This thesis deepens the understanding of decentralized inference in complex networked systems; uncovers connections among estimation, information, and control theories; and provides guidelines for designing decentralized inference algorithms and network operation strategies in applications such as NLN and IoT.","abstract_has_math":false,"creators":["Liu, Zhenyu"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Building on an information-theoretic perspective of Kalman–Bucy filtering in centralized settings, we derive a relationship between Shannon information and Fisher information for decentralized inference. Then, based on results for two-node systems, we characterize the behavior of decentralized inference error in multi-node networks with general channel models. We establish both necessary and sufficient conditions on the sensing and communication capabilities of the network for the boundedness of the mean-square error over time. We show that, in addition to Shannon capacity, anytime capacity plays a critical role in characterizing the impact of the network’s communication capability on the inference accuracy. This thesis deepens the understanding of decentralized inference in complex networked systems; uncovers connections among estimation, information, and control theories; and provides guidelines for designing decentralized inference algorithms and network operation strategies in applications such as NLN and IoT."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Decentralized Inference and its Application to Network Localization and Navigation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Win, Moe Z."],"dc:contributor.department":["Massachusetts Institute of Technology. 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The objectives of the thesis include: (i) designing message encoding strategies that maximize inference accuracy; (ii) establishing connections between information- and estimation-theoretical quantities; and (iii) characterizing the impact of the sensing and communication capabilities of the network on the inference accuracy. First, we investigate a system of two nodes connected via a Gaussian channel. For such a system, we design a real-time strategy for generating the encoded messages exchanged between the nodes and derive conditions under which such a strategy provides optimal inference accuracy. Building on an information-theoretic perspective of Kalman–Bucy filtering in centralized settings, we derive a relationship between Shannon information and Fisher information for decentralized inference. Then, based on results for two-node systems, we characterize the behavior of decentralized inference error in multi-node networks with general channel models. We establish both necessary and sufficient conditions on the sensing and communication capabilities of the network for the boundedness of the mean-square error over time. We show that, in addition to Shannon capacity, anytime capacity plays a critical role in characterizing the impact of the network’s communication capability on the inference accuracy. 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