2026-09-19T09:07:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1451852022-08-30T03:21:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Win, Moe Z.
Liu, Zhenyu
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2022-08-29T16:38:53Z
2022-08-29T16:38:53Z
2022-05
2022-06-09T16:14:37.329Z
https://hdl.handle.net/1721.1/145185
https://orcid.org/0000-0002-6581-2849
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.
Ph.D.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Decentralized Inference and its Application to Network Localization and Navigation
Thesis
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Doctoral
Doctor of Philosophy
Publication
unknown
unknown
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<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>
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