Intelligent Distributed Estimation Architectures

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Smart devices and everyday items equipped with a plurality of sensors - already today - generate massive amounts of sensor data that are to be filtered, analyzed, monitored, and processed. To overcome this challenge, abundant sensor data are typically partitioned into subsets and fed into multiple filters running in parallel. Such an approach, which can be understood as the bottom-up approach, relies on a design and run of multiple independent local filters, collecting the estimation results, and fusing them into a global estimate. As each local filter is designed separately and is not aware of the global estimation goal, the bottom-up approach cannot generally result in an optimal and computationally efficient global estimate.This project views the solution to the large-scale distributed estimation problem from a different perspective by primarily focusing on the desired global estimation quality. The proposed top-down approach starts from the requirements on the global estimation quality and designs the structure and topology of local filters to meet the requirements while the computational complexity is kept as low as possible. The top-down approach is especially important considering the recent developments in distributed computing architectures which give an enormous flexibility and variability to the users and designers to achieve different and possibly time-varying estimation goals.The project will be a novel attempt due to its proactive approach to change the estimation architecture with respect to a given global estimation aim. With such an approach, it is possible to suggest an adaptation to the architecture without doing extensive trials with a given architecture. For this aim, the project will analyze the building blocks of the estimation architecture; that is the data flow, network topology, fusion strategy, and filter parameters with a focus on their individual and cooperative impact on the global objective. The processing units within the architecture also feature monitoring and fault detection functionalities in order to adapt the architecture to changing operational conditions and requirements. We believe this to be a timely consideration in the age of the Internet of Things.