Collaborative Research: CISE-MSI: RCBP-RF: CNS: Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The goal of the project is to create a truthful and optimal resource allocation framework for emerging base station-less sensor networks. As such networks are deployed in challenging environments without data-collecting base station (e.g., underwater exploration), the paramount task is to preserve large amounts of generated data inside the networks before uploading opportunities become available. In a distributed setting and under different control, however, the sensor nodes with limited resources (i.e., energy power and storage spaces) could behave selfishly in order to save their own resources and maximize their own benefits. The tension between node-centric selfishness and data-centric data preservation gives rise to new challenge that calls for integrated study of game theory (the science of strategic interaction), and network flows (that studies how to move network objects efficiently and effectively). This project deploys following research thrusts. First, selfish data preservation is analyzed in terms of Nash equilibrium, price of anarchy, price of stability, and Shapley scheme. Second, mechanism design approach is used to identify the limitations of existing methodology and propose new incentive mechanisms. Third, a suite of new data preservation and data aggregation games are designed to incorporate network-specific features such as data values and data spatial correlations. All the research thrusts intertwine game-theoretic and network flow technique to achieve the truthful and optimal data preservation. Finally, the designed techniques will be evaluated by simulations, existing network flow and game theory software, as well as CloudBank.By preserving large amounts of data of the physical world otherwise inaccessible, base station-less sensor networks provide a comprehensive view of scientific frontiers including scientific exploration, disaster warning and climate change, thus benefiting the society. This project is collaborated between California State University Long Beach Economics Department and California State University Dominguez Hills Computer Science Department. This cross-institutional and interdisciplinary collaboration provides an integrative research and education experience for students. The educational goal is not just recruiting and working with a few best students but inspiring and educating as many underrepresented students as possible at both institutions. Planned activities include student campus visit and poster exhibition, intra-campus collaboration, conference presentation and participation, curriculum update and development, and integrating with existing minority-serving programs at both institutions.Details of the project can be found at https://web.csulb.edu/~ychen7/bsn_gametheory/. This website will be updated regularly as the research progresses and will be maintained for public view for five to ten years.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.