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

Overview:

This project is collaborated between California State University Long Beach (CSULB) Economics Department and California State University Dominguez Hills (CSUDH) Computer Science Department. The collaboration is to build research capacity and develop interdisciplinary partnerships between these two minority-serving institutions (MSIs).

The overarching goal of the project is to create a truthful and optimal resource allocation framework for emerging base station-less sensor networks (BSNs). As BSNs are deployed in challenging environments (e.g., underwater exploration), there is no data-collecting base station available in the BSN. The paramount task of the BSN is to preserve large amounts of generated data inside the BSN before uploading opportunities become available. Previous research designed a sequence of cooperative data preservation techniques based on classic network flows (e.g., maximum (weighted) flow and minimum cost flow). 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 in our unique BSN model gives rise to new challenge that calls for integrated study of game theory and network flows in the same problem space.

This project is supported by NSF Grant 2131309.

Key Words: Wireless Sensor Networks, Game Theory, Network Flows, CloudAccess

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Game Theory and Multi-agent Reinformce Learning Base Station-less Sensor Networks Network Games

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