Truthful and Optimal Data Preservation

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

Personnel:

Faculty: Yutian Chen (PI) and Bin Tang (Co-PI)

Students supported through this grant: Rachel Varghese, Justin Gamoras, Grace Huang (CSULB Economics Undergraduate Students), Jose Chavez, Broian Rios (CSULB Economics Graduate Students), Giovanni Rivera, Jennifer Ly (CSUDH CS Undergraduate Students), Yuning Yu, Shangli Hsu (CSUDH CS Graduate Students)

News:

Attended 2024 MSI Grants PI Meeting and gave a talk.
Congratulations to Giovanni, who will pursue his Ph.D. degree at UCR Computer Science Department.
Congratulations to Grace, who will pursue her M.A. degree in public policy at Georgetown University.
Congratulations to Justin, who will pursue his graduate MBA degree at UCI School of Business.
Congratulations to Justin, who are one of the First Place Winners at 36th Annual CSULB Student Research Competition! His research presentation is titled "Application of Economic Game Theory and Nash-Q Reinforcement Learning in Studying Data Preservation Sensor Nodes".
Congratulations to Grace, who gave a research presentation titled "A Correlated Equilibrium Q-Learning for Data Preservation in Base Station-less Sensor Network" at 98th Annual Western Economic Association International (WEAI)!

Activity Schedules:

2021 Schedule

2022 Schedule

2024 Schedule

Publications:

A Truthful and Efficient Auction Mechanism for Data Preservation in Base Station-less Sensor Networks [pdf]
Ryan Steubs, Yutian Chen, and Bin Tang
IEEE International Conference on Communications (ICC 2025).
Budget-Constrained Traveling Salesman Problem: a Cooperative Multi-Agent Reinforcement Learning Approach
King To Vincent Mak, Christopher Gonzalez, Zari Justine Magnaye, Jessica Gonzalez, Yutian Chen, Bin Tang
Proceedings of the IEEE International Conference on Sensing, Communication, and Networking (SECON 2024).
Revisiting Data Collection in Robotic Sensor Networks: A Budget-Constrained Traveling Salesman Perspective [pdf]
Soham Patil, Yutian Chen, and Bin Tang
Proceedings of the IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2024).
Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach [pdf]
Yuning Yu, Shangli Hsu, Andre Chen, Yutian Chen, Bin Tang
ACM Transactions on Sensor Networks, Volume 20, Issue 1, No. 5, pp 1-40, 2023
On the Performance of Nash Equilibria for Data Preservation in Base Station-less Sensor Networks [pdf]
Giovanni Rivera, Yutian Chen, and Bin Tang
Proceedings of the IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2023).
Data-VCG: A Data Preservation Game for Base Station-less Sensor Networks with Performance Guarantee [pdf]
Jennifer Ly,Yutian Chen, and Bin Tang
Proceedings of the 3rd International Workshop on Time-Sensitive and Deterministic Networking (TENSOR), IFIP Networking 2023.
Nash Equilibria of Data Preservation in Base Station-less Sensor Networks [pdf]
Giovanni Rivera, Yutian Chen, and Bin Tang
Proceedings of the Third Computer Science Conference for CSU Undergraduates (CSCSU 2023).
Voluntary Data Preservation Mechanism in Base Station-less Sensor Networks [pdf]
Yutian Chen, Jennifer Ly, Bin Tang
Proceedings of the 12th EAI International Conference on Game Theory for Networks (GameNets 2022).
Service Function Chain Placement in Cloud Data Center Networks: a Cooperative Multi-Agent Reinforcement Learning Approach [pdf]
Lynn Gao, Yutian Chen, Bin Tang
Proceedings of the 11th EAI International Conference on Game Theory for Networks (GameNets 2021).
Data Preservation in Base Station-less Sensor Networks: A Game Theoretic Approach [pdf]
Yutian Chen and Bin Tang
Proceedings of the 6th EAI International Conference on Game Theory for Networks (GameNets 2016).

Related Websites:

Computing Alliance of Hispanic-Serving Institutions

For Students:

Why Undergraduate Research Matters?
How to Read, Write, and Present a Rearch Paper [1], [2]
How to Give a Technical Presentation [1], [2], [3]
Advice on Research and Writing
How to Be a Good Graduate Student