Correlated-Ambiguity AoI Contracts: Distributionally Robust Menus for Freshness-Sensitive Crowdsensing

Most MCS incentive work either optimizes participation or task allocation; Zhou, Niyato, and Yuen (2025) elevate AoI as a core objective and derive cycle–price menus. Separately, Zhan et al. (2025) handle uncertainty via distributionally robust optimization (DRO), and Wang et al. (2024) relax the independence assumption by addressing correlated resource contributions under strong asymmetry. This idea unifies these strands: design AoI-aware contracts when (i) agent types and outputs are correlated, (ii) the principal only knows an ambiguity set of type/outcome distributions, and (iii) sampling cycles must guarantee freshness SLAs. Methodologically, we propose moment-constrained DRO over correlated type distributions and a contract structure that adapts sampling cycles and payments as estimated correlation structure shifts. We generalize the pair-switching allocation algorithm in Zhou et al. (2025) to a DRO setting with correlation and prove convergence to a robust allocation given IR/IC. The novelty lies in treating correlation and ambiguity as central rather than peripheral, which is more realistic for urban mobility or environmental sensing where agents’ contexts co-move. Impact: robust AoI performance under distributional shifts, reduced over-sampling costs, and better resilience to information asymmetry—all in one tractable framework.

References:

1. Distributionally Robust Contract Theory for Edge AIGC Services in Teleoperation. Zijun Zhan, Yaxian Dong, D. M. Doe, Yuqing Hu, Shuai Li, Shaohua Cao, Lei Fan, Z. Han (2025). IEEE Transactions on Mobile Computing.
2. Age-of-Information-Driven Task Allocation for Periodic Updating Crowdsensing: A Contract Theory-Based Approach. Xuying Zhou, Dusist Niyato, Chau Yuen (2025). IEEE Internet of Things Journal.
3. Incentivizing Federated Learning with Contract Theory Under Strong Information Asymmetry. Siyang Wang, Wenchao Xia, Haitao Zhao, Yiyang Ni, Chun Zhu, Hongbo Zhu (2024). IEEE Wireless Communications and Networking Conference.