# Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing

arXiv: https://arxiv.org/abs/2309.10594 · HTML: https://mlsanity.com/paper/2309.10594/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [Bandit Learning in Decentralized Matching Markets](https://mlsanity.com/paper/2012.07348/) (2020)
- [Regret, stability & fairness in matching markets with bandit learners](https://mlsanity.com/paper/2102.06246/) (2021)
- [Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions](https://mlsanity.com/paper/1910.09089/) (2019)
- [Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems](https://mlsanity.com/paper/2104.01527/) (2021)
- [Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions](https://mlsanity.com/paper/2106.13669/) (2021)
- [Distributed Time-Sensitive Task Selection in Mobile Crowdsensing](https://mlsanity.com/paper/1503.06007/) (2015)
- [Matching Theory for Future Wireless Networks: Fundamentals and Applications](https://mlsanity.com/paper/1410.6513/) (2014)
- [Exploiting Social Trust Assisted Reciprocity (STAR) towards Utility-Optimal Socially-aware Crowdsensing](https://mlsanity.com/paper/1508.05525/) (2015)
- [Task Allocation in Mobile Crowd Sensing: State of the Art and Future Opportunities](https://mlsanity.com/paper/1805.08418/) (2018)

## Built on by (most cited first, 1 of 1)

- [Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information](https://mlsanity.com/paper/2605.02705/) (2026)

## Abstract (arXiv)

The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP's and MUs' conflicting goals, and the uncertainty about the MUs' required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP's and MUs' individual goals while the MUs learn their efforts online. Our innovative "free-sensing" mechanism significantly improves the MU's learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs' and the MCSP's satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%.