Decentralized Online Learning in Task Assignment Games for Mobile
Crowdsensing
- URL: http://arxiv.org/abs/2309.10594v1
- Date: Tue, 19 Sep 2023 13:07:15 GMT
- Title: Decentralized Online Learning in Task Assignment Games for Mobile
Crowdsensing
- Authors: Bernd Simon, Andrea Ortiz, Walid Saad, Anja Klein
- Abstract summary: 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.
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.
- Score: 55.07662765269297
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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%.
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