[Paper Review] Crowdsensing Game with Demand Uncertainties: A Deep Reinforcement Learning Approach

Yufeng Zhan, Yuanqing Xia+3 more|arXiv (Cornell University)|Dec 8, 2018

Mobile Crowdsensing and Crowdsourcing34 references4 citations

TL;DR

This paper proposes a deep reinforcement learning (DRL)-based dynamic incentive mechanism for mobile crowdsensing under demand uncertainties and private information constraints. By modeling the sensing platform (SP) and mobile users (MUs) as a Stackelberg game, it proves the existence and uniqueness of the Stackelberg Equilibrium (SE) and enables the SP to learn optimal pricing strategies via DRL without prior knowledge of MU private information, significantly improving system performance under uncertainty.

ABSTRACT

Currently, explosive increase of smartphones with powerful built-in sensors such as GPS, accelerometers, gyroscopes and cameras has made the design of crowdsensing applications possible, which create a new interface between human beings and life environment. Until now, various mobile crowdsensing applications have been designed, where the crowdsourcers can employ mobile users (MUs) to complete the required sensing tasks. In this paper, emerging learning-based techniques are leveraged to address crowdsensing game with demand uncertainties and private information protection of MUs. Firstly, a novel economic model for mobile crowdsensing is designed, which takes MUs' resources constraints and demand uncertainties into consideration. Secondly, an incentive mechanism based on Stackelberg game is provided, where the sensing-platform (SP) is the leader and the MUs are the followers. Then, the existence and uniqueness of the Stackelberg Equilibrium (SE) is proven and the procedure for computing the SE is given. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience without any prior knowledge about MUs' information. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach.

Motivation & Objective

• To address the challenge of designing incentive mechanisms in mobile crowdsensing systems where mobile users (MUs) face uncertain resource demands and limited device resources.
• To model the interaction between the sensing platform (SP) and MUs as a two-stage Stackelberg game, accounting for MU resource constraints and demand uncertainties.
• To prove the existence and uniqueness of the Stackelberg Equilibrium (SE) in the static game setting, enabling optimal pricing and resource allocation strategies.
• To develop a dynamic incentive mechanism (DIM) using deep reinforcement learning (DRL) that allows the SP to learn optimal pricing without prior knowledge of MU private information.
• To evaluate the performance of the proposed mechanism under various demand uncertainty levels and resource constraints through numerical simulations.

Proposed method

• Formulates a two-stage Stackelberg game where the SP acts as the leader setting pricing policies, and MUs as followers optimizing sensing effort based on price, resource constraints, and demand uncertainty.
• Derives analytical expressions for the Stackelberg Equilibrium (SE), proving its existence and uniqueness under the proposed economic model.
• Models the dynamic MCS game as a Markov Decision Process (MDP), enabling the SP to learn optimal pricing strategies through deep reinforcement learning (DRL) from game experience.
• Employs DRL algorithms to allow the SP to adaptively learn optimal pricing policies without requiring knowledge of MU private information such as cost or demand parameters.
• Introduces a dynamic incentive mechanism (DIM) that enables the SP to maximize payoff while preserving MU privacy through experience-based learning.
• Uses numerical simulations to validate the theoretical analysis and evaluate system performance under varying demand uncertainty levels and MU characteristics.

Experimental results

Research questions

• RQ1 How can an incentive mechanism be designed to effectively manage demand uncertainties and resource constraints in mobile crowdsensing systems?
• RQ2 What conditions ensure the existence and uniqueness of the Stackelberg Equilibrium (SE) in a crowdsensing game with uncertain MU demand and limited resources?
• RQ3 Can a dynamic incentive mechanism be developed that enables the sensing platform to learn optimal pricing strategies without access to private information of mobile users?
• RQ4 How do demand uncertainties and MU cost/resource parameters affect the performance and payoff of the sensing platform in a crowdsensing system?
• RQ5 To what extent does the DRL-based dynamic incentive mechanism improve system performance compared to static or information-dependent approaches?

Key findings

• The Stackelberg Equilibrium (SE) exists and is unique in the proposed static crowdsensing game, enabling the SP to determine optimal pricing and MUs to choose optimal sensing effort under uncertainty.
• The SP's optimal pricing strategy increases with higher MU cost ($c_n$) and higher utility gain ($\lambda$), reflecting market-driven incentives.
• When MU demand uncertainty ($\overline{\xi_n}$) increases, the SP must raise prices and MUs reduce their resource allocation to the SP, leading to a decrease in the SP’s payoff.
• MUs with lower cost ($c_n$) and lower demand intensity ($\delta_n$) are more readily recruited by the SP, especially at lower prices, due to lower opportunity cost.
• The DRL-based dynamic incentive mechanism enables the SP to learn optimal pricing strategies without prior knowledge of MU private information, preserving privacy while maintaining system performance.
• Simulation results confirm that demand uncertainties significantly impact system performance, with higher uncertainty leading to reduced SP payoff and increased pricing requirements.