The adoption of smart device technologies is steadily increasing. Most of the smart devices in use today have built-in sensors which measure motion, direction, and various environmental conditions. Sensors are able to provide raw data with different quality and accuracy. A large group of smart devices forms a mobile crowdsensing system which is capable of sensing, collecting and sharing the environmental data to perform large scale sensing jobs. This paper aims to study and design an incentive mechanism for a mobile crowdsensing system based on a one-leader multi-follower Stackelberg game. A platform provider, as proponent of the sensing job, will act as the leader, while the mobile users will act as the followers. The final goal is to devise an efficient mechanism able to motivate the smart device users to participate in the sensing activity. Different from existing approaches, we propose a centralized method where the platform provider can estimate users’ parameters very efficiently sending and receiving a few messages. We formulate the optimization problem on the platform provider side as a mixed integer nonlinear program with time constraints for each job and a budget constraint. Finally, a heuristic algorithm based on the derivative-free directional direct search method is designed to solve the platform optimization problem and achieve a close-to-optimal solution for the game. Results show that our Stackelberg game solution is much more scalable than the approach proposed in the work by other authors Zhan et al. (2018) as we can decrease the average number of messages by a factor between 53 to 80 and the average running time between 23 and 650 times. Furthermore, we compared our heuristic algorithm with BARON, a state of the art commercial tool for mixed integer global optimization, to solve the platform optimization problem. Results demonstrated that our proposed algorithm converges to a near-optimal solution much faster especially in large scale systems.

智能设备技术的采用正在稳步增加。今天使用的大多数智能设备都有内置传感器，可以测量运动、方向和各种环境条件。传感器能够提供不同质量和精度的原始数据。一大群智能设备形成了一个移动的群体感应系统，能够感应、收集和共享环境数据，以执行大规模的感应工作。本文旨在研究和设计一种基于单领导多追随者 Stackelberg 博弈的移动人群感知系统的激励机制。平台提供者作为感知工作的支持者将充当领导者，而移动用户将充当追随者。最终目标是设计一种能够激励智能设备用户参与传感活动的有效机制。与现有方法不同，我们提出了一种集中式方法，在这种方法中，平台提供商可以非常有效地发送和接收一些消息来估计用户的参数。我们将平台提供方的优化问题表述为一个混合整数非线性程序，每个工作都有时间约束和预算约束。最后，设计了一种基于无导数定向直接搜索方法的启发式算法来解决平台优化问题，实现博弈的接近最优解。结果表明，我们的 Stackelberg 游戏解决方案比其他作者 Zhan 等人在工作中提出的方法更具可扩展性。（2018）因为我们可以将平均消息数量减少 53 到 80 倍，平均运行时间减少 23 到 650 倍。此外，我们将启发式算法与最先进的混合整数全局优化商业工具 BARON 进行比较，以解决平台优化问题。结果表明，我们提出的算法收敛到接近最优解的速度要快得多，尤其是在大规模系统中。