Chen Xiao, Liu Min, Zhou Yaqin, Li Zhongcheng, Chen Shuang, He Xiangnan
Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing 100190, China.
School of Computer and Control Engineering, University of Chinese Academy of Sciences, No. 19 A Yuquan Road, Shijingshan District, Beijing 100049, China.
Sensors (Basel). 2017 Jan 1;17(1):79. doi: 10.3390/s17010079.
We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and truthful incentive mechanism to encourage users to participate. To address the challenge, we propose a novel truthful online auction mechanism that can efficiently learn to make irreversible online decisions on winner selections for new MCS systems without requiring previous knowledge of users. Moreover, we theoretically prove that our incentive possesses truthfulness, individual rationality and computational efficiency. Extensive simulation results under both real and synthetic traces demonstrate that our incentive mechanism can reduce the payment of the platform, increase the utility of the platform and social welfare.
我们研究新兴的移动人群感知（MCS）系统，在该系统中，新的基于云的平台会依次向服务质量不确定的动态到达用户分配同质感知任务。鉴于人类本质上是自私的，设计一种高效且真实的激励机制以鼓励用户参与既至关重要又具有挑战性。为应对这一挑战，我们提出了一种新颖的真实在线拍卖机制，该机制能够在无需用户先验知识的情况下，高效地学习为新的MCS系统做出关于获胜者选择的不可撤销在线决策。此外，我们从理论上证明了我们的激励机制具有真实性、个体理性和计算效率。在真实和合成轨迹下的大量仿真结果表明，我们的激励机制可以降低平台的支付成本，提高平台的效用和社会福利。
