A Truthful Double Auction Mechanism for Multi-Resource Allocation in Crowd Sensing Systems

Abstract

As a novel sensing paradigm, crowd sensing systems have gained great attention and been widely adopted in the environmental monitoring and calculation areas. In crowd sensing systems, mobile users provide their multiple resources to the requesters to execute tasks. Existing studies focus on the divisible task or one-to-one mapping for single resource allocation. However, this assumption does not hold for crowd sensing systems. Owing to the task attribute, some tasks cannot be divided into multiple parts to run on different devices. In addition, a high performance mobile device can execute multiple tasks simultaneously. We address the problem of multi-resource allocation in crowd sensing systems for the auction-based model considering many-to-one mapping for indivisible tasks, where many-to-one mapping allows one mobile device to provide multiple resources to execute one or more tasks. In this article, we study, for the first time to the best of our knowledge, a truthful mechanism that stimulates mobile users and requesters to declare their true values. We design a truthful double auction mechanism together with a payment scheme tailored to fit it that would help researchers understand how a truthful double auction mechanism can be designed. In addition, we prove that our proposed mechanism maintains budget-balance, individual rationality, and computational tractability. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. Experimental results demonstrate that our proposed mechanism has high computation efficiency and good performance.