群智感知中基于反拍卖模型的众包激励方法  

Crowdsourcing incentive method based on reverse auction model in crowd sensing

作　　者：朱旋[1] 杨麦顺[1] 安健[1,2] 向乐乐[1] 杨蔷薇[1] 

机构地区：[1]西安交通大学电子与信息工程学院,西安710049 [2]陕西省计算机网络重点实验室(西安交通大学),西安710049

出　　处：《计算机应用》2016年第7期2038-2045,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61502380;61472316);陕西省自然科学基金资助项目(2014JQ8322)~~

摘　　要：激励是实现群智感知(CS)众包服务的主要方法,针对现有方法在服务过程中没有充分考虑节点参与数量和恶意竞争对群智感知带来的影响,提出一种基于反拍卖模型的激励(RVA-IM)方法。首先,研究众包的激励机制,结合反拍卖与Vickrey拍卖思想,构建面向任务覆盖的反拍卖模型;其次,对模型中涉及的任务覆盖、反拍卖选择和奖励实施等关键技术问题进行深入分析与研究;最后,从计算有效、个人理性、预算平衡、真实性和诚实性五个方面分析RVA-IM激励方法的有效性。实验结果表明,与IMC-SS和MSensing激励方法相比,RVA-IM在有效性和可行性方面均有较好的表现,能够解决现有方法中的恶意竞争问题,并能够平均提升众包服务完成率约21%。Intention is the main method of crowdsourcing service in Crowd Sensing（ CS）, in view of the existing methods in the process of service without fully considering the effects on CS which are from the number of participants and malicious competition, a kind of Incentive Mechanism based on Reverse Vickrey Auction model（ RVA-IM） method was proposed.Firstly, incentive mechanisms of crowdsourcing were studied in this paper, in combination with reverse auction and Vickrey auction, a reverse auction model oriented to task covering was built. Secondly, the in-depth analysis and research on the key technical problems involved in the model were conducted, such as task covering, reverse auction selection and reward implementation. Finally, the effectiveness of RVA-IM method was analyzed in five ways： computational efficiency, individual rationality, budget-balance, truthfulness and honesty. The simulation results show that, compared with IMC-SS（ Incentive Mechanism for Crowdsourcing in the Single-requester Single-bid（ SS）-model） and MSensing（ Myerson Sensing） method,RVA-IM method is more effective and feasible. It can solve the problem of malicious competition in the existing methods, and improves the average rate of service completion by 21%.

关 键 词：群智感知 众包 激励机制 反拍卖 Vickrey拍卖 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]