基于Tangle网络的群智感知用户可信激励方法

Crowd Sensing User Credibility Incentive Method Based on Tangle Network

随着群智感知技术的广泛应用,用户参与度成为影响群智感知技术发展的关键问题,提出一种基于Tangle网络的群智感知用户可信激励方法.首先,基于Tangle网络构建群智感知网络模型,通过去中心化的Tangle网络来克服群智感知平台面临可信第三方参与的安全隐患,制定Tangle网络的可信度标准.然后,通过可信度来衡量用户权重,借此来评估交易的可信性,将感知任务分配给更可信的用户.最后,对用户上传的感知数据使用蒙特卡洛最大期望算法量化用户的工作量,支付相应报酬.通过基于真实数据集的仿真实验表明,所提方法能有效激励用户参与感知任务,同RSFP方法和CSII方法相比,所提方法在用户参与度上分别提升了0.379和0.067.

With the widespread application of crowd sensing technology,user participation has become a key issue affecting the development of crowd sensing technology.A trusted incentive method for crowd sensing users based on Tangle network is proposed.First,based on Tangle network to build a crowd sensing network model,through the decentralized Tangle network to overcome the security risks faced by trusted third parties involved in the crowd sensing platform,and formulate the Tangle network credibility standard.Then,the user’s weight is measured by the credibility index,thereby evaluating the credibility of the transaction,and assigning the perception task to more credible users.Finally,the Monte Carlo maximum expectation algorithm is used to quantify the user’s workload and pay the corresponding remuneration to the user’s uploaded perception data.Simulation experiments based on real data sets show that the proposed method can effectively motivate users to participate in perception tasks.Compared with the RSFP method and CSII method,the proposed method respectively improves user participation by 0.379 and 0.067.