To stimulate large-scale users to participate in the big data construction of IoT (internet of things), auction mechanisms based on game theory are used to select participants and calculate the corresponding reward in the process of crowdsensing data collection from IoT. In online auctions, bidders bid many times and increase their bid price. All the bidders want to maximize their utility in auctions. An effective incentive mechanism can maximize social welfare in online auctions. It is complicated for auction platforms to calculate social welfare and the utility of each bidder’s bidding items in online auctions. In this paper, a transaction trade-off utility incentive mechanism is introduced. Based on the transaction trade-off utility incentive mechanism, it can make the forecasting process consistent with bidding behaviors. Furthermore, an end-price dynamic forecasting agent is proposed for predicting end prices of online auctions. The agent develops a novel trade-off methodology for classifying online auctions by using the transaction trade-off utility function to measure the distance of auction items in KNN. Then, it predicts the end prices of online auctions by regression. The experimental results demonstrate that an online auction process considering the transaction utility is more consistent with the behaviors of bidders, and the proposed prediction algorithm can obtain higher prediction accuracy.

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预测物联网在线拍卖最终价格的交易权衡效用函数方法

为了刺激大规模用户参与物联网（物联网）的大数据构建，在基于物联网的数据采集过程中，采用基于博弈论的拍卖机制来选择参与者并计算相应的奖励。在在线拍卖中，投标人多次投标并提高其投标价格。所有投标人都希望在拍卖中发挥最大作用。有效的激励机制可以使在线拍卖中的社会福利最大化。拍卖平台计算在线拍卖中的社会福利和每个投标人的投标项目的实用性很复杂。本文介绍了交易权衡效用激励机制。基于交易权衡效用激励机制，可以使预测过程与投标行为保持一致。此外，提出了一种最终价格动态预测代理来预测在线拍卖的最终价格。该代理开发了一种新颖的权衡方法，通过使用交易权衡实用程序功能来度量KNN中拍卖项目的距离，从而对在线拍卖进行分类。然后，它通过回归预测在线拍卖的最终价格。实验结果表明，考虑交易效用的在线拍卖过程与投标人的行为更加一致，所提出的预测算法可以获得较高的预测精度。它通过回归预测在线拍卖的最终价格。实验结果表明，考虑交易效用的在线拍卖过程与投标人的行为更加一致，所提出的预测算法可以获得较高的预测精度。它通过回归预测在线拍卖的最终价格。实验结果表明，考虑交易效用的在线拍卖过程与投标人的行为更加一致，所提出的预测算法可以获得较高的预测精度。