A Transaction Trade-Off Utility Function Approach for Predicting the End-Price of Online Auctions in IoT

Abstract

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.