Crowdsourcing is an effective tool to allocate tasks among workers to obtain a cumulative outcome. Algorithmic game theory is widely used as a powerful tool to ensure the service quality of a crowdsourcing campaign. By this paper, we consider a more general optimization objective for the budget-free crowdsourcer, profit maximization, where profit is defined as the difference between the benefit obtained by crowdsourcer and payments to workers. Based on the framework of random sampling and profit extraction, we proposed a strategy-proof profit-oriented mechanism for our problem, which also satisfies computational tractability and individual rationality and has a performance guarantee. We also extend the profit extract algorithm to the online case through a two-stage sampling. Also, we study the setting in which workers are not trusted, and untrustworthy workers would infer others’ true type. For untrustworthy workers, we introduce a differentially private mechanism, which also has desired properties. Finally, we will conduct numerical simulations to show the effectiveness of our proposed profit maximization mechanisms. By this work, we enrich the class of competitive auctions by considering a more general optimization objective and a more general demand valuation function.
To further improve the prediction accuracy of the critical frequency of the ionospheric F2 layer (foF2), we use the machine learning method (ML) to establish an explanatory dynamic model to predict foF2. Firstly, according to the ML modeling process, the three elements of establishing a prediction model of foF2 and four problems to be solved are determined, and the idea and concrete steps of model building are determined. Then the data collection is explained in detail, and according to the modeling process, foF2 dynamic change mapping and its parameters are determined in turn. Finally, the established model is compared with the International Reference Ionospheric model (IRI-2016) and the Asian Regional foF2 Model (ARFM) to verify the validity and reliability. The results show that compared with the IRI-URSI, IRI-CCIR, and ARFM models, the statistical average error of the established model decreased by 0.316 MHz, 0.132 MHz, and 0.007 MHz, respectively. Further, the statistical average relative root-mean-square error decreased by 9.62%, 4.05%, and 0.15%, respectively.
The ionospheric F2 layer is the essential layer in the propagation of high-frequency radio waves, and the peak electron density height of the ionospheric F2 layer (hmF2) is one of the important parameters. To improve the predicted accuracy of hmF2 for further improving the ability of HF skywave propagation prediction and communication frequency selection, we present an interpretable long-term prediction model of hmF2 using the statistical machine learning (SML) method. Taking Moscow station as an example, this method has been tested using the ionospheric observation data from August 2011 to October 2016. Only by inputting sunspot number, month, and universal time into the proposed model can the predicted value of hmF2 be obtained for the corresponding time. Finally, we compare the predicted results of the proposed model with those of the International Reference Ionospheric (IRI) model to verify its stability and reliability. The result shows that, compared with the IRI model, the predicted average statistical RMSE decreased by 5.20 km, and RRMSE decreased by 1.78%. This method is expected to provide ionospheric parameter prediction accuracy on a global scale.
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