Utility-Aware Legitimacy Detection of Mobile Crowdsensing Tasks via Knowledge-Based Self Organizing Feature Map

In Mobile Crowdsensing (MCS), fake tasks can drain significant amount of resources. This paper proposes a new methodology to determine a proper time window for the training dataset and the impact of the accuracy of task legitimacy detection on the MCS campaign performance. To reach the desired performance, the task legitimacy detection is utilized in such a way that while legitimate tasks are kept, the fake tasks are eliminated as much as possible in the MCS platform through machine learning (ML) prediction. The proposed methodology is evaluated for legitimacy detection under multiple ML methods. Moreover, a knowledge-based fake task detection technique with effective feature selection is formulated to ensure fake tasks are filtered at the MCS servers. Detection accuracy is improved by using shorter time frame in training and longer time frame in prediction. The overall performance improvement based on profit, cost, legitimate tasks loss ratio, and fake tasks elimination ratio has been achieved under three different sizes of training datasets to verify the efficiency of the proposed methodology. Moreover, Prior Knowledge Input with Self-Organizing Feature Map outperforms the conventional legitimacy detection by 5.48%, 12.11% and 58.05% in terms of test accuracy, profit and cost under the small dataset, respectively.