Optimum Worker Sampling in Crowdsensing with Multiple Areas

In mobile crowdsensing (MCS), sensing data measured by mobile devices equipped with sensing capabilities are collected from various workers to estimate true values. Since the widespread use of smartphones, MCS has attracted particular attention because of its excellent sensing capabilities with low cost without sensing infrastructure. However, values measured by smartphones have errors, so MCS service providers need to control errors. Moreover, MCS cannot totally avoid attackers who try to maximize the errors in estimated values of MCS, e.g., data poisoning attack (DPA). Although existing studies related these issues assumed DPA of a single area, many MCSs seem to consist of multiple areas. In addition, although existing studies have assumed that sensing data is collected from all workers, it is necessary to provide incentives to workers under the budget constraints of the MCS, and it is expected that sensing data is actually collected only from workers sampled with a certain probability. Therefore, this paper proposes a method for setting the optimal number of workers to sample in each area when collecting sensing data from workers in multiple areas under the condition that the total number of sampled workers is fixed. Through the computer simulation, we confirm the effectiveness of the proposed method in improving the estimation accuracy in MCS with multiple areas.