Efficient and privacy-preserving worker selection in mobile crowdsensing
Date
2022-05
Authors
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Publisher
University of New Brunswick
Abstract
The rapid growth of the Internet of Things (IoTs) has enabled a new sensing paradigm, called Mobile Crowdsensing (MCS). A crowd of mobile participants, namely workers, are selected by the MCS platform to outsource their real-time sensing data for specific tasks, such as location recommendation, air quality monitoring, and traffic monitoring. Work selection is the process of allocating qualified workers to suitable MCS tasks. In MCS, workers’ reliability and their sensing data quality play significant roles in the service quality. Therefore, worker selection is always one of the most fundamental problems in MCS applications.
Despite the considerable potential and extensive development, there are still several challenges and issues in MCS services which cannot be ignored. i) In worker selection, it is inevitable for the workers to share some of their personal sensitive information, which may be exploited by hostile MCS platform for malicious activities. Therefore, privacy preservation for workers’ sensitive information is a major concern in MCS platforms. ii) Evaluating workers’ trustability or credibility is one of the most essential issues that the MCS platform needs to solve. But these attributes were often neglected in previous literature. iii) Worker selection is a dynamic process where the workers can continuously arrive at/leave the platform. However, most of the existing studies only focus on selecting workers statically. iv) In MCS tasks, the workers are always heterogeneous with different computational resources. Therefore, the MCS platform needs to select qualified workers in terms of their various computing characteristics. v) Worker’s real-time spatial-temporal information plays a vital role in worker selection, and should be paid more attention in designing real-world MCS applications.
In this thesis, we focus on efficient and privacy-preserving worker selection in MCS and propose several schemes for addressing the above challenges. Specifically, the main contributions are as follows. i) We proposed a privacy-preserving worker selection scheme based on probabilistic skyline computation for calculating workers’ trustability based on their historical reviews. ii) We proposed a privacy-preserving dynamic worker selection scheme based on probabilistic skyline query over sliding windows, which can continuously and dynamically select qualified workers. iii) We proposed a novel application scenario called Federated MCS by integrating the concept of Federated Learning with MCS. The proposed scheme can select qualified workers based on the group skyline technique and aggregate local model updates for training the global model. iv) We conducted a series of studies on worker selection regarding spatial-temporal matching and evaluation. The proposed privacy-preserving schemes can select qualified workers in terms of their real-time spatial-temporal information.
The research findings and experimental results in this thesis should be useful for selecting qualified workers effectively and securely in MCS applications.