Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems

The evolution of the Internet of Things-enabled Maritime Transportation Systems (IoT-MTS) provides a sturdy data cornerstone for efficient maritime traffic scheduling and management. However, due to task heterogeneity and the limited computing power of individual vessels or stakeholders, although third-party clouds could provide powerful computing support for MTS, directly aggregating data from vessels to the cloud may cause privacy leakage and security concerns. Crowdsourcing as a newly distributed problem-solving paradigm could provide new solutions for conducting maritime big data computing, deep learning and sensing tasks by leveraging the crowd intelligence and computing power of vessels, but strong incentives are required to stimulate vessels to participate because of their selfishness and rationality. Nevertheless, existing incentives rarely consider the security problems caused by the man-in-the-middle attacks, honest-but-curious platform attacks and inference attacks simultaneously, and ignore the redundant winners and multi-attribute characteristics of participants. Toward this end, this paper proposes a tensor-based secure truthful incentive for IoT-MTS dubbed CrowdTensor to maximize the social welfare by eliminating redundant winners and meanwhile guaranteeing the desired economic properties, where the multi-attribute features and the complex association relationships of crowdsourcing systems are characterized by utilizing the tensor tool. A two-phase bid-preserving mechanism based on the cryptographic hash function and digital signature is introduced against malicious attacks. Both the rigorous theoretical analysis and extensive experimental results show that CrowdTensor outperforms other compared incentives and the desired properties can be achieved simultaneously.