A deep learning-based reverse auction mechanism for semantic communication in IoV crowdsensing services

Abstract

Internet of Vehicles (IoV) crowdsensing is an efficient approach to vehicle data collection in which vehicle service providers (VSPs) recruit users to participate in IoV crowdsensing tasks to obtain large amounts of vehicle data at low costs. However, the massive amount of vehicular data imposes significant challenges to the limited storage and communication resources, thereby hindering the efficient acquisition of the required information. To address these challenges, this paper proposes multiple effective strategies. To address the challenge of large data volumes, we employ semantic communication techniques to effectively compress the collected data for efficient storage and transmission. Furthermore, we define a semantic information value function to quantify the value of vehicular semantic information, and to address the problem of slow data transmission, we propose shunting offloading data to edge servers to improve the transmission efficiency. Building on this foundation, we design a deep learning-based reverse auction mechanism, SVRANet, to effectively allocate crowdsensing tasks and communication resources. SVRANet leverages self-attention mechanisms to uncover latent interactions within the information, thereby enhancing the model’s ability to allocate resources more efficiently. The experimental results demonstrate that SVRANet performs excellently, achieving high utility and social welfare while guaranteeing incentive compatibility, individual rationality, and budget feasibility.