Energy-Efficient Data Collection and Resource Allocation for UoI-Aware Mobile Crowdsensing in IoV

Mobile Crowdsensing (MCS) is a promising paradigm where embedded sensor are exploited for collecting and sharing environmental data. In IoV, participating vehicles sense the environment, collect data and transmit the data to the edge server for processing to provide real-time services. However, real-time services not only pose energy challenges for massive data transmission and analysis, but also demand dynamic multitimeslot optimization for MCS systems. Additionally, sensing data often require continuous updates to prevent the provision of obsolete services. To address this, we introduce the concept of Urgency of Information (UoI) to characterize the freshness of sensing data. Diverging from the linear growth trend of Age of Information (AoI), UoI enables dynamic adjustment of the decay rate of data freshness based on traffic complexity. In this article, we propose a dynamic multitimeslot MCS system for IoV, under constraints of UoI, which intelligently leverages the spatial correlation of perception to update data and minimizes network energy consumption while ensuring compliance with UoI constraints. Then we propose a joint data collection and resource allocation (JDCRA) algorithm to obtain the solution based on convex optimization. To the best of our knowledge, this is the first work to jointly optimize multiroad data collection and resource allocation, taking into account UoI metrics. We evaluate JDCRA by experiments on SUMO in real-world scenarios. Experimental results show that JDCRA outperforms state-of-the-art methods in terms of energy consumption and UoI violation probability, and obtains solutions that consumes only 7.43% more energy than the optimal solution in polynomial complexity.