Communication-Efficient Participant Selection for Crowdsensing in Internet of Vehicles With Heterogeneous Sensing, Communication, and Computing Resources

Environment-dependent applications, such as high-definition maps, that rely on environmental information as input data deserve further research to reduce the amount of transmission data. The cooperation of crowndsensing and local preprocessing is a potential solution to sense and preprocess the environmental data in real-time. However, the sensing and computing capabilities of different participants are heterogeneous, which may lead to significant differences in the total amount of transmission data (TATD). Therefore, a novel communication-efficient participant selection strategy is proposed, incorporating heterogeneous sensing, communication, and computing resources. The matching process between target sensing subregions and participants, along with preprocessing task allocation, is jointly optimized to minimize the TATD. A heuristic algorithm with low complexity is developed to solve the optimization problem. Performance evaluations show that the proposed mechanism can reduce the TATD by up to 62.8% compared with benchmark mechanisms.