Multitask-Oriented Collaborative Crowdsensing Based on Reinforcement Learning and Blockchain for Intelligent Transportation System
Mengge Li, Miao Ma, Liang Wang, Bo Yang, Tao Wang, Jinqiu Sun
Abstract
With the rapid development of smart cities, vehicles equipped with various sensors can effectively sense traffic, thus forming a crowdsensing paradigm for the intelligent transportation system (ITS). Although mobile crowdsensing in ITS has broad application advantages, it still faces many challenges, such as single point of failure, inefficient independent task allocation, and the inability to deal with safety emergency tasks in time. To handle the abovementioned issues, we establish a decentralized ITS architecture based on blockchain and propose the concurrent tasks assignment problem proved to be NP-hard and safety emergency tasks assignment problem. Then, we propose reinforcement learning-based concurrent tasks and the safety emergency tasks assignment method, which can maximize the utility of concurrent tasks based on satisfying the requirements of safety emergency tasks. Simulation results demonstrate the effectiveness of the proposed methods.