Task allocation in IoV-based crowdsensing combing clustering and CMAB - Xinxin FENG; Danying GUO; Zefeng LIU; Haifeng ZHENG (物联网学报)
The crowdsening network based on Internet of vehicles (IoV) users has the advantages of extensive node coverage, complete and timely data.A major difficulty in the realization of this technology lies in how to fully mine and use the information of connected vehicular users (such as the user's geographic location, etc.) to select appropriate perception task participants, so as to carry out reasonable task assignments, thereby improving the completion quality of perception tasks and task publisher’s benefits.To solve the above problems, a task allocation method combining the trajectory features and the combinatorial multi-armed bandits (CMAB) algorithm was proposed.Firstly, users were clustered based on the similarity of their historical driving trajectories.Then, the CMAB model was adopted so that the trajectory clustering information could be used as the basis for deciding the optimal worker combination.Finally, the proposed algorithm was verified using the real taxi-trajectory dataset.The experimental results show that the task assignment algorithm considering the trajectory feature information has a higher accuracy and higher profit.At the same time, the selected workers have a high completion quality for tasks at the same location, and can effectively improve the quality of perceived data and the benefits of task publishers, which is suitable for practical application scenarios.