Data quality optimized online task allocation method for mobile crowdsensing - Wei ZHANG; Zhuo LI; Xin CHEN (物联网学报)
Optimization of the perceived quality and the recruitment of user are two important issues of mobile crowdsensing.As the amount of data increases rapidly,perceived data becomes redundant,and perceived quality is at risk of decreasing.A mechanism of task assignment based on the perceptive quality optimization was proposed to improve the perceived quality under the condition of full coverage.The clustering algorithm was used to evaluate the truth value of the task and quantify the quality of the user data.Based on Thompson sampling algorithm and greedy algorithm,a user recruitment strategy was designed and implemented to optimize the perceived quality on the basis of ensuring the spatial coverage of the task.The performance of Thompson based user recruit (TSUR) algorithm was simulated and analyzed that compared with the existing algorithms of BBTA and basic user recruitment (BUR).Experiments show that in the same area,compared with bandit-based task assignment (BBTA) algorithm and BUR algorithm,the quality of the cumulative sensing data was improved by 16% and 20%,and the spatial coverage was improved by 30% and 22%.