In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user’s perceived quality or cost value, how to establish a suitable user recruitment mechanism is the critical issue that this article needs to solve. It is necessary to learn the user’s perceived quality in the execution p. It also needs to try its best to ensure the efficiency and profit maximization of the mobile group intelligence perception platform. Therefore, this paper proposes a mobile crowdsensing user recruitment algorithm based on Combinatorial Multiarmed Bandit (CMAB) to solve the recruitment problem with known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multiarm bandit model. Each rocker arm represents the selection of different users, and the income obtained represents the user’s perceived quality. Secondly, it proposes the upper confidence bound (UCB) algorithm, which updates the user’s perceptual quality according to the completion of the task. This algorithm sorts the users’ perceived quality values from high to low, then selects the most significant ratio of perceived quality to recruitment costs under the budget condition, assigns tasks, and updates their perceived quality. Finally, this paper introduces the regret value to measure the efficiency of the user recruitment algorithm and conducts many experimental simulations based on real data sets to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recruitment algorithm with known user cost is close to the optimal algorithm, and the recruitment algorithm with unknown user cost is more than 75% of the optimal algorithm result, and the gap tends to decrease as the budget cost increases, compared with other comparisons. The algorithm is 25% higher, which proves that the proposed algorithm has good learning ability and can independently select high-quality users to realize task assignments.

SummaryWith the large‐scale popularity of mobile terminals, crowdsensing technology gradually replaces the existing static sensors with its advantages of high efficiency and low cost, becoming an emerging data collection method. How to assign perception tasks to the best performing users under the premise of ensuring quality and reducing costs to maximize the number of user tasks completed is the focus of the research on quantity sensitive task allocation. Based on this, a solution based on the improved whale optimization algorithm that combines the three operations of nonlinear decreasing convergence factor, optimal local jitter, and dynamic position update is put forward, which is used to solve the proposed task allocation problem. First, modeling the quantity sensitive task allocation problem, and then defining the spatial matching degree and skill matching degree according to the degree of adaptation between users and tasks. Taking into account the user's learning ability during the user's task execution, the skill update mechanism is introduced to update the user's existing skills in a timely manner, so as to improve task allocation effectiveness. Second, comprehensively considering the budget, the user's online time and the perceived task completion quality, and reasonably defining the task allocation problem that maximizes the number of tasks completed. In addition, from the perspective of selecting the best performing user for the task, designing a user selection strategy based on user's priority to reduce the cost of task allocation while ensuring the quality of the perceived task is basically completed. Then, in the process of solving the optimal task allocation plan, the improved algorithm is used to continuously optimize the initial task sequences of each iteration, and the final result can be obtained after a limited number of iterations. Finally, the improved algorithm is compared with other optimization algorithms in the same environment, and the results show that the improved algorithm has higher performance in solving task allocation problem.