Abstract: In mobile crowdsensing systems,the user’s participation intention and experience have an important impact on the overall performance and long-term sustainable operation of the system.Most existing user-oriented task allocation strategies only consider the cost-benefits of users and ignore the load balancing issue in the task allocation process,resulting in the premature exit of some key nodes due to heavy loads,which affects the long-term performance of the system.Therefore,this paper proposes a user-centered,long-time dynamic task allocation model.Aiming at the dynamics and persistence of the model,a solution algorithm based on improved Lyapunov optimization theory is proposed,which simultaneously considers the dual optimization of overall system benefits and load balancing,achieving optimal task allocation with load balancing constraints in dynamic environments.Experimental results demonstrate that the proposed algorithm improves the load balancing of users by nearly 20% under the pre-mise of ensuring queue stability and optimal overall system benefits.

Key words: Mobile crowdsensing, Load balancing, Lyapunov optimization theory, Overall system benefits