For task allocation of mobile crowd sensing, aiming at the problem that the task cannot​ be completed normally due to the change of sensing state and the data quality is reduced because the sensor willingness is not satisfied, a task allocation method with active learning ability based on the normal cloud model is proposed. Firstly, the data quality, sensing environment and network state of sensors are evaluated, and the threshold is set according to the power of sensor and the total amount of data sent, and on the basis, the sensor sensing ability is monitored in real time. Then, the multi-granularity standard distribution cloud and sensor state cloud are established through normal cloud model when the sensing state changes. Furthermore, the willingness score of the sensor to the task is obtained by cosine method. Finally, according to the willingness list, tasks are allocated by the maximum sensor willingness and the minimum number of sensors. Simulation experiment verifies that the task can be allocated when the sensing state is changed, and the quality of sensing data is better. Besides, in the incentive effect, the actual proportion of sensing sensors and the average numbers of tasks completed by sensors are better, and the incentive budget is smaller when the same data quality is obtained.

针对移动人群感知的任务分配，针对由于感知状态的变化而导致任务无法正常完成，由于传感器意愿不满足而导致数据质量下降的问题，一种基于主动学习能力的任务分配方法在正常的云模型上提出。首先，评估传感器的数据质量，传感环境和网络状态，并根据传感器的功率和发送的数据总量设置阈值，并在此基础上实时监视传感器的传感能力。然后，当感测状态发生变化时，通过正态云模型建立多粒度标准分布云和传感器状态云。此外，通过余弦法获得传感器对任务的意愿得分。最后，根据意愿清单，任务由传感器的最大意愿和传感器的最小数量分配。仿真实验验证了在改变传感状态时可以分配任务，提高了传感数据的质量。此外，在激励效果上，感测传感器的实际比例和传感器完成的任务的平均数量较好，而在获得相同数据质量的情况下，激励预算较小。