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The active learning multi-task allocation method in mobile crowd sensing based on normal cloud model
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DOI：10.1016/j.pmcj.2020.101181.png)
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Abstract
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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. (c) 2020 Elsevier B.V. All rights reserved.
Keywords:
Mobile crowd sensing
Task allocation
Normal cloud model
Active learning
Real-time monitoring
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