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Sensory Task Assignment Based on Dempster-Shafer Theory and Multi-Attribute Fusion in Mobile Sensor Networks

Sensing task allocation is one of the most challenging issues in the field of mobile sensor networks (MSNs). Many existing studies have focused on various aspects of sensing task allocation, such as the relationship between sensing task allocation and the number of participants, the sensing task completion time, and the reward required to complete the task. However, few studies have focused on the relationship between sensing tasks and agent attributes. To address this issue, we first analyze the relationship between the structural characteristics of the mobile perceptron network and the agents. Further, we model the relationship between agents using multi-attribute fusion. Then, according to this model and its preference for sensing tasks, we proposed the sensing task allocation algorithm based on Dempster-Shafer (D-S) theory and multi-attribute fusion(STADMF). Finally, STADMF is compared with three algorithms on large-scale real data sets and a synthetic mobile crowdsensing (MCS) trace. The results show that the proposed algorithm achieves good performance in terms of the similarity and accuracy of task sensing.