Abstract
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.
Highlights
The concept of crowdsourcing was first proposed by Jeff Howe, a journalist of Wired magazine, in June 2006
A large number of successful executions of the task indicates that the agent has strong perceptual ability, intuitively, the agent is capable of performing the task
Where X represents the set of agents in the sensor network, Y represents the set of agent-aware tasks in the perceptron network, and R indicates that in the sensor network, according to the attribute characteristics of the perceptron and different preference selection criteria, the agent that meets the criteria of agent preference is chosen for the perceptual task
Summary
The concept of crowdsourcing was first proposed by Jeff Howe, a journalist of Wired magazine, in June 2006. The rapid development of electronic and network technologies has contributed toward the growing importance of smart devices, including embedded, handheld, and wearable devices, in daily life [1], [2] Such smart devices can perceive large-scale physical and social phenomena, which has led to the emergence of a new cognitive paradigm, namely mobile crowdsensing (MCS). L. Zhang et al.: Sensory Task Assignment Based on D-S Theory and Multi-Attribute Fusion in MSNs. In a social network, the influence of the user preference and the space-time constraint on the recommendation service can be determined by mining the location information of the user. We establish the sensing task allocation algorithm based on Dempster-Shafer (D-S) theory and multi-attribute fusion (STADMF), which provides a new way to distribute the perceived tasks.
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