A crowd sensing data collection framework based on edge computing

With the rapid increase in the use of mobile devices equipped with built-in sensors, mobile crowd sensing (MCS) as a human driven perception mode came into being. Since a large number of users submit data to the cloud servers in parallel, it will not only increase the pressure on the cloud servers, but also lead to the problem of data redundancy. In order to solve this problem, this paper introduces edge computing into mobile crowd sening for collecting perception data, and proposes a group intelligence perception network data collection model based on edge computing. The data is observed and sampled at the edge node through the compressed sensing algorithm, and the compressed data is transmitted to the cloud server.Using Cl_ BP algorithm restores the compressed data in cloud server .The results show that compared with the orthogonal matching pursuit algorithm (OMP), the data collection model based on edge cloud computing proposed in this paper can better solve the problem of data redundancy.