The invention relates to an optimization
algorithm based on
edge computing that is applied in the MCS (Mobile Crowd Sensing) sensing task scene, and is based on the recruitment of task participants, the judgment of information validity, and the detection and correction of errors. The present invention represents platform utility as a logarithmic function (strictly increasing and sagging) of independent measurement data for each location, and the
algorithm aims to maximize total utility under exerting budget constraints. Since the proposed joint
optimization problem is a non-deterministic polynomial
combinatorial optimization problem, it cannot be solved in polynomial time. Therefore, we design an
approximation algorithm that greatly reduces the computational complexity. The present invention uses a deep neural network (DNN) for
data verification, thereby increasing the accuracy of predicted data. In addition, the present invention also adopts
data redundancy to improve the sensing quality, and when measurement errors are detected near the
data source, the amount of data transmitted from the
base station to the sensing platform is greatly reduced, the occupied
frequency band resource is reduced, and the time
delay is reduced.