Hongmei Fei

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1. Introduction During the big data era, with the sharp increase in feature dimensions in intelligent information processing tasks, a large number of unnecessary or unrelated features appear in the dataset, which not only results in reduced model accuracy but also adds to the computational time burden [1].

1. Introduction Enhancing network revenue has consistently remained a crucial focus in the research of IUWSNs [1,2,3,4]. Due to size constraints, the sensing capacity of small wireless sensors is limited. The task allocation strategy is commonly designed for IUWSNs to enhance the revenue of the network. Generating an optimum task allocation for IUWSNs with limited power sources and limited perception constraints is very useful, but the problem is NP-hard.