 The Internet of Things, IoT, technology provides convenience for data acquisition in environmental monitoring and environmental protection, as well as avoiding invasive damage caused by traditional data acquisition methods. Additionally, an adaptive cooperative optimization Segal algorithm, PSOSOA, has been proposed to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the IoT. This algorithm calculates the individual fitness value according to the total number of nodes, coverage radius, and area edge length, selects the initial population, and aims at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximized, the global output is output. The optimal solution is the node's mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current Segal individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal Segal individual position is fine-tuned by random opposite learning, leading the whole Segal to move to the correct position in the given search space, improving the ability to jump out of there. This article was authored by Li Chao, Zihui Wang, Zihai Wang, and others. We are article.tv, links in the description below. Thank you for watching.