Wireless sensor networks are widely used in many fields, such as medical and health care,military monitoring, target tracking, and\npeopleâ??s life, because of their advantages of convenient deployment, low cost, and good concealment. However, due to the low\nbattery capacity of sensor nodes and environmental changes, the energy consumption of nodes is serious and the accuracy of data\ncollection is low. In the data collection method of multiple random paths, due to the uneven geographical distribution between\nnodes and the influence of the environment, it is easy to cause the communication between nodes to be blocked and the\nconstruction of random paths to fail.This paper proposes an efficient data collection algorithm for this problem. The algorithm is\nimproved on the basis of the random node selection algorithm.This method can effectively avoid the failure of random path node\nselection and improve the node selection of random path in wireless sensor networks. Then, the sensor network in the dynamic\nenvironment is analyzed based on the static environment. An efficient data collection algorithm based on the position prediction\nof extreme learning machines is proposed.This method uses extreme learning machine methods to perform trajectory prediction\nfor nodes in a dynamic environment.
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