Method for using graph convolution reinforcement learning to minimize information age in group perception

Abstract

The invention discloses a method for using graph convolution reinforcement learning to minimize information age in group perception. The method comprises the following steps that: step 1, a sensing platform opens a main process; step 2, the sensing platform opens a sub-process interacting with the environment; step 3, states of a mobile user and a sensing body in a sensing area are observed, wherein the states comprise positions and moving directions of the sensing body and the user; step 4, interaction characteristics obtained by using a relational graph convolutional network are used as input; step 5, the step 3 and the step 4 are repeatedly executed until rounds are finished, step 6, batch empirical data is sampled from the empirical multiplexing pool by the host process of the sensing platform, and step 7, the step 2, the step 5 and the step 6 are repeatedly executed until the number of interaction rounds reaches an upper limit; and step 8, taking out the stored optimal parameters by the host process of the sensing platform. The method has the beneficial effects that the information age can be minimized, and excessive dependence on a simulation platform can be reduced.

Description

technical field [0001] The invention belongs to the technical field of group sensing, and in particular relates to a method for minimizing information age by using graph convolution reinforcement learning in group sensing. Background technique [0002] Crowd sensing has been recognized as an efficient and scalable way to obtain data for various smart city applications, such as traffic control and road condition monitoring. In the scenario of group sensing tasks, mobile users on the ground are constantly moving and obtaining data in the city. The sensing cluster (composed of multiple sensory bodies) serves as a mobile base station to serve mobile users and collect data obtained by mobile users for subsequent use. analysis processing. [0003] In group sensing tasks, many data have strong timeliness (such as traffic information), and the delay in obtaining real-time data from mobile users is a key indicator to measure the quality of task completion. How to design the trajecto...

AI Technical Summary

Problems solved by technology

[0005] 1. The perception cluster scheduling algorithm should achieve good results on multiple evaluation indicators at the same time, including collecting the data of each mobile user as soon as possible and efficiently, ensuring high user coverage, and saving energy consumption of the perception body as much as possible. However, the challenge lies in these It is difficult to balance the evaluation indicators. In order to improve the user coverage, the sensing body inevitably needs to collect data in relatively remote areas, resulting in a large energy consumption

[0006] 2. The movement of mobile users is random and uncontrollable, and the perception cluster needs to carefully design its trajectory according to the location distribution of mobile users over time, which is much more difficult than the task of scheduling sensory bodies to collect data from stationary objects

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