Mobile sink path planning method based on deep reinforcement learning algorithm

Abstract

The invention discloses a mobile sink path planning method based on a deep reinforcement learning algorithm. A mobile sink path is planned by using a deep reinforcement learning algorithm. A rasterized network state is mapped to an RGB image and the RGB image is input into a deep convolutional neural network, and network parameters are updated constantly through training. In practical application,an actual network state is only input into a trained neural network, so as to obtain an optimal traveling path of sink. The method considers a data delay requirement and network energy consumption ofa wireless sensor network comprehensively. Compared with a traditional wireless sensor network, the method can balance network energy consumption effectively and can improve energy efficiency. Rasterisation of the network state makes network state less complex.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor networks, and in particular relates to a mobile sink path planning method based on a deep reinforcement learning algorithm. Background technique [0002] The wireless sensor network is composed of a large number of sensor nodes deployed in the network area, the aggregation nodes for collecting information, and the management nodes. The sensor nodes communicate with each other in a multi-hop manner, forming a multi-hop self-organizing network. The wireless sensor network can collect, process and transmit data in the area. In the network coverage area, the sensor nodes collect and process the data, and forward them to other sensor nodes or sink nodes. [0003] In a traditional wireless sensor network, the positions of all nodes are fixed, and most of the sensor nodes are powered by batteries, and it is difficult to replace the batteries once they are deployed. In WSN, the most typical data ...

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Problems solved by technology

[0003] In a traditional wireless sensor network, the positions of all nodes are fixed, and most of the sensor nodes are powered by batteries, and it is difficult to replace the batteries once deployed

In WSN, the most typical data collection method is that sensor node data is transmitted to the base station or sink node in a multi-hop manner. Sensor nodes close to the base station or sink carry more forwarding tasks, and energy consumption is faster, resulting in energy holes. and hot spots

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