Internet of things (IoT) error sensor node location method based on improved Q learning algorithm

Abstract

The invention discloses an internet of things (IoT) error sensor node location method based on an improved Q learning algorithm. The method is characterized in that the traditional Q learning method is improved to make calculated Q values adaptively change according to characteristic information such as residual energy of a sensor node, routing and transmission hop count, a routing path is established by means of a maximal Q value, meanwhile, a background server works out a network topological structure, when the node is attached or produces error data, an error range is set by comparison with the Q value of the node of the next period, and when the range is exceeded, the node is judged to be the error node and is located. According to the invention, extra energy of the sensor node does not need to be consumed, and when a wireless sensor network topological structure changes, higher robustness is also achieved; and the method has the advantages of intelligent property, low energy consumption, high adaptive degree and the like, can be used to not only routing, location and energy consumption performance evaluation of the sensor node but also accurate location of unknown error nodes, and has wide application values.

Description

technical field [0001] The invention belongs to the field of Internet of things public security, and in particular relates to an improved reinforcement learning algorithm applied to error node positioning of a wireless sensor network. Background technique [0002] The Internet of Things (The Internet of Things) is a new system of real-time interaction between the virtual network and the real world. Its ubiquitous data perception, wireless-based information transmission, and intelligent information processing are conducive to improving social efficiency. , but it also arouses the public's attention to information security and privacy protection issues. Among them, the wireless sensor network (Wireless Sensor Network, WSN) is an important part of the Internet of Things. The sensor nodes are exposed in the public. Compared with the wired network, the wireless sensor network is more vulnerable to various security threats, such as node Victimization, routing destruction, error m...

AI Technical Summary

Problems solved by technology

Compared with the traditional Q-learning, the one-dimensional state-action pair only solves the routing problem of the wireless sensor network, the reward function is relatively single and fixed, and the error node cannot be accurately located, which requires additional energy consumption of the sensor node. Less robustness when sensor network topology changes

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