Topology control method based on unsupervised learning for ultra-dense wireless sensor network

A wireless sensor network, unsupervised learning technology, applied in network topology, transmission monitoring, wireless communication, etc., can solve problems such as poor flexibility, lack of adaptability and customizability in multiple application fields, and inability to effectively adapt to application requirements, etc. Increase the network life and improve the effect of energy consumption

Active Publication Date: 2018-11-02
SHANGHAI INST OF MICROSYSTEM & INFORMATION TECH CHINESE ACAD OF SCI +1
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Problems solved by technology

Based on IEEE 802.15.4 (PHY, MAC), Zigbee forms a low-speed network specification by adding three layers of Network, Security and Application protocols. Short-distance communication within 100 meters cannot effectively meet the application requirements of heterogeneous sensor networks where low-speed short-di...

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  • Topology control method based on unsupervised learning for ultra-dense wireless sensor network
  • Topology control method based on unsupervised learning for ultra-dense wireless sensor network
  • Topology control method based on unsupervised learning for ultra-dense wireless sensor network

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[0022] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0023] Embodiments of the present invention relate to a topology control method based on unsupervised learning for ultra-dense wireless sensor networks, such as figure 1 shown, including the following steps:

[0024] (1) Initial population: Randomly code all nodes in the network as chromosome individuals, "1" represents the cluster head node, "0" represents the cluster member, and randomly generate R chromosome individuals to ...

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Abstract

The invention relates to a topology control method based on unsupervised learning for an ultra-dense wireless sensor network. On the basis of a framework of a genetic algorithm, network nodes are clustered, and the optimal clustering network topology is searched through continuous unsupervised learning. In the optimization process, the three factors of node energy, node distance and node density of the network are important input data sets, and different factor weights are determined by using a hierarchical analysis method to establish a fitness function. By adoption of the topology control method provided by the invention, the node energy consumption can be effectively improved, and the network life of the wireless sensor network is improved at last.

Description

technical field [0001] The invention relates to the technical field of wireless sensor networks, in particular to a topology control method based on unsupervised learning for ultra-dense wireless sensor networks. Background technique [0002] With the advent of the 5G era, ultra-dense wireless sensor networks are one of the important components of future networks, and the issue of network lifespan has always been the focus and focus of research on ultra-dense wireless sensor networks. Wireless sensor network is a network in which nodes can dynamically and automatically find the optimal path to transmit and collect new information to the base station. It is widely used in military, industrial control, agricultural production and many other fields. Due to the limited energy supply and processing capability of nodes, traditional routing algorithms cannot be directly applied to wireless sensor networks. Therefore, establishing a topology with good network performance is the key...

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Application Information

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IPC IPC(8): H04L12/24H04W40/04H04W40/32H04W84/18H04B17/391
CPCH04L41/0893H04L41/145H04W40/04H04W40/32H04W84/18H04B17/3911Y02D30/70
Inventor 常玉超唐洪莹赵沁王艳丁吉芸马忠建程小六李宝清袁晓兵
Owner SHANGHAI INST OF MICROSYSTEM & INFORMATION TECH CHINESE ACAD OF SCI
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