Multi-base station charging method based on SOM neural network in WRSNs
A neural network and charging method technology, applied in the field of wireless sensor networks, can solve the problems of difficult to achieve accurate division of high-density wireless sensor network area division, difficult to achieve sensor charging coverage, mobile energy loss, etc., to reduce algorithm complexity, The effect of reducing mobile energy loss and reducing charging loss
Active Publication Date: 2019-07-19
HOHAI UNIV CHANGZHOU
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Problems solved by technology
[0007] (1) Large-scale wireless rechargeable sensor network is difficult to achieve the charging coverage of all sensors;
[0008] (2) Excessively long mobile paths in large wireless sensor networks cause a large amount of mobile energy loss;
[0009] (3) The area division of high-density wireless sensor network is difficult to achieve accurate division;
[0010] (4) Large-scale wireless sensor networks involve frequent charging between charging vehicles, which will cause a lot of charging loss problems
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[0040] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.
[0041] A multi-base station charging method based on SOM neural network in high-density low-power WRSNs under the background of industrial Internet of Things, comprising the steps of:
[0042] (1) Use the SOM neural network to classify wireless rechargeable sensor networks according to the characteristics of sensor node energy consumption, residual energy and location, such as figure 1 shown;
[0043] (2) Before the charging operation starts, according to the energy consumption of the sensors and the battery capacity of the mobile charging vehicle, the i-th level of the network is divided into k with nearly equal number of sensor nodes i An area and equipped with a first-class mobile charger t...
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The invention discloses a multi-base station charging method based on an SOM neural network in WRSNs. The method comprises the steps of classifying wireless rechargeable sensor networks by utilizing the SOM neural network; according to the energy consumption of sensors and the battery capacity of chargers, dividing the ith level of the network into ki areas with the same sensor node number, and equipping the first-level mobile chargers; performing optimal planning on traversing paths of the first-level mobile chargers by utilizing a genetic algorithm, and selecting resident points on the paths; planning an optimal path by utilizing the genetic algorithm according to the resident points, and equipping the second-level mobile chargers and base stations on the path; and in each charging period, charging sensor nodes by the first-level mobile chargers, and after all the first-level mobile chargers finish the charging, enabling the second-level mobile chargers to start to charge the first-level mobile chargers. The method has the beneficial effects that the algorithm complexity is low; the sensors can be accurately classified; full coverage charging of the sensor networks is realized; and the mobile energy loss and the charging loss are reduced.
Description
technical field [0001] The invention belongs to the technical field of wireless sensor networks, and in particular relates to a multi-base station charging method based on a SOM neural network in high-density and low-power WSNs. Background technique [0002] In the Industrial Internet of Things, sensor networks are widely used. However, it is impossible to use wired connections to add sensors to rotating equipment to monitor the working conditions of equipment, and to monitor the environment of large coal mines. Therefore, wireless sensor networks are booming, often connected with low-power wide-area networks, and have the characteristics of large-scale and high-density. The key to promoting the development of wireless sensor networks in the Industrial Internet of Things is to solve the problem of limited sensor node energy. [0003] Wireless charging sensor networks have received attention in recent studies, including planning mobile charging vehicle paths, making chargin...
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Login to View More IPC IPC(8): H02J7/00H04W16/18H04W84/18G06N3/02G06N3/12
CPCH02J7/0003H04W16/18H04W84/18G06N3/126G06N3/02Y02T10/70
Inventor 韩光洁廖泽钦刘立刘国高
Owner HOHAI UNIV CHANGZHOU