WSAN actuator task distribution method based on BA-BPNN data fusion

Abstract

The invention discloses a WSAN actuator task distribution method based on BA-BPNN data fusion, and the method employs a BA optimization BP neural network to build a data fusion model. The method specifically comprises the steps: employing a bat algorithm to optimize the weight value and threshold value of the BP neural network, building a data fusion model, carrying out the data fusion of the sensor node information, and obtaining the task distribution information of an actuator node. The bat algorithm is a meta heuristic type group intelligent optimization algorithm, employs an echo positioning method of a miniature bat under the condition of different transmitting speeds and responses, can achieve a precise capturing and obstacle avoidance random search algorithm. The BP neural network is a multilayer feedforward neural network which can search a global optimal value in a training process, and can increase the convergence rate of the network. The method searches the optimal parameter of the BP neural network through the positioning updating of bats, is more precise in data fusion, and is more reasonable in task distribution of an actuator.

Description

technical field [0001] The patent of the present invention relates to a WSAN actuator task assignment method based on BA-BPNN data fusion, in particular to a bat algorithm (BATA Algorithm, BA ) The method of optimizing the weight and threshold of BP neural network, constructing a data fusion model, and performing data fusion on sensor node information to obtain task assignment information of actuator nodes belongs to the field of Internet of Things device information perception and control technology. Background technique [0002] BP neural network (BPNN) is a multi-layer feed-forward neural network based on the artificial neural network (ANN) in order to accelerate the convergence rate of the network, so as to find the global optimal value during the training process. BP neural network follows the basic principle of ANN - simulating the learning process of human brain nerves. Similar to the structure of ANN, the BP neural network includes an input layer, a hidden layer, an...

AI Technical Summary

Problems solved by technology

The above-mentioned A-A coordination does not exist in SA, that is, the coordination between actuators, so the response speed of events is fast, but the sensor nodes in the event area need to determine the actuator nodes and their corresponding events through complex distribution. response mechanism, which increases energy loss

In MA, sensor nodes each decide which actuator to send data to, and S-S coordination may not be performed, but this will not only lead to redundant sending of information, but also may activate unnecessary actuator nodes

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