Abstract
Wireless Sensor Network (WSN) as one of the representatives of the Internet of Things technology has also received much attention. To accurately diagnose fault sensor nodes, a fault diagnosis method based on fireworks algorithm optimization convolutional neural network algorithm is proposed. The weights and biases of the convolutional neural networks are optimized by using the self-regulating mechanism of global and local searching ability of fireworks algorithm. So the problem of convolution neural network in extreme judgment and limited convergence speed is solved, to effectively realize the fault diagnosis of the WSN. Simulation experiments show that this algorithm has higher fault diagnosis accuracy than other classic WSN fault diagnosis algorithms.
Highlights
In the context of the development of big data and the Internet of Things, Wireless Sensor Network (WSN) has gradually become a research hotspot in various fields, especially in the fields of military warfare, environmental monitoring and forecasting, security monitoring, smart home, and health care
Considering that the fireworks algorithm has strong self-regulation mechanism of global search ability and local search ability [19], and fireworks algorithm can effectively solve the problem of system-level fault diagnosis [20], this paper introduces the fireworks algorithm into the convolutional neural network model and proposes a new WSN fault diagnosis method–FWA-CNNFD (Fireworks Algorithm-Convolutional Neural Networks Fault Detection)
By combining the fireworks algorithm and the convolutional neural network, it can overcome the problems of the slow speed of the general convolutional neural network algorithm and easy to fall into the local minimum value, and have the advantages of the convolutional neural network and the firework algorithm itself
Summary
In the context of the development of big data and the Internet of Things, WSN has gradually become a research hotspot in various fields, especially in the fields of military warfare, environmental monitoring and forecasting, security monitoring, smart home, and health care. Bias are used as the initial weight and bias of the convolutional neural network to construct the wireless sensor fault diagnosis model. By combining the fireworks algorithm with the convolutional neural network, the wireless sensor fault diagnosis efficiency is greatly improved. The base station or center node collects node information in a centralized way, and the fault identification requires a high level of equipment, so this method does not apply to large-scale networks. If the node can make more decisions, the less information that needs to be fed back to the base station and other centers for judgment, effectively reducing the extra energy overhead and extending the life of the network This diagnostic framework can make the diagnosis of large-scale dense networks easier. The k activation values y1, y2, · · · , yn are output after the multi-layer full connection, and the final classification result after the softmax regression processing is as shown in Eq (4)
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