An Optimized Data Fusion Paradigm for WSN Based on Neural Networks

Alsafasfeh, Moath; Arida, Zaid A.; Saraereh, Omar A.; Alsafasfeh, Qais; Alemaishat, Salem

doi:10.32604/cmc.2021.018187

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The sink node uses the Adaptive decentralized Kalman filter to calculate the fusion estimation. Alsafasfeh M et al [15] proposed a data fusion algorithm based on the back propagation neural network model. The algorithm regards the cluster as a back propagation neural network to fuse the sensed data.…”

Section: Related Workmentioning

confidence: 99%

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WITHDRAWN: An efficient dual layer data aggregation scheme in clustered wireless sensor networks

Yang,

Xu,

Yang

2023

Preprint

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In wireless sensor network monitoring system, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. In order to reduce energy consumption of sensor nodes and prolong the life of wireless sensor networks, this paper proposed a dual layer intra-cluster data fusion scheme based on ring buffer (DLIDF). To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experiment results reveal that the scheme proposed in this paper has clear advantages in three aspects: the number of remaining nodes, the residual energy, and the number of packets transmitted.

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Section: Related Workmentioning

confidence: 99%

“…Following node deployment, some nodes are chosen as cluster head nodes base on the cluster head selection algorithm [15]. The cluster head broadcasts the cluster head beacon to its neighboring nodes.…”

Section: Construction Of Clustermentioning

confidence: 99%

WITHDRAWN: An efficient dual layer data aggregation scheme in clustered wireless sensor networks

Yang,

Xu,

Yang

2023

Preprint

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“…Alsafasfeh proposed a data fusion model based on the backpropagation neural network (BPNN) model. In the process of information transmission, the output function of the neural network was used to process a large amount of sensory data, and the eigenvalues of the sensory data were extracted and transmitted to the sink node, in order to solve the problem of a large number of invalid or redundant data [8]. Zhao proposed a new approach to modeling the overall operational risk of discrete manufacturing systems, based on the characteristics and nature of operational data.…”

Section: Related Workmentioning

confidence: 99%

Structure Optimization and Governance of Multilevel Rail Transit Integration under the Background of a Metropolitan Area Based on the Industrial Internet of Things Security Data Fusion Method

Dai

Zhu

2022

Mobile Information Systems

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In recent years, the urban rail transit network architecture has gradually grown, the contradiction between rail transit passenger flow and transport load has been deepening, and its carrying capacity has also been tested. Passenger flow risk has become the most important source of risk in rail transit. The key to restricting rail transit service quality is how to effectively monitor and manage rail transit passenger flow and provide accurate and convenient early warning to staff. In order to effectively manage the complex passenger flow scene, the premise is to distinguish the real-time state of its moving target. The time series data feature extraction and LSTM data fusion were used to analyze the traffic data sequence in the multilevel rail transit network model. The multilevel rail transit integration of the Internet of things is modeled by the method of data fusion. It can be seen from the experimental data that in the data fusion mode, the network comprehensive evaluation prediction value fitting effect can quickly converge, and the error rate is less than 4%. By comparing the mean square error (MSE) and mean absolute error (MAE) data of the traditional method and the experimental method used in this paper through two different datasets, it was understood that the MAE under the data fusion method was reduced by 8 compared with the traditional method, and the MSE was decreased by 33, indicating that this method can bring better simulation effect to the model. The improvement of the synergistic and complementary functional network and the acceleration of the efficient and convenient flow of elements are the inevitable results of the integration of multilayer rail transit in the metropolitan area.

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“…(1) Distribution Multiple computers in a distributed system will be randomly distributed in space, and the distribution of machines will change at any time [11][12].…”

Section: Features Of Distributed Systemsmentioning

confidence: 99%

Distributed System Optimization Analysis Based on Data Fusion and Data Transmission Method

2021

DPS

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With the continuous development of network technology and the release of various software products, people's lives are increasingly connected with the Internet. With the rapid increase in the number of users of software products, the processing requirements of servers are also increasing rapidly, and distributed systems begin to play an important role. The purpose of this paper is to analyze the optimization of distributed systems based on data fusion and transmission methods. First, it reveals the research and development background of the subject and its importance in scientific research, and studies the data acquisition technology based on RabbitMQ. Some new RabbitMQ implementation functions are proposed in the log collection service, and the RabbitMQ message processing method inside the model is analyzed and optimized. After comparative experiments in this paper, it is confirmed that the optimized RabbitMQ can support more log generators and thus have more efficient processing performance. Through experimental comparison and data analysis, it can be found that in this use case, when there is only one producer, the maximum delivery speed of the server before optimization can be reached faster, with a speed of 3800msg/s, while the optimized server with the maximum delivery speed is 3950msg/s, 2.3% higher than the highest delivery speed.

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An Optimized Data Fusion Paradigm for WSN Based on Neural Networks

Cited by 6 publications

References 12 publications

WITHDRAWN: An efficient dual layer data aggregation scheme in clustered wireless sensor networks

WITHDRAWN: An efficient dual layer data aggregation scheme in clustered wireless sensor networks

Structure Optimization and Governance of Multilevel Rail Transit Integration under the Background of a Metropolitan Area Based on the Industrial Internet of Things Security Data Fusion Method

Distributed System Optimization Analysis Based on Data Fusion and Data Transmission Method

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