A data fusion method in wireless sensor network based on belief structure

Long, Chengfeng; Liu, Xingxin; Yang, Yuekui; Zhang, Tao; Tan, Siqiao; Fang, Kui; Tang, Xuhua; Yang, Gelan

doi:10.1186/s13638-021-01916-4

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Recently, many scholars have studied the data aggregation technology to prolong the lifetime of wireless sensor networks. Long C et al [12] proposed a data fusion algorithm based on belief structure in wireless sensor networks. The data fusion is performed at the cluster head node based on multi-scale multi-granulation rough set.…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: An efficient dual layer data aggregation scheme in clustered wireless sensor networks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

WITHDRAWN: An efficient dual layer data aggregation scheme in clustered wireless sensor networks

Yang,

Xu,

Yang

2023

Preprint

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“…Long et al [ 22 ] developed the belief structure-based data fusion for reducing the attributes of the multi-granulation rough set. Next, the transitivity and consistency were examined by developing the architecture of the positive region, evidence and belief.…”

Section: Related Workmentioning

confidence: 99%

“…A number of nodes that belong to the next hop is represented as node degree, which is expressed in Equation (22).…”

mentioning

confidence: 99%

Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based African Vulture Optimization Algorithm for Data Fusion in Wireless Sensor Networks

Qaffas

2023

Sensors

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A Wireless Sensor Network (WSN) is a group of autonomous sensors geographically distributed for environmental monitoring and tracking purposes. Since the sensors in the WSN have limited battery capacity, the energy efficiency is considered a challenging task because of redundant data transmission and inappropriate routing paths. In this research, a Quasi-Oppositional Learning (QOL)-based African Vulture Optimization Algorithm (AVOA), referred to as QAVOA, is proposed for an effective data fusion and cluster-based routing in a WSN. The QAVOA-based Back Propagation Neural Network (BPNN) is developed to optimize the weights and threshold coefficients for removing the redundant information and decreasing the amount of transmitted data over the network. Moreover, the QAVOA-based optimal Cluster Head Node (CHN) selection and route discovery are carried out for performing reliable data transmission. An elimination of redundant data during data fusion and optimum shortest path discovery using the proposed QAVOA-BPNN is used to minimize the energy usage of the nodes, which helps to increase the life expectancy. The QAVOA-BPNN is analyzed by using the energy consumption, life expectancy, throughput, End to End Delay (EED), Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR). The existing approaches such as Cross-Layer-based Harris-Hawks-Optimization (CL-HHO) and Improved Sparrow Search using Differential Evolution (ISSDE) are used to evaluate the QAVOA-BPNN method. The life expectancy of QAVOA-BPNN for 500 nodes is 4820 rounds, which is high when compared to the CL-HHO and ISSDE.

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

Yang,

Xu,

Yang

2024

ETRI Journal

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In wireless sensor network (WSN) monitoring systems, 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. To reduce the energy consumption of sensor nodes and prolong the life of WSNs, this study proposes a dual layer intracluster data fusion scheme based on ring buffer. 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 the 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. Experimental results reveal that the scheme proposed in this study has clear advantages in three aspects: the number of remaining nodes, residual energy, and the number of packets transmitted. The data fusion of the proposed scheme is confined to the data fusion of the same attribute environment parameters.

show abstract

A data fusion method in wireless sensor network based on belief structure

Cited by 3 publications

References 25 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

Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based African Vulture Optimization Algorithm for Data Fusion in Wireless Sensor Networks

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

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