Data aggregation algorithm based on clustering for wireless sensor networks

Zhai, Shuying; Yang, Xinyu; Li, Shuzhuang; Guo, Xiaoming

doi:10.1007/s00170-022-09998-0

Cited by 1 publication

(2 citation statements)

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“…The vultures participate for food to avoid starvation and many flock to the same food source. Equations ( 13) and (14) show the vulture's motion while the location update is performed by using Equation (15).…”

Section: Food Source Aggregationmentioning

confidence: 99%

“…Therefore, the data fusion approach is considered in the application of the WSN [ 11 , 12 ]. The main principle of data fusion is to combine the data received from various sensors, and the fused information is forwarded to BS [ 13 , 14 , 15 ]. An efficient data fusion is depending on the sensors delivery information where reliability and efficiency are the key parameters [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Food Source Aggregationmentioning

confidence: 99%