Cokriging for cross-attribute fusion in sensor networks

Palafox-Albarrán, Javier; Jedermann, Reiner; Hong, Bonghee; Lang, Walter

doi:10.1016/j.inffus.2014.09.007

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…To improve the accuracy of sensing and reduce the burden of data transmission, researchers are now focusing on data fusion mechanisms [ 14 , 15 , 16 ]. A data fusion scheme based on a grey model and an extreme learning machine has been proposed to reduce redundant transmissions and extend the lifetime of the network [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks

Zou

Wang

et al. 2017

Sensors

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Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim’s algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.

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