Abstract
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.
Highlights
In recent years, wireless sensor networks (WSNs) have been widely used to monitor the environment [1,2,3], such as the temperature, humidity, gas concentration, gas composition, dust and so on, for agricultural production purposes [4,5,6]
WSNs are composed of hundreds or thousands of sensor nodes that are used to acquire parameters under a range of conditions and transmit these parameters to a base station or sink node [8,9,10], enabling an information-based decision to be made in an automated manner
To verify the effect of the smooth weighted data fusion (SWDF) process introduced in this work, a simulation implemented in MATLAB is performed on a PC with a 3.4 GHz Intel Core CPU and 4 GB
Summary
Wireless sensor networks (WSNs) have been widely used to monitor the environment [1,2,3], such as the temperature, humidity, gas concentration, gas composition, dust and so on, for agricultural production purposes [4,5,6]. WSNs are composed of hundreds or thousands of sensor nodes that are used to acquire parameters under a range of conditions and transmit these parameters to a base station or sink node [8,9,10], enabling an information-based decision to be made in an automated manner. They are often used as the system for fire alarm in forests or used to monitor the environment in the field. A data fusion algorithm is required for selecting correct reports from mass data to identify accurate values from the measurements [11,12,13]
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