In aquaculture ponds, wireless sensor networks (WSNs) with uneven temperature distribution and low collection efficiency may lead to poor monitoring effects. To improve the performance of temperature monitoring, a high-precision fusion strategy for a hierarchical WSN is proposed. In the bottom layer, the temperature data collected by the sensors are preprocessed by an improved unscented Kalman filter. In the middle layer, each cluster head sensor, as a local fusion center, is used to fuse the data collected from the sensors by a sequential analysis and fast inverse covariance intersection (ICI) algorithm. In the top layer, a global fusion center is utilized to fuse the temperature data from the middle layer to reflect the global temperature by an improved seagull algorithm to optimize the extreme learning machine (ELM) algorithm. Through calculation and simulation, the results show that the fusion strategy not only reduces external interference but also improves the accuracy of global optimal temperature state estimation while ensuring the stability and accuracy of data fusion. 

Data Fusion Based on Temperature Monitoring of Aquaculture Ponds With Wireless Sensor Networks

In aquaculture ponds, wireless sensor networks (WSNs) with uneven temperature distribution and low collection efficiency may lead to poor monitoring effects. To improve the performance of temperature monitoring, a high-precision fusion strategy for a hierarchical WSN is proposed. In the bottom layer, the temperature data collected by the sensors are preprocessed by an improved unscented Kalman filter. In the middle layer, each cluster head sensor, as a local fusion center, is used to fuse the data collected from the sensors by a sequential analysis and fast inverse covariance intersection (ICI) algorithm. In the top layer, a global fusion center is utilized to fuse the temperature data from the middle layer to reflect the global temperature by an improved seagull algorithm to optimize the extreme learning machine (ELM) algorithm. Through calculation and simulation, the results show that the fusion strategy not only reduces external interference but also improves the accuracy of global optimal temperature state estimation while ensuring the stability and accuracy of data fusion.

The process of extracting gold by biological oxidation involves oxidizing the refractory high-sulfur and high-arsenic ore with the help of bacteria to decompose the wrapping material of gold to extract the gold. Therefore, maximizing the activity of bacteria will directly affect the efficiency of gold extraction, for which it is particularly important to maintain the pulp temperature in the oxidation tank at the optimal bacteria breeding temperature. However, gold mines are generally located in mountainous areas, and the large temperature difference between day and night in winter, coupled with the influence of wind and snow, creates variations in the temperature in the oxidation tank. The traditional temperature measurement method cannot fully reflect the temperature change of the oxidation tank. As a multi-field application method, sensor information fusion can effectively address the problem of pulp temperature measurement. First, we analyzed the heat transfer principle inside the oxidation tank, and designed the cluster hierarchical sensor network according to the spatial position of each oxidation tank and the environmental interference factors. The network structure is divided into three layers; the bottom of the sensor to collect pulp temperature data shows a spiral distribution in the inner wall of the oxidation tank. Each cluster head node sensor is used as an intermediate layer to complete local measurement fusion estimation. Finally, the fusion center is taken as the upper layer to realize the global state fusion estimation. Secondly, in the data processing of the bottom temperature sensor, the traditional unscented Kalman filter (UKF) algorithm is improved and the fading memory matrix is added to improve the identification of nonlinear modeling errors. The sequential observation fusion estimator (SOFE) algorithm is embedded in the measurement update to improve the performance of local measurement fusion. Finally, in the global state fusion estimation, the sequential analysis is combined with the inverse covariance intersection, and the sequential analysis and inverse covariance intersection-global state fusion estimation (SICI-GSFE) algorithm is proposed. Through calculation and simulation, the results show that the external interference can be reduced by combining all the temperature state estimations, and the accuracy of the best global temperature state estimation is improved.

https://www.mdpi.com/1424-8220/20/16/4533/pdf

Temperature Sequential Data Fusion Algorithm Based on Cluster Hierarchical Sensor Networks.

Safety is a major concern for autonomous vehicle driving. The autonomous vehicles relying solely on ego-vehicle sensors have limitations in dealing with collisions. The risk can be reduced by communicating with other vehicles sharing sensed information. In this paper, we study a real-time multisource data fusion scheme based on Dempster–Shafer theory of evidence (DS) through cooperative vehicle-to-vehicle (V2V) communications. The classical DS can produce erroneous outputs when confidences are highly conflicting. In particular, a false negative error on object detection may lead to serious accidents. We propose a novel metric to measure distance between confidences on the collected data taking the asymmetry in the importance of sensed events into account. Based on the proposed distance, we consider the weighted DS combination in order to fuse multiple confidences. The proposed scheme is simple, and enables the vehicles to combine confidences in real-time. From real-world experiments, we show that it is feasible to lower the false negative rate by the proposed data fusion scheme, and to avoid accidents using the proposed collision warning system.

https://www.mdpi.com/2076-3417/10/19/6834/pdf

Real-Time Inter-Vehicle Data Fusion Based on a New Metric for Evidence Distance in Autonomous Vehicle Systems

Temperature Monitoring System For Plant Factory Based On Multi-Sensor Data Fusion

Biological oxidation pretreatment, which can improve the yield of gold, is the main gold extraction technology for disposing refractory gold ore with high arsenic and sulfur. The temperature of the oxidation tank influences the oxidation efficiency between the ore pulp and bacteria, including the yield of gold. Therefore, measurement has consistently been an important subject for researchers. As an effective data processing method, data fusion has been used extensively in many fields of industrial production. However, the interference of equipment or external factors such as the diurnal temperature difference or powerful wind may constantly increase measurement errors and damage certain sensors, which may transmit error data. These problems can be solved by following a pretreatment process. First, we establish a heat transfer mechanism model. Second, we design a small-range sensor network for the pretreatment process and present a layered fusion structure of sharing sensors using a multi-connected fusion structure. Third, we introduce the idea of iterative operation in data processing. In addition, we use prior data for predicting state values twice in order to improve the effectiveness of extended Kalman filtering in one time step. This study also proposes multi-fading factors on the basis of a weighted fading memory index to adjust the prediction error covariance. Finally, the state estimation accuracy of each sensor can be used as a weighting principle for the predictive confidence of each sensor by adding a weighting factor. In this study, the performance of the proposed method is verified by simulation and compared with the traditional single-sensor method. Actual industrial measurement data are processed by the proposed method for the equipment experiment. The performance index of the simulation and the experiment shows that the proposed method has a higher global accuracy than the traditional single-sensor method. Simulation results show that the accuracy of the proposed method has a 55% improvement upon that of the traditional single-sensor method, on average. In the equipment experiment, the accuracy of the industrial measurement improved by 37% when using the proposed method.

/pdf/real-time-weighted-data-fusion-algorithm-for-temperature-1yp6xn3wxn.pdf

Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network

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Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network