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

Zou, Tengyue; Wang, Yuanxia; Wang, Mengyi; Shao, Lin

doi:10.3390/s17112555

Cited by 10 publications

(3 citation statements)

References 27 publications

(31 reference statements)

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“…Multi sensor Data Fusion Environment: Sensors with Different agriculture variants have been used to provide information that are capable in monitoring and optimizing growth level of plants, land water stage, humidity stage, soil category and temperature point of the agriculture ground as per the varying environmental factors [19] .These sensors inspect by penetrating the soil and records resistive forces before sending them to the central node for further data integration and data fusion [12].…”

Section: Deployment Of Agricultural Sensorsmentioning

confidence: 99%

Data Fusion For Intelligent Decision Making in Agriculture Resource Management

Dhaya¹,

Kanthavel²,

Venusamy³

2021

Preprint

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The Agricultural Resource Management System (ARMS) is an information system that geographically permits farmers to classify their field and farm animals’ for making services in terms of setting up resources, increasing their ecological Farm Plan, food traceability, and animal physical condition. It is a tough task of planning the agricultural resources and making use of them effectively such as farm construction, tools utilization, fertilizer processing, management amenities, and use facilities which adds to the production, training, and marketing of crops. Apart from the fundamental property sunlight, soil, and water, the seeds and animal breeds, and human Endeavour also should be given more priority as agricultural resources. On the other hand, Agriculture Management System is the administration website to assist farmers in achieving high productivity and profitability to furnish efficient agricultural procedures through online selling of products. In order to attain the benefits and profits through agricultural resources, the Data fusion technique is used to integrate data and knowledge impending from several sources to acquire exact outcomes remotely. The purpose of this paper is to construct an intelligent decision-making Data fusion algorithm for proficient agriculture resource management. In addition, the analysis of challenges of the resource management and solutions also are rendered by taking agriculture land’s natural resources like water, air, and temperature level in winter and autumn seasons..

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Section: Deployment Of Agricultural Sensorsmentioning

confidence: 99%

Data Fusion For Intelligent Decision Making in Agriculture Resource Management

Dhaya¹,

Kanthavel²,

Venusamy³

2021

Preprint

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“…Second, the reactor of biological oxidation pretreatment is set on an outdoor field. Third [19] analyzed the problem of the “sensor actuator effect” in greenhouses. The sensor near the temperature regulator was considerably affected due to the placement of the temperature regulator; however, the sensor that was far from the temperature regulator could not evidently change.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Nan

Wang

2018

Sensors

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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.

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“…The development of ubiquitous sensing, including wireless sensor networks and their applications, speeds up the production of data [6,7,8,9,10]. The value mined from these data further promotes rapid advances in the manufacturing of wireless sensors and the development of the Internet of Things.…”

Section: Introductionmentioning

confidence: 99%

A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications

Shen

Liao

Dan

et al. 2018

Sensors

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Big data gathered from real systems, such as public infrastructure, healthcare, smart homes, industries, and so on, by sensor networks contain enormous value, and need to be mined deeply, which depends on a data storing and retrieving service. HBase is playing an increasingly important part in the big data environment since it provides a flexible pattern for storing extremely large amounts of unstructured data. Despite the fast-speed reading by RowKey, HBase does not natively support multi-conditional query, which is a common demand and operation in relational databases, especially for data analysis of ubiquitous sensing applications. In this paper, we introduce a method to construct a linear index by employing a Hilbert space-filling curve. As a RowKey generating schema, the proposed method maps multiple index-columns into a one-dimensional encoded sequence, and then constructs a new RowKey. We also provide a R-tree-based optimization to reduce the computational cost of encoding query conditions. Without using a secondary index mode, experimental results indicate that the proposed method has better performance in multi-conditional queries.

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

Cited by 10 publications

References 27 publications

Data Fusion For Intelligent Decision Making in Agriculture Resource Management

Data Fusion For Intelligent Decision Making in Agriculture Resource Management

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

A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications

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