scholarly journals Advancing Wireless Sensor Networks for Irrigation Management of Ornamental Crops: An Overview

2013 ◽  
Vol 23 (6) ◽  
pp. 717-724 ◽  
Author(s):  
John D. Lea-Cox ◽  
William L. Bauerle ◽  
Marc W. van Iersel ◽  
George F. Kantor ◽  
Taryn L. Bauerle ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Irrigation Management ◽  
Economic Benefits ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Future Challenges ◽  
Substrate Moisture ◽  
Species Specific ◽  
And Control

Wireless sensor networks (WSNs) transmit sensor data and control signals over long distances without the need for expensive infrastructure, allowing WSNs to add value to existing irrigation systems since they provide the grower with direct feedback on the water needs of the crop. We implemented WSNs in nine commercial horticulture operations. We provide an overview of the integration of sensors with hardware and software to form WSNs that can monitor and control irrigation water applications based on one of two approaches: 1) “set-point control” based on substrate moisture measurements or 2) “model-based control” that applied species-specific irrigation in response to transpiration estimates. We summarize the economic benefits, current and future challenges, and support issues we currently face for scaling WSNs to entire production sites. The series of papers that follow either directly describe or refer the reader to descriptions of the findings we have made to date. Together, they illustrate that WSNs have been successfully implemented in horticultural operations to greatly reduce water use, with direct economic benefits to growers.

scholarly journals Ornamental Grower Perceptions of Wireless Irrigation Sensor Networks: Results from a National Survey

2013 ◽  
Vol 23 (6) ◽  
pp. 775-782 ◽  
Author(s):  
John Majsztrik ◽  
Erik Lichtenberg ◽  
Monica Saavoss
Keyword(s):  
Sensor Networks ◽  
Irrigation Management ◽  
Water Source ◽  
Wireless Sensor ◽  
Sensor Systems ◽  
Potential Benefits ◽  
Substrate Moisture ◽  
Disease Pressure ◽  
And Control ◽  
On Farm

Irrigation management systems that use wireless transmission of substrate moisture data are beginning to become commercially available for ornamental growers, particularly for use in soilless substrates. These systems allow growers to precisely monitor and control irrigation in real time and are being shown to save time and other resources. On-farm evaluations indicate that these systems have potential benefits extending beyond reductions in water use and associated irrigation inputs: Some growing systems experience increases in plant growth rates, with corresponding reductions in production time, whereas some experience reductions in disease pressure and corresponding plant losses. We asked ornamental growers across the nation what they see as potential benefits and limitations of these systems as a means of assessing the likely state of acceptance of this technology at the time of its initial introduction. Grower perceptions were overwhelmingly positive, with the majority of respondents agreeing that wireless sensor systems can increase irrigation efficiency, improve product quality, reduce product losses, reduce irrigation management costs, reduce disease prevalence, increase ability to manage growth, reduce irrigation management costs, and reduce monitoring costs. System cost and reliability were major concerns. Grower perceptions of the benefits and drawbacks of irrigation sensor networks varied across size and type of operation as well as geographically and by the type of water source used. Making wireless sensor systems affordable and robust will likely be critical determinants of the speed and reach of adoption of these technologies.

scholarly journals Implementation of Wireless Sensor Networks for Irrigation Control in Three Container Nurseries

2013 ◽  
Vol 23 (6) ◽  
pp. 747-753 ◽  
Author(s):  
Matthew Chappell ◽  
Sue K. Dove ◽  
Marc W. van Iersel ◽  
Paul A. Thomas ◽  
John Ruter
Keyword(s):  
Wireless Sensor Networks ◽  
Soil Moisture ◽  
Sensor Networks ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Wireless Sensor ◽  
And Control ◽  
On Farm ◽  
Control Irrigation ◽  
Monitor And Control

Water quality and quantity are increasingly important concerns for agricultural producers and have been recognized by governmental and nongovernmental agencies as focus areas for future regulatory efforts. In horticultural systems, and especially container production of ornamentals, irrigation management is challenging. This is primarily due to the limited volume of water available to container-grown plants after an irrigation event and the resultant need to frequently irrigate to maintain adequate soil moisture levels without causing excessive leaching. To prevent moisture stress, irrigation of container plants is often excessive, resulting in leaching and runoff of water and nutrients applied to the container substrate. For this reason, improving the application efficiency of irrigation is necessary and critical to the long-term sustainability of the commercial nursery industry. The use of soil moisture sensing technology is one method of increasing irrigation efficiency, with the on-farm studies described in this article focusing on the use of capacitance-based soil moisture sensors to both monitor and control irrigation events. Since on-farm testing of these wireless sensor networks (WSNs) to monitor and control irrigation scheduling began in 2010, WSNs have been deployed in a diverse assortment of commercial horticulture operations. In deploying these WSNs, a variety of challenges and successes have been observed. Overcoming specific challenges has fostered improved software and hardware development as well as improved grower confidence in WSNs. Additionally, growers are using WSNs in a variety of ways to fit specific needs, resulting in multiple commercial applications. Some growers use WSNs as fully functional irrigation controllers. Other growers use components of WSNs, specifically the web-based graphical user interface (GUI), to monitor grower-controlled irrigation schedules.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Temporal and Spatial Nearest Neighbor Values Based Missing Data Imputation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1782
Author(s):  
Yulong Deng ◽  
Chong Han ◽  
Jian Guo ◽  
Lijuan Sun
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Missing Data ◽  
Nearest Neighbor ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Temporal And Spatial ◽  
Two Parameters

Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented. First, four nearest neighbor values have been defined from the perspective of space and time dimensions as well as the geometrical and data distances, which are the bases of the algorithm that help to exploit the correlations among sensor data on the nodes with the regression tool. Next, the algorithm has been elaborated as well as two parameters, the best number of neighbors and spatial–temporal coefficient. Finally, the algorithm has been tested on an indoor and an outdoor wireless sensor network, and the result shows that TSNN is able to improve the accuracy of imputation and increase the number of cases that can be imputed effectively.

scholarly journals Performance metrics in wireless sensor networks :a survey and outlook

2018 ◽  
Vol 7 (2.26) ◽  
pp. 25
Author(s):  
E Ramya ◽  
R Gobinath
Keyword(s):  
Wireless Sensor Networks ◽  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Performance Metrics ◽  
Resource Constraints ◽  
Sensor Data ◽  
Wireless Sensor

Data mining plays an important role in analysis of data in modern sensor networks. A sensor network is greatly constrained by the various challenges facing a modern Wireless Sensor Network. This survey paper focuses on basic idea about the algorithms and measurements taken by the Researchers in the area of Wireless Sensor Network with Health Care. This survey also catego-ries various constraints in Wireless Body Area Sensor Networks data and finds the best suitable techniques for analysing the Sensor Data. Due to resource constraints and dynamic topology, the quality of service is facing a challenging issue in Wireless Sensor Networks. In this paper, we review the quality of service parameters with respect to protocols, algorithms and Simulations. 

A Novel Method for Data Calibration in Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 1068-1073
Author(s):  
Wei Min Qi ◽  
Jie Xiao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Measurement Errors ◽  
Information Technologies ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Network Data ◽  
Error Elimination ◽  
Calibration Algorithm ◽  
Data Calibration

In order to provide efficient and suitable services for users in a ubiquitous computing environment, many kinds of context information technologies have been researched. Wireless sensor networks are among the most popular technologies providing such information. Therefore, it is very important to guarantee the reliability of sensor data gathered from wireless sensor networks. However there are several factors associated with faulty sensor readings which make sensor readings unreliable. The research put forward classifying faulty sensor readings into sensor faults and measurement errors, then propose a novel in-network data calibration algorithm which includes adaptive fault checking, measurement error elimination and data refinement. The proposed algorithm eliminates faulty readings as well as refines normal sensor readings and increase reliability. The simulation study shows that the in-network data calibration algorithm is highly reliable and its network overhead is very low compared to previous works.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

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