Development of an IoT system for sensing monitoring of soil properties

2020 ◽  
Author(s):  
Byeongchul Lee ◽  
Kyoung Jae Lim ◽  
Jae E Yang ◽  
Dong Seok Yang ◽  
Jiyoeng Hong
Keyword(s):  
Big Data ◽  
Ecosystem Services ◽  
Soil Properties ◽  
Real Time ◽  
System Analysis ◽  
Vital Role ◽  
Data System ◽  
Soil Sensors ◽  
Soil Ecosystem Services ◽  
Time Basis

<p>In the age of big data, constructing a database plays a vital role in various fields. Especially, in the agricultural and environmental fields, real-time databases are useful because the fields are easily affected by dynamic nature phenomena. To construct a real-time database in these fields, various sensors and an Internet of Things (IoT) system have been widely used. In this study, an IoT system was developed to construct soil properties database on a real-time basis and aim to a big data system analysis that can assess ecosystem services provided from soil resources. The IoT system consisted of three types of soil sensors, main devices, sensor connectors, and subsidiary devices. The IoT system can measure soil temperature, moisture, and electrical conductivity (EC) data on a five-minute interval. Also, the devices were applied to two test-beds near Chuncheon city in South Korea and have been testing for the stability and availability of the system. In a further study, we will add various soil sensors and functions into the developed IoT system to improve their availability. If the developed IoT system becomes to be stable and functional, it can contribute to constructing soil properties database on a real-time basis and a big data system that assesses soil ecosystem services.</p>

scholarly journals Monitoring Water‐Soil Dynamics and Tree Survival Using Soil Sensors under a Big Data Approach

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4634
Author(s):  
Pascual ◽  
Rivera ◽  
Gómez ◽  
Domínguez-Lerena
Keyword(s):  
Big Data ◽  
Soil Water ◽  
Real Time ◽  
Water Dynamics ◽  
Species Selection ◽  
Soil Dynamics ◽  
Small Scale ◽  
Soil Water Dynamics ◽  
Soil Sensors ◽  
Tree Survival

The high importance of green urban planning to ensure access to green areas requires modern and multi-source decision-support tools. The integration of remote sensing data and sensor developments can contribute to the improvement of decision-making in urban forestry. This study proposes a novel big data-based methodology that combines real-time information from soil sensors and climate data to monitor the establishment of a new urban forest in semi-arid conditions. Water‐soil dynamics and their implication in tree survival were analyzed considering the application of different treatment restoration techniques oriented to facilitate the recovery of tree and shrub vegetation in the degraded area. The synchronized data-capturing scheme made it possible to evaluate hourly, daily, and seasonal changes in soil‐water dynamics. The spatial variation of soil‐water dynamics was captured by the sensors and it highly contributed to the explanation of the observed ground measurements on tree survival. The methodology showed how the efficiency of treatments varied depending on species selection and across the experimental design. The use of retainers for improving soil moisture content and adjusting tree-watering needs was, on average, the most successful restoration technique. The results and the applied calibration of the sensor technology highlighted the random behavior of water‐soil dynamics despite the small-scale scope of the experiment. The results showed the potential of this methodology to assess watering needs and adjust watering resources to the vegetation status using real-time atmospheric and soil data.

Integrating Big Data in “e-Oman”: opportunities and challenges

Info ◽  
2016 ◽  
Vol 18 (5) ◽  
pp. 79-97 ◽  
Author(s):  
Stuti Saxena ◽  
Sujeet Kumar Sharma
Keyword(s):  
Big Data ◽  
Real Time ◽  
Design Methodology ◽  
Public Services ◽  
Content Type ◽  
Challenges And Opportunities ◽  
The Government ◽  
Time Basis ◽  
Privacy And Confidentiality ◽  
Practical Implications

Purpose This paper aims to integrate Big Data in e-government in Oman, also known as “e-Oman”, wherein Big Data might be better harnessed to tackle real-time challenges. Design/methodology/approach Besides a description of the concepts of e-government and Big Data in general, the paper underscores the dimensions of “e-Oman”. Following a qualitative approach, the paper asserts how integration of Big Data in “e-Oman” may be useful by invoking examples from four short case studies across different sectors. Findings The paper supports the integration of “e-Oman” and Big Data wherein besides providing smooth public services, the government is encouraged to forge inter- and intra-ministerial collaboration and public-private partnership. The paper probes through the challenges and opportunities in effecting this integration. Practical implications The paper provides a platform for the policymakers to conceive of a synchronized programme for integrating “e-Oman” and the Big Data generated by it. This integration would go a long way in building upon the economy of Oman, besides providing better public services to the individuals and businesses on a real-time basis. Social implications The paper does throw light on the issues of privacy and confidentiality of data available with the government. There are challenges of cybercrime as well. Therefore, the paper posits that a robust fool-proof infrastructure should be instituted by the government for effecting integration of e-government and Big Data. Originality/value This paper seeks to fill the gap in extant literature which remains scant on the integration of e-government with Big Data. This is especially true in the case of Oman where not a single study has been presented to probe this issue. Given that “e-Oman” is expanding its scope over the years, this paper foresees the concomitant opportunities and challenges in the integration of Big Data in “e-Oman”.

Real-Time Monitoring System for Sustainable Domestic Electric Power Consumption

2014 ◽  
Vol 699 ◽  
pp. 816-821
Author(s):  
Mohamad Riduwan Md Nawawi ◽  
Johar Akbar Mohamat Gani ◽  
Mohd Ruzaini Hashim ◽  
Mohanraj Letchimenan ◽  
Sundram Ramahlingam
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Real Time ◽  
Monitoring System ◽  
Vital Role ◽  
Real Time Monitoring ◽  
Display Unit ◽  
Communication Device ◽  
Time Basis ◽  
Hourly Basis

Utility monitoring system plays a vital role for consumers in domestic areas to identify their electric power usage, thus creates awareness, motivates and educates consumers about saving power consumption. This paper develops a monitoring system for electric power usage, designed consisting of a digital meter, display unit, microcontroller, auto-generated data-based logging and communication device. The system is in real-time structure which measures the power usage by hourly basis and displays the results in kWh every time in convenient and organized manner. The data-based logging feature enables utility usage information to be recorded in timely basis. An additional feature provided in this system is a notification module that send charges information corresponding to consumer’s utility usage. Consumers are able to be aware of the utility usage with the corresponding charges at real-time basis as well as capable to predict, control and manage their utility usage on daily manner. Eventually this system is beneficial for both parties which are the consumers and the utility provider.

Development of a big data system to assess ecosystem services of surface soil

2020 ◽  
Author(s):  
Kyoung Jae Lim ◽  
Dongjun Lee ◽  
Jonggun Kim ◽  
Jae E Yang ◽  
Minhwan Shin
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Processing ◽  
Data Acquisition ◽  
Soil Properties ◽  
Data Storage ◽  
Surface Soil ◽  
Data System ◽  
Environmental Model ◽  
Data Set

<p>A big data system plays a significant role in various fields. This technology has also been applied to environment fields because it can discover hidden patterns between environmental factors. As the massive data set was constructed for several decades, big data analysis has widely been using for extracting useful information by analyzing different types of big data sets. In this study, we developed a big data system frame to assess the ecosystem service provided from surface soil. Among big data platforms, we used the Amazon Web Service (AWS) due to their cost-efficiency and hardware flexibility. There are five stages of the big data system (i.e. data acquisition– data storage – data processing – data analysis – visualization). In the data acquisition step, the soil sensor and Internet of Things (IoT) system were used, and we collected existing soil properties data provided by national institutes such as Rural Development Administration (RDA), Ministry of Environment (MOE), and Ministry of Land, Infrastructure, and Transport (MOLIT). AWS S3 platform, which is an object storage service and provides easy-to-use management features to users, was accepted as the data storage platform of the big data system. Amazon EMR, Amazon SageMaker, and Amazon QuickSight were used for the step of data processing, data analysis, and visualization of the big data system respectively. We tested that the developed system could predict soil bulk density and able to replace a typical environmental model by using models based on machine learning and deep learning. The results of the two tests showed positive results that the developed models could predict soil properties and simulate natural phenomena as much as the typical environmental model could.  However, since the system is at an early development stage, it needs repetitive tests in the future considering various soil properties. If this system becomes fully functional, the system will be helpful to improve soil environments.</p>

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