Multilevel data fusion for the internet of things in smart agriculture

2020 ◽  
Vol 171 ◽  
pp. 105309 ◽  
Author(s):  
Andrei B.B. Torres ◽  
Atslands R. da Rocha ◽  
Ticiana L. Coelho da Silva ◽  
José N. de Souza ◽  
Rubens S. Gondim
2014 ◽  
Vol 17 (11) ◽  
pp. 1313-1324 ◽  
Author(s):  
Joonyoung Lee ◽  
ShinHo Kim ◽  
SaeBom Lee ◽  
HyeonJin Choi ◽  
JaiJin Jung

2021 ◽  
Author(s):  
O. Vishali Priya ◽  
R. Sudha

In today’s world, technology is constantly evolving; various instruments and techniques are available in the agricultural field. And within the agrarian division, the IoT preferences are Knowledge processing. With the help of introduced sensors, all information can be gathered. The reduction of risks, the mechanization of industry, the enhancement of production, the inspection of livestock, the monitoring of environment conditions, the roboticization of greenhouses, and crop monitoring Nearly every sector, like smart agriculture, has been modified by Internet-of-Things (IoT)-based technology, which has shifted the industry from factual to quantitative approaches. The ideas help to link real devices that are equipped with sensors, actuators, and computing power, allowing them to collaborate on a task while staying connected to the Internet, dubbed the “Internet of Things” (IoT). According to the World Telecommunication Union’s Worldwide Guidelines Operation, the Internet of Things (IoT) is a set of sensors, computers, software, and other devices that are connected to the Internet. The paper is highly susceptible to the consequences of its smart agriculture breakthrough.


2017 ◽  
Vol 13 (11) ◽  
pp. 25
Author(s):  
Jie Zhang

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">In order to prove the effect of data fusion technology in the Internet of things, a wireless sensor Internet of things security technology based on data fusion is designed, and the impact of data fusion in the field of communication technology is studied. Therefore, two security fusion algorithms are designed on the basis of analyzing and comparing the advantages and disadvantages of various security fusion algorithms, namely, data security fusion algorithm EDCSDA and approximate fusion algorithm PADSA. By analyzing the probability distribution model of the data collected by the nodes, the disturbance data is superimposed on the original data to hide the effect of the original data. A test bed system for perception layer of the Internet of things is designed and implemented. The test results prove the feasibility of the two algorithms. Meanwhile, it shows that the two algorithms can reduce the transmission overhead of the network while guaranteeing the security. Based on the above finding, it is concluded that data fusion technology is very effective for improving network efficiency and prolonging the network life cycle as one of the key technologies in the perception layer of Internet of things.</span>


2013 ◽  
Vol 760-762 ◽  
pp. 587-591 ◽  
Author(s):  
Xu Ping Zhu

The Internet of Things is a bearer network based on the Internet, the traditional telecommunications network, wireless self-organizing networks etc, so that all can be individually addressable ordinary physical objects to achieve the interconnection network. The Internet of Things is evolved from the wireless sensor network, which has limited resource in energy, memory, calculation, bandwidth and etc,. In addition, many applications in the Internet of Things are related to user privacy. In the process of data collection and transmission, the data may be forged , tampered, and a variety of other information security threats. In order to extend the network lifetime, to protect the authenticity and reliability of data fusion, this paper presents a reputation model of data fusion algorithm. The algorithm is verified by simulation, the experimental results show that the proposed algorithm is effective and the result of data aggregation is reliable.


In earlier times planters utilized to figure the perfection of soil as well as influenced uncertainties to establish which to type of turnout. They failed to consider the humidity, degree of water and especially weather disorder which horrible a farmer significantly the Internet of things (IOT) is remodelling the agri-business enabling the agriculturists with the significant stable of approaches. IOT is actually extended with actuators as well as sensing units. The principal aim is to gather the analyses coming from various nodules and assist the planters deal with different operations for wise planters providing a clever agrarian area.


2021 ◽  
Author(s):  
Tsitsi Zengeya ◽  
Paul Sambo ◽  
Nyasha Mabika

Zimbabwe has faced severe droughts, resulting in low agricultural outputs. This has threatened food and nutrition security in community sections, especially in areas with low annual rainfall. There is a growing need to maximize water usage, monitor the environment and nutrients, and temperatures by the adaptation of smart agriculture. This research explored the use of the Internet of Things (IoT) for smart agriculture in Zimbabwe to improve food production. The mixed methodology was used to gather data through interviews from 50 purposively sampled A2 farmers in the five agricultural regions of Zimbabwe and was supported by the use of the Internet. The findings reveal that some farmers have adopted IoT in Zimbabwe, others are still to adopt such technology and some are not aware of the technology. IoT’s benefits to Zimbabwean farmers are immense in that it improves food security, water preservation, and farm management. However, for most farmers to benefit from IoT, more awareness campaigns should be carried out and mobile and fixed Internet connectivity improved in some of the areas.


Author(s):  
K. Vikranth ◽  
Krishna Prasad K.

India is a country that depends on agriculture, where about half the population relies heavily on agriculture for their livelihood. However, most of the practices undertaken in the agricultural process are not for profit and yield favorable. It should upgrade with current technologies to boost seed quality, check soil infertility, check the water level, environmental changes, and market price prediction, and achieve in agriculture sensitivity of faults and background understanding. The advancement in technology and developments is seen as a significant aspect in their financial development and agricultural production growth. The Internet of Things (IoT), Wireless Sensor Networks (WSN), and data analytics accomplish these upgrades. These technologies help in providing solutions to agricultural issues such as resource optimization, agricultural land monitoring, and decision-making support, awareness of the crop, land, weather, and market conditions for farmers. Smart agriculture is based on data from sensors, data from cloud platform storage and data from databases, all three concepts need to be implemented. The data are collected from different sensors and stored in a cloud-based back end support, which is then analyzed using proper analytics techniques, and then the relevant information is transferred to a user interface, which naturally supported the decision to conclude. The IoT applications mainly use sensors to monitor the situation, which collects a large size of data every time, so in the case of the Internet of Things (IoT) application, sensors contribute more. Data analytics requires data storage, data aggregation, data processing and data extraction. To retrieve data and information from database, we must use data mining techniques. It acts a significant position in the selection-making process on several agricultural issues. The eventual objective of data mining is to acquire information form data transform it for some advanced use into a unique human-comprehensible format. Big data's role in Agriculture affords prospect to increase the farmers' economic gain by undergoing a digital revolution in this aspect that we examine with precision. This paper includes reviewing a summary of some of the conference papers, journals, and books that have been going in favor of smart agriculture. The type of data required for smart farming system are analyzed and the architecture and schematic diagram of a proposed intelligent farming system are included. It also involves implementing different components of the smart farming system and integrating IoT and data analytics in the smart farming system. Based on the review, research gap, research agendas to carry out further research are identified.


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