When One Wireless Technology is Not Enough: A Network Architecture for Precision Agriculture Using LoRa, Wi-Fi, and LTE

2022 ◽  
pp. 103-112
Author(s):  
Jose A. Brenes ◽  
Gabriela Marín-Raventós
Keyword(s):  
Precision Agriculture ◽  
Network Architecture ◽  
Wireless Technology

Investigation of precision agriculture using wireless sensor network architecture

2022 ◽  
Author(s):  
K. Sakthisudhan ◽  
P. Sivakamasundari ◽  
M. Revathi ◽  
R. Shamini ◽  
K. Paul Joshua ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Precision Agriculture ◽  
Network Architecture ◽  
Wireless Sensor

scholarly journals Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves

2020 ◽  
Vol 2 (1) ◽  
pp. 175-205 ◽  
Author(s):  
Athanasios Tsipis ◽  
Asterios Papamichail ◽  
George Koufoudakis ◽  
Georgios Tsoumanis ◽  
Spyros E. Polykalas ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Precision Agriculture ◽  
Network Architecture ◽  
Low Cost ◽  
Fog Computing ◽  
Environmental Parameters ◽  
Traffic Load ◽  
Agricultural Activity ◽  
Computing Paradigm ◽  
Olive Groves

The emerging and vast adoption of the Internet of Things (IoT) has sprung a plethora of research works regarding the potential benefits in smart agriculture. A popular implementation involves the deployment of Wireless Sensor Networks (WSNs), which embed low energy consumption sensory nodes to capture the critical environmental parameters prevailing on the farms. However, to manage the ever-increasing volumes of raw data successfully, new approaches must be explored. Under this scope, current work reports on the design and development of an IoT system, having in mind the case of olive groves, which are considered the dominant sector for agricultural activity in the Mediterranean Basin. The system incorporates the cloud/fog computing paradigm to equip the olive growers with a low-cost solution for accurate, reliable, and almost real-time monitoring of their crops. Its core is based on a three-layered network architecture, capable of dynamically balancing the generated load, by pushing cloud-elastic resources to the underlying fog network. As such, the premise of the approach lies in the conforming character of the system that allows for targeted alterations to its operational functionality to meet stringent latency and traffic load environmental monitoring constraints. To evaluate the performance of the proposed architecture, a demo prototype is developed and deployed in the facilities of the Ionian University. Experimental results illustrate the efficiency, flexibility, and scalability of the approach in terms of latency, achieving response time reduction across all platforms, a subject of the utmost importance when it comes to precision agriculture of the future. Moreover, it is shown that the system is capable of dynamic functionality adaptation, to meet network traffic load constraints, achieving high throughput (on average 95%) and addressing potential environmental dangers to olive oil production.

Wireless technology and satellite internet access for high-speed whole farm connectivity in precision agriculture

2004 ◽  
Vol 81 (3) ◽  
pp. 201-212 ◽  
Author(s):  
J.M McKinion ◽  
S.B Turner ◽  
J.L Willers ◽  
J.J Read ◽  
J.N Jenkins ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
High Speed ◽  
Internet Access ◽  
Wireless Technology

Extending the RPL Routing Protocol to Agricultural Low Power and Lossy Networks (A-LLNs)

2013 ◽  
Vol 4 (4) ◽  
pp. 25-47 ◽  
Author(s):  
Yibo Chen ◽  
Jean-Pierre Chanet ◽  
Kun Mean Hou ◽  
Hong Ling Shi
Keyword(s):  
Low Power ◽  
Routing Protocol ◽  
Precision Agriculture ◽  
Network Architecture ◽  
Web Application ◽  
Hybrid Network ◽  
Lossy Networks ◽  
Industrial Control ◽  
Ongoing Work ◽  
New Vision

The routing protocol for low power and lossy network (RPL) started to be designed by the ROLL working group of IETF since the year of 2008. Until the RFC6550 was released, this standard with its routing algorithms and four application scenarios, such as home and building automation, industrial control, and urban environment, have been grounded. As a main jigsaw of the paradigm of the Internet of Things (IoT), RPL plays the major role and has become an impressed technical tendency in the field of wireless communication. However, it is still very difficult to find effective approaches to simulate and evaluate RPL’s behaviors and other extensions of its applicability, especially in the domain of precision agriculture. Notice that wireless sensor network (WSN) has been deployed a wide variety of wireless sensing devices, and should be one valued supported part of the promising IoT ecosystem. In this paper, first the authors provide a brief presentation of the related protocols including their standardization, the existing implementations, and a group of simulation experiment results obtained from the RPL capable COOJA simulator with its developed modules. Second, the authors then focus on the utilization of this protocol in the agricultural low power and lossy network (A-LLN) area and propose their dedicated instances hybrid network architecture to meet its specific requirement. Moreover, the Web of things (WoT), a trend and new vision of IoT, is appended in the authors’ proposal to provide a novel dimension in design of A-LLN since it enables a full interoperability with current web application and higher efficiency of development. As a conclusion, the authors summarized their ongoing work and future solutions of the current technology issues.

scholarly journals 5G Technology Evolutions

2021 ◽  
Author(s):  
Shreyas Joshi ◽  
Sohan Zadbuke ◽  
Nagesh Kumbar ◽  
Sanket Malshette ◽  
Vrushali Gurav
Keyword(s):  
Mobile Networks ◽  
Network Architecture ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Wireless Technology ◽  
Wireless Internet ◽  
Mobile Wireless ◽  
Wireless Technologies ◽  
Radio Access Technology ◽  
Mobile Wireless Technology

5G technology has number of existing generations of wireless technologies in terms of their portal, efficiency, effectiveness, and cost-benefit analysis. The paper puts a focus on the evolution and development of various generations of mobile wireless technology along with their significance and advantages of one over the other. From past several decades, mobile wireless technologies have experience 4 or 5 generations of technology revolution and evolution, namely from 1G to 4G. The Current research in mobile wireless technology concentrates on promoting the implementation of LTE technology and 5G technology. At present, the term is not officially used. In 5G, the research on the development of World Wide Wireless internet access (WWW), Dynamic adhoc Wireless Networks (DAWN) and Real Wireless World. In this paper, we focusa new network architecture for the next-generation of mobile networks, 5G. In this architecture, the mobile device will have the ability to get to the Radio Access Technology - RAT based on certain user-defined criteria.

Environmental Benefits of Precision Agriculture Adoption

2020 ◽  
pp. 637-656 ◽  
Author(s):  
Marco Medici ◽  
Søren Marcus Pedersen ◽  
Giacomo Carli ◽  
Maria Rita Tagliaventi
Keyword(s):  
Environmental Impacts ◽  
Precision Agriculture ◽  
New Technologies ◽  
Environmental Benefits ◽  
Herbicide Application ◽  
Pesticide Application ◽  
Policy Makers ◽  
Application Rates ◽  
Biodiversity Policy ◽  
Lime Application

The purpose of this study is to analyse the environmental benefits of precision agriculture technology adoption obtained from the mitigation of negative environmental impacts of agricultural inputs in modern farming. Our literature review of the environmental benefits related to the adoption of precision agriculture solutions is aimed at raising farmers' and other stakeholders' awareness of the actual environmental impacts from this set of new technologies. Existing studies were categorised according to the environmental impacts of different agricultural activities: nitrogen application, lime application, pesticide application, manure application and herbicide application. Our findings highlighted the effects of the reduction of input application rates and the consequent impacts on climate, soil, water and biodiversity. Policy makers can benefit from the outcomes of this study developing an understanding of the environmental impact of precision agriculture in order to promote and support initiatives aimed at fostering sustainable agriculture.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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