A Nearest Neighbors based Data Filter for Fog Computing in IoT Smart Agriculture

2020 ◽  
Author(s):  
Franklin Magalhaes Ribeiro ◽  
Ronaldo Prati ◽  
Reinaldo Bianchi ◽  
Carlos Kamienski
Keyword(s):  
Fog Computing ◽  
Nearest Neighbors ◽  
Smart Agriculture

scholarly journals An Integrated Heterogeneous Smart Agriculture System

2019 ◽  
Vol 8 (4) ◽  
pp. 7562-7564
Keyword(s):  
Energy Consumption ◽  
Thermal Imaging ◽  
Energy Efficient ◽  
Wireless Sensors ◽  
Fog Computing ◽  
Irrigation Scheduling ◽  
Integrated System ◽  
Cloud Server ◽  
Smart Agriculture ◽  
Agricultural Regions

It is very essential to use smart agriculture in present days. This will solve various issues occur in agriculture. With Internet of Things (IoT), wireless sensors and fog computing an integrated system providing the smart agriculture is running in present villages. Various issues are identified on this smart agriculture. Parameters such as irrigation scheduling and inefficient utilization of water resources are two of several ubiquitous parameters restricting production in many agricultural regions. To solve these issues, energy consumption of the sensors plays major role to send and receive the data on various parameters. In this paper, an integrated energy efficient sensors by using thermal imaging to maintain the constant data flow from sensors to fog and cloud server.

scholarly journals Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture

2020 ◽  
Vol 10 (4) ◽  
pp. 1544 ◽  
Author(s):  
Kyuchang Lee ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Fog Computing ◽  
Multimedia Data ◽  
Network Congestion ◽  
Service Delay ◽  
Comparison Results ◽  
Smart Agriculture ◽  
Iot Devices ◽  
Computing Capacity

Colossal amounts of unstructured multimedia data are generated in the modern Internet of Things (IoT) environment. Nowadays, deep learning (DL) techniques are utilized to extract useful information from the data that are generated constantly. Nevertheless, integrating DL methods with IoT devices is a challenging issue due to their restricted computational capacity. Although cloud computing solves this issue, it has some problems such as service delay and network congestion. Hence, fog computing has emerged as a breakthrough way to solve the problems of using cloud computing. In this article, we propose a strategy that assigns a portion of the DL layers to fog nodes in a fog-computing-based smart agriculture environment. The proposed deep learning entrusted to fog nodes (DLEFN) algorithm decides the optimal layers of DL model to execute on each fog node, considering their available computing capacity and bandwidth. The DLEFN individually calculates the optimal layers for each fog node with dissimilar computational capacities and bandwidth. In a similar experimental environment, comparison results clearly showed that proposed method accommodated more DL application than other existing assignment methods and utilized resources efficiently while reducing network congestion and processing burden on the cloud.

scholarly journals Image Annotation via Reconstitution Graph Learning Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shi Chen ◽  
Meng Wang ◽  
Xuan Chen
Keyword(s):  
Data Mining ◽  
Image Annotation ◽  
Nearest Neighbors ◽  
Previous Method ◽  
Semantic Gap ◽  
Graph Learning ◽  
Neighbor Graph ◽  
Mining Methods ◽  
Smart Agriculture ◽  
Better Than

With great developments of computing technologies and data mining methods, image annotation has attracted much attraction in smart agriculture. However, the semantic gap between labels and images poses great challenges on image annotation in agriculture, due to the label imbalance and difficulties in understanding obscure relationships of images and labels. In this paper, an image annotation method based on graph learning is proposed to accurately annotate images. Specifically, inspired by nearest neighbors, the semantic neighbor graph is introduced to generate preannotation, balancing unbalanced labels. Then, the correlations between labels and images are modeled by the random dot product graph, to deeply mine semantics. Finally, we perform experiments on two image sets. The experimental results show that our method is much better than the previous method, which verifies the effectiveness of our model and the proposed method.

Sustainable Green Fog Computing for Smart Agriculture

2021 ◽  
Author(s):  
Rehan Qureshi ◽  
Syed Haris Mehboob ◽  
Muhammad Aamir
Keyword(s):  
Fog Computing ◽  
Smart Agriculture

scholarly journals Implementasi Fog Computing Pada Aplikasi Smart Home Berbasis Internet of Things

2021 ◽  
Vol 6 (1) ◽  
pp. 127
Author(s):  
Ahmad Zainudin ◽  
Ida Anisah ◽  
Melki Mario Gulo
Keyword(s):  
Cloud Computing ◽  
Face Recognition ◽  
Internet Of Things ◽  
Smart Home ◽  
Fog Computing ◽  
Smart Factory ◽  
Smart Agriculture

Teknologi Internet of Things (IoT) saat ini terus berkembang dan manfaatnya sudah mulai bayak dirasakan oleh sebagian besar masyarkat. Beberapa aplikasi IoT seperti smart home, smart factory, smart agriculture. Pada implementasi sistem IoT diperlukan perangkat yang berfungsi untuk mengumpulkan dan memproses beberapa jenis data. Sehingga diperlukan sebuah resouce yang handal yang sering disebut dengan cloud computing. Cloud computing merupakan pusat data yang terpusat. Karena jarak yang jauh maka menjadi kelemahan untuk beberapa aplikasi yang sensitif terhadap waktu. Pada penelitian ini akan dikembangkan sebuah sistem fog computing pada internet of things services pada untuk aplikasi smart home. Berdasarkan hasil pengujian didapatkan waktu proses komputasi pada aplikasi monitoring suhu dan kelebaban sebesar 0,152 detik, pada aplikasi pengaturan dimmer lampu sebesar 0,339 detik dan apada aplikasi face recognition sebesar 6,602 detik.

A Multi-Layered framework for Internet of Everything (IoE) via Wireless Communication and Distributed Computing in Industry 4.0

2019 ◽  
Vol 13 ◽  
Author(s):  
Srinidhi Hiriyannaiah ◽  
L M Patnaik ◽  
Sidddesh G M ◽  
Srinivasa K G
Keyword(s):  
Smart Grid ◽  
Industry 4.0 ◽  
Oil And Gas ◽  
Fog Computing ◽  
Industrial Applications ◽  
Smart Factory ◽  
Industrial Evolution ◽  
Layered Architecture ◽  
Industrial Internet ◽  
Smart Agriculture

: In the recent years, due to the proliferation of internet and smart computing there has been progress in the areas of energy, manufacturing, oil and gas, smart grid and other industrial applications. The progress in these areas is due to the advances in the emerging areas such as Big data, cloud computing, fog computing and wireless networks. The advancement in these areas has paved the way for new industrial evolution, Industry 4.0 or smart factory or Industrial internet of things (IIoT). IIoT is made up of a collection of devices, with each device being able to monitor, collect, exchange, analyze and take decisions intelligently based on the environment through proper communication channels. IIoT consists of smart solutions in the fields of electricity (smart-grid), home automation (smart-home), supply-chains (smart-automation) and agriculture (smart-agriculture). The interconnection among the different smart components of IIoT is a key requirement for IIoT systems. In this paper, a novel three-layered architecture is proposed to handle the heterogeneity of components of IIoT and thus providing a way for digital transformation of IIoT systems. The paper also provides an overview of underlying components, principles, communication standards and enabling technologies for IIoT or smart factory.

Observation of Superlattice Dislocations on Cube Planes in Ni3Al

1973 ◽  
Vol 31 ◽  
pp. 174-175 ◽  
Author(s):  
J. M. Oblak ◽  
W. H. Rand
Keyword(s):  
Nearest Neighbor ◽  
Antiphase Boundary ◽  
Nearest Neighbors ◽  
High Energy ◽  
Cross Slip ◽  
Octahedral Plane ◽  
Trapping Mechanism ◽  
Slip Traces

The energy of an a/2 <110> shear antiphase. boundary in the Ll2 expected to be at a minimum on {100} cube planes because here strue ture is there is no violation of nearest-neighbor order. The latter however does involve the disruption of second nearest neighbors. It has been suggested that cross slip of paired a/2 <110> dislocations from octahedral onto cube planes is an important dislocation trapping mechanism in Ni3Al; furthermore, slip traces consistent with cube slip are observed above 920°K.Due to the high energy of the {111} antiphase boundary (> 200 mJ/m2), paired a/2 <110> dislocations are tightly constricted on the octahedral plane and cannot be individually resolved.

Probing vacancies and structural distortions at individual defects in ceramics using EELS

1996 ◽  
Vol 54 ◽  
pp. 678-679
Author(s):  
D. J. Wallis ◽  
N. D. Browning
Keyword(s):  
Structural Information ◽  
Core Loss ◽  
Nearest Neighbors ◽  
Ceramic Materials ◽  
Scanning Transmission Electron Microscope ◽  
Real Space ◽  
Atomic Scale ◽  
Structure Property ◽  
Scanning Transmission ◽  
Key Issues

In electron energy loss spectroscopy (EELS), the near-edge region of a core-loss edge contains information on high-order atomic correlations. These correlations give details of the 3-D atomic structure which can be elucidated using multiple-scattering (MS) theory. MS calculations use real space clusters making them ideal for use in low-symmetry systems such as defects and interfaces. When coupled with the atomic spatial resolution capabilities of the scanning transmission electron microscope (STEM), there therefore exists the ability to obtain 3-D structural information from individual atomic scale structures. For ceramic materials where the structure-property relationships are dominated by defects and interfaces, this methodology can provide unique information on key issues such as like-ion repulsion and the presence of vacancies, impurities and structural distortion.An example of the use of MS-theory is shown in fig 1, where an experimental oxygen K-edge from SrTiO3 is compared to full MS-calculations for successive shells (a shell consists of neighboring atoms, so that 1 shell includes only nearest neighbors, 2 shells includes first and second-nearest neighbors, and so on).

Bringing the Concept of Climate-Smart Agriculture to Life

10.1596/31064 ◽  
2018 ◽  
Author(s):  
Chase Anthony Sova ◽  
Godefroy Grosjean ◽  
Tobias Baedeker ◽  
Tam Ninh Nguyen ◽  
Martin Wallner ◽  
...  
Keyword(s):  
Smart Agriculture
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