scholarly journals Precision Farming and Smart Irrigation using IoT

2020 ◽  
Vol 9 (2) ◽  
pp. 1226-1229
Keyword(s):  
Soil Moisture ◽  
User Interface ◽  
Internet Of Things ◽  
Graphical User Interface ◽  
Solenoid Valve ◽  
The Internet ◽  
Home User ◽  
And Control ◽  
The Internet Of Things ◽  
Control Water

Agriculture is the spine of Indian Economy. It mainly depends on Irrigation. The Internet Of Things is used to farmers are easier to monitor and control water possessions. This paper proposed, IoT architecture customized for smart irrigation application. Arduino board is used to communicate a variety of sensors like ultrasonic, soil moistur and light sensors. This work managed to decrease the expenditure, diminish devastate water and diminish substantial individual edge. Relay is developed to organize the switching of solenoid. Also, the scheme preserved to measure the soil moisture. It controls the solenoid valve according to human. Graphical User Interface (GUI) connected withAndroid application to motivate watering movement. SMS alert moreover sent to the home user in critical situations.

scholarly journals AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT

2019 ◽  
Vol 01 (01) ◽  
pp. 38-47 ◽  
Author(s):  
Jennifer S. Raj ◽  
Vijitha Ananthi J
Keyword(s):  
Soil Moisture ◽  
Internet Of Things ◽  
Embedded System ◽  
Climatic Condition ◽  
The Internet ◽  
Green House ◽  
Large Structures ◽  
The Embedded System ◽  
House Automation ◽  
The Internet Of Things

Green house is generally a building of small or large structures. The structure of the green house is made of walls and the translucent roof, with the capability of maintaining the planned climatic condition. It ensures the growth of plants that requires a specified level of soil moisture, sunlight, humidity and temperature. The green house systems available are human monitored systems that entail the continuous human visit causing distress to the worker and also decrease in the yield if the temperature and the humidity are not properly and regularly maintained. This paves way for the concept of the green house automation. The green house automation formed by the incorporation of the Internet of things and the embedded system addresses the problem faced in the green house and provides with the automated controlling and monitoring of the green house environment replacing the undeviating administration of the farmers. This paper also proposes the automation using internet of things in green house environment by using the Netduino 3 and employing the sensors for the sensing the moisture, temperature, sunlight and humidity, to enhance the production rate and minimize the discomfort caused to the farmers.

scholarly journals Management of Software-Defined Networking Powered by Artificial Intelligence

2021 ◽  
Author(s):  
Jehad Ali ◽  
Byeong-hee Roh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Heterogeneous Networks ◽  
Software Defined Networking ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Underlying Network ◽  
And Control ◽  
The Internet Of Things

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

scholarly journals The spectrum of control: A social theory of the smart city

First Monday ◽  
2015 ◽  
Author(s):  
Jathan Sadowski ◽  
Frank Pasquale
Keyword(s):  
Internet Of Things ◽  
Social Theory ◽  
Smart City ◽  
Critical Infrastructure ◽  
The Internet ◽  
Control Mechanisms ◽  
Physical Objects ◽  
Smart Technologies ◽  
And Control ◽  
The Internet Of Things

There is a certain allure to the idea that cities allow a person to both feel at home and like a stranger in the same place. That one can know the streets and shops, avenues and alleys, while also going days without being recognized. But as elites fill cities with “smart” technologies — turning them into platforms for the “Internet of Things” (IoT): sensors and computation embedded within physical objects that then connect, communicate, and/or transmit information with or between each other through the Internet — there is little escape from a seamless web of surveillance and power. This paper will outline a social theory of the “smart city” by developing our Deleuzian concept of the “spectrum of control.” We present two illustrative examples: biometric surveillance as a form of monitoring, and automated policing as a particularly brutal and exacting form of manipulation. We conclude by offering normative guidelines for governance of the pervasive surveillance and control mechanisms that constitute an emerging critical infrastructure of the “smart city.”

scholarly journals Agrocompanion: A Smart Farming Approach Based on Iot and Machine Learning

2020 ◽  
Vol 9 (12) ◽  
pp. 254-262
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Random Forest ◽  
Weather Conditions ◽  
The Internet ◽  
Machine Learning Model ◽  
Smart Farming ◽  
Water Tanks ◽  
And Control ◽  
The Internet Of Things

Agriculture is one of the cardinal sectors of the Indian Economy. The proposed system offers a methodology to efficiently monitor and control various attributes that affect crop growth and production. The system also uses machine learning along with the Internet of Things (IoT) to predict the crop yield. Various weather conditions such as temperature, humidity, and soil moisture are monitored in real-time using IoT sensors. IoT is also used to regulate the water level in the water tanks, which helps in reducing the wastage of water resources. A machine learning model is developed to predict the yield of the crop based on parameters taken from these sensors. The model uses Random Forest Regressor and gives an accuracy of 87.5%. Such a system provides a simple and efficient way to maintain and monitor the health of the crop.

scholarly journals The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges

2020 ◽  
Author(s):  
Tanweer Alam ◽  
Baha Rababah ◽  
Rasit Eskicioglu
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Raw Data ◽  
Next Generation Internet ◽  
Distributed Intelligence ◽  
Research Challenges ◽  
Network Load ◽  
And Control ◽  
Edge Layer ◽  
The Internet Of Things

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.

scholarly journals Smart Monitoring and Control in the Future Internet of Things

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 27
Author(s):  
Franco Cicirelli ◽  
Antonio Guerrieri ◽  
Andrea Vinci
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Future Internet ◽  
The Internet ◽  
Monitoring And Control ◽  
The Future ◽  
And Control ◽  
The Internet Of Things

The Internet of Things (IoT) and related technologies are promising in terms of realizing pervasive and smart applications, which, in turn, have the potential to improve the quality of life of people living in a connected world [...]

scholarly journals IoT Masters Programs

2021 ◽  
pp. 53-57
Author(s):  
М.А. Держо ◽  
М.М. Лаврентьев ◽  
А.В. Шафаренко
Keyword(s):  
Internet Of Things ◽  
Cyber Security ◽  
Information Technologies ◽  
Systems Design ◽  
Team Development ◽  
The Internet ◽  
Development Effort ◽  
Applied Math ◽  
And Control ◽  
The Internet Of Things

В данной работе обсуждаются фундаментальные вопросы разработки программ магистратуры в области Интернета вещей (Internet of Things — IoT). Мы кратко сравниваем предложения Сколтеха и Стэнфорда и утверждаем, что наиболее гибкое решение достигается посредством вводного блока и четырех параллельных потоков учебных курсов: обработка сигналов и управление, обучение машин и искусственный интеллект (ИИ), программирование и схемотехника платформ с применением микроконтроллеров, и, наконец, сети и кибербезопасность. Вводный блок предполагается оснастить достаточным количеством предметов по выбору, чтобы поступающие выпускники бакалавриата из областей прикладной математики, информационных технологий и электроники/телекоммуникаций могли приобрести необходимые знания для освоения потоковых курсов. Мы утверждаем, что еще одним необходимым отличием программы IoT должен явиться междисциплинарный групповой дипломный проект значительного объема, также основанный на потоковых курсах. This paper discusses the fundamentals of postgraduate curriculum development for the area of the Internet of Things (IoT). We provide a brief contrasting analysis of Skoltech and Stanford Masters programs and argue that the most flexible way forward is via the introduction of a leveling-off, elective introductory stage, and four parallel course streams: signal processing and control; Artificial Intelligence (AI), and machine learning; microcontroller systems design; and networks and cyber security. The leveling-off stage is meant to provide sufficient electives for graduates of applied math, Information Technologies (IT), or electronics/telecom degrees to learn the necessary fundamentals for the stream modules. We argue that another distinguishing feature of an IoT masters program is a large project drawing on the stream modules and requiring a multidisciplinary, team development effort.

scholarly journals The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges

2020 ◽  
Author(s):  
Baha Rababah ◽  
Tanweer Alam ◽  
Rasit Eskicioglu
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Raw Data ◽  
Next Generation Internet ◽  
Distributed Intelligence ◽  
Research Challenges ◽  
Network Load ◽  
And Control ◽  
Edge Layer ◽  
The Internet Of Things

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.

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