Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR

A Distributed Data Mesh Paradigm for an Event-based Smart Communities Monitoring Product

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

This paper presents a smart water pipeline monitoring system to control the water leakages occurring in it. In day by day life, usage of water is increasing with This paper presents a smart water pipeline monitoring proportional to increase in wastage of water. So, to overcome from this, a smart monitoring system with the help of Internet of things(IOT) is designed and proposed. In this modern era, usages and advantages of IOT are immeasurable. There are a lot of sensors are available in the market to measure the water flow. In this system ,to monitor the water quality ,we are using a pH sensor.ESP32 sensor is used in this system. The main purpose of this microcontroller used is because of its interrupt pins. the values measured by the remaining sensors are stored in the server. with the measured the moisture sensor, urbidity sensor are displayed on the LCD Keywords: Turbidity, PHsensor,DHT11, ESP32,LCD

Sensor‐based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning‐based methods. To overcome these limitations, this research is aimed at proposing an IoT‐based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method‐convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera‐mounted mobile robot captured images from the agriculture field for which the proposed IoT‐based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT‐based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops.

This paper presents a smart water pipeline monitoring system to control the water leakages occurring in it. In day by day life, usage of water is increasing with proportional to increase in wastage of water. So, to overcome from this, a smart monitoring system with the help of Internet of Things (IoT) is designed and proposed. In this modern era, usages and advantages of IoT are immeasurable. There are a lot of sensors are available in the market to measure the water flow [2]. In this system, to monitor the flow of water, the water flow sensor is used in the pipeline and also to measure the contamination of water a turbidity sensor has been used. Flow sensor works on the principle of a hall effect [5]. Nodemcu microcontroller, is one of the most common microcontrollers used for IoT purposes has been used in this system [8]. Main purpose of this microcontroller used is because of its interrupt pins. The values measured by the water flow sensor and turbidity sensor are uploaded to the cloud server. For storing the data in the cloud, the ThingSpeak cloud server has been used for this system, because ThingSpeak cloud server is open and free to use. With the values measured by the water flow sensor the data is displayed in the ThingSpeak cloud webserver. So, monitoring of the water flow in the pipeline will be done very easily.

Workplace Monitoring and Technology aims to showcase results of research and explanatory theories that influence employees' acceptance of the fact that work is monitored using ICT-based monitoring tools. Work monitoring, understood as obtaining, storing and reporting the results of collected observations, has always been a managerial task. Traditionally it was carried out by supervisors who, while overseeing the work of employees, would draw conclusions from their observations and implement corrective actions. The use of information and communication technologies (ICT) to monitor the working employee and their performance has changed the methods of monitoring, and the popularization of remote work has increased interest in searching for new monitoring systems using the full potential of new ICT solutions. The new developments in ICT have caused smart monitoring systems and new solutions to evolve in electronic work monitoring based on the Internet of Things and Artificial Intelligence, which enables nearly cost-free monitoring. However, scientific knowledge about them is limited, and above all, so is managerial knowledge about the reception of these tools by employees, while their misuse can cause considerable damage. Presenting a broad overview of the current state of different areas of scientific knowledge regarding smart and electronic monitoring systems of work performance, this book will be of relevance for academics within the fields of human resource management and performance management, and for similar groups of researchers in psychology and sociology.

This paper presents a smart water pipeline monitoring system to control the water leakages occurring in it. In day by day life, usage of water is increasing with proportional to increase in wastage of water. So, to overcome from this, a smart monitoring system with the help of Internet of Things (IoT) is designed and proposed. In this system, to monitor the flow of water, the water flow sensor is used in the pipeline and also to measure the contamination of water a turbidity sensor has been used. Flow sensor works on the principle of a hall effect. Nodemcu microcontroller, is one of the most common microcontrollers used for IoT purposes has been used in this system. Main purpose of this microcontroller used is because of its interrupt pins. The values measured by the water flow sensor and turbidity sensor are uploaded to the cloud server. For storing the data in the cloud, the ThingSpeak cloud server has been used for this system, because ThingSpeak cloud server is open and free to use. With the values measured by the water flow sensor the data is displayed in the ThingSpeak cloud webserver. The system has a water flow sensor, a microcontroller to interpret the data for evaluating the leakage content and to store the data in the cloud. So, monitoring of the water flow in the pipeline will be done very easily.

Precision agriculture is a modern farming practice that makes production more efficient. It can help determine everything from what factors may be stressing a crop at a specific point in time to estimating the amount of moisture in the soil. One important aspect in precision agriculture is precision irrigation. This paper provides the design and implementation of a Smart Precision Irrigation and Monitoring System which uses Microsoft Azure along with Internet of Things technologies to provide for automatic precision irrigation. Sensors are used to collect water level, temperature, humidity and soil moisture data and Azure Cloud services are utilized to perform real-time analytics on the data obtained. A Web App and a Mobile App have been implemented for the farmer to manage the system, control the automatic and manual irrigation processes and receive important notifications. Azure Machine Learning has also been used to generate the chance of rain, hence facilitating the decision-making process of the farmer.

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime. Here, a conceptual framework of the proposed scheme and statistical design model has been well defined. The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization. The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%, reliability ratio of 98.63%, a coverage ratio of 99.67%, least sensor error rate of 8.96%, and efficient energy consumption ratio of 4.84%.

Air pollution, water pollution, and radiation pollution are significant environmental factors that need to be addressed. Proper monitoring is crucial with the goal that by preserving a healthy society, the planet can achieve sustainable development. With advancements in the internet of things (IoT) and the improvement of modern sensors, environmental monitoring has evolved into a smart environment monitoring (SEM) system in recent years. This article aims to have a critical overview of significant contributions and SEM research, which include monitoring the quality of air , water pollution, radiation pollution, and agricultural systems. The review is divided based on the objectives of applying SEM methods, analyzing each objective about the sensors used, machine learning, and classification methods. Moreover, the authors have thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring system.

The acquisition of vast volumes of health-related data using IoT-enabled smart sensing approaches is well adapted. Analyzing this rich data using machine learning techniques can augment conventional healthcare systems by improving diagnosis accuracy, finding newer cures for diseases, and aid in remote healthcare. Integration of IoT and machine intelligence can alleviate the pressure on the healthcare system as an alternative for providing affordable healthcare to patients. Implementing appropriate learning techniques is key to addressing the problem of sensor data integration and analysis in such systems. Thus, in this chapter, IoT-Based smart and intelligent remote healthcare monitoring applications and systems from a generalized perspective are reviewed, with a particular focus on the correlation required between different machine learning approaches and pertinent data that can be utilized towards the development of an effective health monitoring systems. This chapter aims to provide researchers with a detailed review and guidelines to be considered in solving novel challenges towards the development of IoT-enabled smart health management system for breakthroughs towards affordable medical diagnosis and healthcare applications.

Public speaking is the most common form of fear, and everyone feels uneasy with it. Fear of speaking in public is commonly called “glossophobia,” where people are discouraged from speaking in front of people due to embarrassment and rejection. Public speaking anxiety (PSA) is one of the most universal subtypes of anxiety where people fear, lose their confidence, and become uncomfortable physically and mentally. But public speaking is considered important in the educational sector and workplaces, where people get higher opportunities. Therefore, clubs like Toastmasters help people overcome their fear of public speaking and improve their confidence. We are launching the idea of a Smart Monitoring and Reporting Toastmasters System for people to improve their public speaking so they do not need a supervisor or mentor to train them. This smart monitoring system recognizes the candidate through image processing and deep learning. Moreover, this will analyze some features from the candidates’ speeches, such as facial emotion recognition, speech recognition, hand and body gesture recognition, and the candidates’ attire and appearance separately. This system will identify their mistakes and flaws and provide overall feedback to the users on the speech provided by the candidate. By implementing this web application, users can train themselves without a supervisor, and they can improve themselves and gain the confidence to participate in a Toastmasters competition as perfect candidates.

We present our experience of broadband seismic equipment application to detect changes of roadbed railway state in operation.
 Aim: to obtain experimental validation to expand the scientific basis of geophysical surveys in roadbed railway state monitoring and identifying hazardous processes at an early stage.
 Methods: seismic records were obtained by the authors on the Northern railway section. Seismic data were processed using a low-frequency filter of 0.5 Hz and analysis of points movement trajectories of the roadbed railway, we performed numerical modelling of the stress-strain state of the soil when specifying various physical parameters, the results were compared with the experiment.
 Results: We have shown experimentally and by numerical simulation that by conducting near the railway track vibrations registration formed by passing trains and analyzing the amplitudes change of these vibrations in the low-frequency band (periods of about 100 seconds), it is possible to identify potentially dangerous phenomena in the roadbed. Field observations showed changes in amplitude of oscillations during seasonal flooding; the results are consistent with modeling. The possibility of identifying the stages of freezing-thawing of the soil and associated negative processes is considered. By discussing the parameters of modern seismic equipment, the possibilities and applicability of different types of seismic sensors for roadbed railway monitoring as well as the organization of a smart monitoring system are shown.
 Conclusion: The possibility of using passing trains vibrations to detect changes of the roadbed railway state by analyzing the amplitudes of these vibrations is proved. Recommendations for seismic sensors choice and organization of smart roadbed state monitoring system are presented.

Assessment of factors affecting implementation of IoT based smart skin monitoring systems

This paper implements a smart loading monitoring and control system with ZigBee wireless network in the objective of developing the potential for demand response for numerous numbers of residential and commercial customers. This system can effectively solve the insufficiency problem of power supply while such problem happened in the electricity utility side, the system restrains the peak loading of Taipower system in time to prevent transformer from overloading, and decrease peak power generation simultaneously in order to achieve the goal of energy conservation and carbon reduction. ZigBee wireless smart loading monitoring and control system uses visualized program language to develop graphic control software, and the control module of this system which applied the perimeter interface controller (PIC) single chip microprocessor and embedded ZigBee wireless integrated circuit (IC). Meanwhile, through the application of infrared control technology and the deployment of temperature loading control module, the software and hardware were integrated into a set of home-using smart loading control system. The operating status of electrical appliances then can be monitored and controlled at any time. The integration of ZigBee smart loading control module and digital meter, in the other hand, can also monitor and control the electricity consumption of household appliances and manage standby power as well. It effectively reduces the power consumption of peak hours and saves the cost of power rate for residential users and commercial clients.