A study on IoT-based sensors for precision agriculture

A transformative revolution has been triggered by the inclusion of Internet of Things (IoT) sensors in healthcare’s real-time tracking capabilities. Recent advancements in miniaturization and low-power designs, wireless connectivity, and sensor fusion (integration of information from multiple sensors) continue to have a profound impact on the monitoring of health. Intelligent embeddability and superior sensing capabilities facilitate seamless patient data tracking, and strong security protects data against breaches. The role of IoT sensors in healthcare is to connect devices, such as biometric and temperature sensors or imaging and environmental sensors. IoT sensors are used in multiple applications such as fitness and vital signs tracking information, and patient care in smart healthcare facilities. Such technologies enable IoT-based medication adherence systems so that patients follow prescribed treatments, thus improving outcomes and reducing the possibility of hospital readmissions. Compared to regular monitoring devices, IoT sensors provide continuous data capture, increased accuracy, improved patient mobility and personalized healthcare solutions. Healthcare IoT adoption faces challenges in data privacy, regulation, and the complexity of technical integration of healthcare IoT in existing infrastructures. Ethical considerations must also be addressed to ensure equitable access and unbiased use of IoT technology. However, despite these obstacles, the future of IoT-based healthcare technologies remains an attractive proposition with continuous technology evolution likely to improve the quality of care, optimal resource allocation, and decision-making in real-time. This chapter explores the evolution of healthcare monitoring through IoT sensors, detailing their technological mechanisms, applications, benefits, and the potential for transforming the healthcare landscape.

Chapter 5 - Functional framework for edge-based agricultural system

Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation

\n Unmanned Aerial Vehicles (UAVs) are being employed in many fields such as precision agriculture, environmental monitoring, disaster response support, and others. Following the technological development, these vehicles are now being connected to other devices and systems such as the Internet of Things (IoT), allowing them to provide and consume services as one of the things in the environment. The IoT is believed to bring new unprecedented development to our society and will impact and enable new, more powerful applications in many areas. This integration does not come without challenges. The intrinsic IoT heterogeneity regarding devices, platforms, and communication protocols makes the task of service provision and management much harder to be done in the application layer. Security is one of the most critical challenges in IoT-enabled applications, being sometimes put in after the main development is done, if at all. This work demonstrates how to leverage UAVs to be connected in the IoT using SEMU SErvice Management for UAVs. SEMU comprises (a) communication security, including contextaware security; (b) service management (registration, discovery, request and consumption) for different types and data requirements and (c) autonomous task management that allows the UAVs to perform their mission without human intervention. Its functionality and feasibility is demonstrated by its use in different applications simulating real-life scenarios such as a UAV-enabled delivery service and a UAV-enabled data collection from IoT sensors. Future research will be able to leverage on SEMU and its prototype to further investigate and advance the state-of-art regarding IoT-enabled UAV applications and their security mechanisms.\n

A past generation has seen the propagation of the Internet of Things (IoT) devices to sense information from environment and is actively being used in various sectors like healthcare, smart agriculture, and smart cites. Agriculture is the primary occupation and serves as the backbone of many nations. Past few decades’ climate, changes and rainfall have been unpredictable, leading to reduced productivity in agriculture. Even-though in the changing smart farming using IoT, enables the farmers to confront these productivity problems. Therefore, utilizing fog computing and the WiFi-based Long Distance (WiLD) network in IoT makes effective communication to farming and agricultural bases in rural areas. This achieved through IoT sensors, which can inform farmers daily about agricultural land such as crop yields, precipitation and soil nutrition. Numerous sensors are available today for smart farming. The State disaster or node failure these sensors cannot update information about agricultural land. To overcome this problem, we proposed Node Recovery (NR) algorithm for Smart farming IoT sensors in fog computing-based WiLD network to improve node sustainability and overcomes the node failure by replacing the best node in the network. We simulated the proposed NR algorithm, and the results indicate that the NR algorithm outshines existing systems.

Agriculture is a major backbone of the world economy and it sustains humanity. Due to inefficient practices and the lack of incorporating modern-day technology in farming, the yield is affected. Internet of Things (IoT) is an evolving technology that connects various devices and shares information mutually. IoT also plays a crucial role in smart agriculture. Smart farming is an emerging field as IoT sensors can monitor many environmental factors and provide necessary data for better yield and safety of crops. Monitoring factors like soil humidity, soil moisture, pH, and fertilizer concentration are very essential to improve the yield of major crops. The focus of this project is to use these technologies in the field of agriculture to channelize the usage of the existing resources. That is, it uses soil moisture, humidity, and pH sensors which in turn control the motor mechanisms for inflow and outflow of water in the field to sustain an optimum level of retained water in these fields. The outflow of water from fields will be controlled by drainage mechanisms incorporated in our system. The running water that is pumped out will be stored in a tank. Once the amount of water stored in it is measured, the required amount of fertilizer and chemicals will be added to the tank through automated process and this water and fertilizer mixture will be reused for irrigation of the crops in a process known as fertigation.

The Internet of things (IoT) plays important roles in the modern digital world. It has several important roles in the modern power systems and power grids. IoT presents a lot of potential in the power systems and power grids. Some of the support functions are direct and several others are found to be indirect. Either way, IoT can play a lot of important roles in the modern power systems. It can help significantly in the measurement, control, and monitoring of the physical parameters in the power grids. It helps to in the reduction of energy consumption in the power electronic components. It has the potential to provide a lot of operational flexibilities in the power electronic components. Implementation of advanced operational algorithms using artificial intelligence and machine learning is facilitated by the IoT based sensors, actuators and other key components. It can provide smart operational assistance to the power electronic systems used in the power grids. Due to their logical flexibilities IoT sensors can be deployed alongside the power electronic components to track their performances. Consequently, using the IoT sensors' information, the actuators are driven to deliver optimal outcome. IoT sensors' information can be sent directly to the central servers in regular intervals to monitor the overall performances of the power electronic components. In addition to the aforesaid applications, several other potential uses of IoT in power electronics include monitoring of critical power grid parameters such as temperature, current, voltage and vibration at different key locations. In this paper, we analyze the use of IoT in power electronic components in the modern power systems.

Due to the use of internet of things (IoT) devices, communication between different things is effective. The application of IoT in agriculture industryplays a key role to make functionalities easy. Using the concept of IoT and wireless sensor network (WSN), smart farming system has been developedin many areas of the world. Precision farming is one of the branches comes forward in this aspect. Many researchers have developed monitoring andautomation system for different functionalities of farming. Using WSN, data acquisition and transmission between IoT devices deployed in farms will be easy. In proposed technique, Kalman filter (KF) is used with prediction analysis to acquire quality data without any noise and to transmit this data for cluster-based WSNs. Due to the use of this approach, the quality of data used for analysis is improved as well as data transfer overhead is minimized in WSN application. Decision tree is used for decision making using prediction analytics for crop yield prediction, crop classification, soil classification, weather prediction, and crop disease prediction. IoT components, such as and cube (IOT Gateway) and Mobius (IOT Service platform), are integrated in proposed system to provide smart solution for crop growth monitoring to users.

A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network

World population in recent decades has significant impacts on the traditional agricultural systems which has resulted in increased demand for food, land use and deforestation, water scarcity, climate changes but not limited to these impacts. In order to overcome all these issues, there is a need for advanced farming technologies for growing the most demand food crops. Smart farming also known as precision agriculture has evolved which uses the advanced technology to optimize the efficiency and productivity of the farming operations. It involves the integration of various technologies such as IoT sensors, drones, robotics and machine learning technologies, big data analytics to gather data on crop growth, environmental conditions and weather patterns. Vertical framing (VF) is one such precision framing efficient crop growth practices which adapts the integration of Internet of Things (IoT) and machine learning (ML) technologies in easier manner. Since, the vertical farming is completely an indoor farming technique, they do not depend on the particular geographical locations and outdoor growth parameters (like soil) for crop cultivation; hence, vertical farming is also known as controlled environment agriculture. This article explores the significance of different indoor vertical farming practices under controlled environment with the comparative analysis, efficiency, productivity, advantages and their potential benefits highlighting the need for sustainable agricultural practices that can meet the growing demand for food while minimizing the negative environmental impacts.

Due to increasing advancements in the field of technology the agricultural sector is experiencing a drastic change, this paradigm shift is the result of integrating technologies like Industry 5.0, Society 5.0, Internet of Things (IoT), Artificial Intelligence (AI), and Remote Sensing. The integration of these technologies helps in multiple aspects such as IoT sensors help in real-time data monitoring which includes parameters such as soil conditions, meteorological aspects„the valuable insights about overall crop health, and also help in promoting informed decision-making in agriculture. Cognitive systems of Industry 5.0, revolutionize manufacturing processes, enabling predictive maintenance, real-time data analyses, and autonomous decision-making for increased production efficiency. Implementing digital twin technology further boosts this transformation process by creating dynamic representations of agricultural systems. Digital twins simplify complex interactions for farmers by integrating data obtained from diverse sources. This paper explores the relationship between Industry 5.0, smart farming practices, implementation of the latest techniques like IoT, and digital twins, understanding their impact on precision agriculture, sustainable farming, and efficiency. In conclusion, this study demonstrates how Industry 5.0 technologies, including digital twin technology, possess the potential to revolutionize agricultural operations, enhance crop yields, foster sustainability, and prepare agriculture for the challenges of a rapidly changing global environment.

Smart farming with accurate greenhouses needs to be implemented for better farming growth management, and therefore, precision agriculture monitoring in diverse conditions is required. The Internet of Things (IoT) is a new era in computer communication that is gaining traction due to its vast range of applications in project development. The IoT provides individuals with smart and remote approaches, such as smart agriculture, smart environment, smart security, and smart cities. This is the most recent technology that is making things easier these days. The Internet of Things (IoT) has fundamentally expanded remote distance control and the diversity of networked things or devices, which is an intriguing element. The IoT comprises the hardware as well as the Internet connectivity to the real-time application. Sensors, actuators, embedded systems, and an Internet connection are the key components of the Internet of Things. As a result, we are interested in creating a smart farm IoT application. In greenhouse agriculture, this study presented a remote sensing of parameters and control system. The objective is to manage CO2, temperature, soil moisture, humidity, and light, with regulating actions for greenhouse windows/doors dependent on crops being carried out once a quarter throughout the year. The major goal is to properly regulate greenhouse conditions in accordance with plant requirements, in order to enhance output and provide organic farming. The results show that the greenhouse may be controlled remotely for CO2, temperature, soil moisture, humidity, and light, resulting in improved management.

The role of the Internet of Things (IoT) in precision farming is increasing day by day. In these systems, smart sensors have been utilized for soil analysis to maintain sustainable agricultural practices. This analysis is crucial for maximizing the productivity of farming in an optimal manner. Nowadays, soil nutrients such as nitrogen, phosphorus, and potassium (NPK) have been predicted for real-time updates. Existing methodologies suffer from high-energy consumption of sensors with lower accuracy. Therefore, the present research proposes an IoT-based soil monitoring system with multimodal data analytics to evaluate soil properties in real time. IoT sensors measure the most critical parameters like moisture, temperature, pH, and nutrient values (NPK), which are analyzed using machine learning and predictive modeling. This work shows the potential of IoT and multimodal analytics in transforming soil assessment, promoting effective resource utilization, and improving crop yield. In addition, the proposed system allows for ongoing soil health monitoring, anomaly detection, and precision farming suggestions. Experimental outcomes show enhanced accuracy in soil classification and crop suitability prediction, enabling data-driven decision-making in agriculture.

ABSTRACTDigital map is considered as a cyber world, which maps visual representations (cyber objects) to the physical objects in the real world and allows the user to interact with these physical objects through their cyber representations. However, it typically requires significant programming effort to create a map application. This paper proposes MapTalk, a web-based visual map platform that allows the user to interact with the physical objects through their cyber representations in a visual map. We show how the administrator can add applications to the map without any programming effort. The novel idea in our approach is to utilise the IoT concept. Specifically, we implement the map as an output IoT device and all physical objects to be mosaicked in the map as input IoT devices. We show how to automatically create the device features of IoT devices when the administrator creates applications in the Map web page. We have deployed over 20 services in MapTalk including smart home, smart farm, tracking (bus, person, dog, etc), video monitoring, travel route planning, sensing of CO2, PM2.5, Internet of Things (IoT). temperature, humidity, and so on.Abbreviation: Internet of Things (IoT)

Smart agriculture (SA) is referred to as an improved approach in agricultural production. In SA, particularly smart irrigation systems, the integration of Internet of Things (IoT) devices has improved automation and efficiency. The SA idea is developed by integrating Information Technology (IT) and data processing techniques with quantitative decision‐making and intelligent control measures to advance production. Heterogeneous sensors are implemented in SA communication for enhanced security. However, the deployment of heterogeneous IoT sensors in large‐scale networks increases the vulnerability to cyber intrusions, which can severely disrupt irrigation control and crop health. For secure SA farming, this paper proposes a SpinalFuzzyGoogLeNet (SpinalFGNet) module for intrusion detection in smart irrigation. Initially, the IoT system is simulated; the process of intrusion detection is performed by acquiring input data from specific datasets. Here, data pre‐processing is done with Quantile Normalization. Then, feature fusion is achieved by Deep Kronecker Network with Gower distance. Afterward, data augmentation is performed by deploying oversampling based on Synthetic Minority Oversampling Technique. At last, intrusion detection is performed using the proposed SpinalFGNet, which is developed by the integration of SpinalNet and GoogLeNet with fuzzy concept. The evaluation of SpinalFGNet is conducted and accomplishments in terms of metrics gained accuracy at 92%, precision as 93%, recall as 95%, and F1‐score as 94%.