IoT-based Smart Research Articles

IoT Enabled Smart Agriculture using Digital Dashboard

Objectives: To develop an IoT-based smart device to retrieve soil and water parameters by collecting and storing sensor-based data and to make crop recommendations. The primary goal of this work is to create a link between farmers and their farms. Methods: This work produces a smart device to predict suitable crop based on several parameters using a technical approach that makes use of the Internet of Things, Machine learning algorithms, and Sensor Networks. The Gaussian Naive Bayes algorithm is used in the Prediction module to predict suitable crop types. Several components are used to make an IoT system like Raspberry Pi, DHT11 sensor, soil moisture sensor, pH sensor, LDR sensor, Ultrasonic sensor, NPK sensor, and other components to collect data related to soil properties and weather conditions. Soil samples were collected from different fields and tested by switching the IoT kit and the message will be passed to the field owner. Dataset is used from the Kaggle database to train the model for crop prediction. Seven parameters have been taken in the trained dataset such as pH, N, P, K, temperature, humidity, and rainfall. All these values were collected through soil properties sensors and weather condition sensors. Existing traditional methods like soil testing laboratories involve lengthy and costly procedures for soil testing and crop prediction. The proposed system will produce results faster at a lesser cost while making digital data available on the cloud server. Findings: The proposed system performs efficiently when compared to the previous report with 99.3% accuracy over 97.18%. The existing approach for lab-based soil testing takes approximately 5 – 8 days; whereas the proposed system using an IoT kit can perform n number of soil tests on a single device and the results can be viewed instantly. Novelty: Most of the existing systems like smart agriculture system and smart irrigation system use Arduino Uno for soil test which doesn’t support high-end sensors and works only with desktop computer traditional programs. The existing system for nutrient identification uses a soil Ec sensor and color sensor to find N, P, and K values which include multi-steps and are time-consuming. The proposed system uses Raspberry Pi for soil tests which are more compatible with implementing the latest machine-level algorithms. In this system, the NPK sensor is used instead of soil Ec sensor and color sensor which will give the N, P, and K values directly. In the existing system, accuracy is 95% with the Random Forest algorithm but the proposed system will give 99.3% with the Gaussian Naïve Bayes algorithm. Keywords: Internet of Things (IoT), Smart Agriculture, Raspberry Pi, Sensors

A Novel Steganographic Approach for Secured Communication via Boustrophedon Transformation to Develop IoT-based Smart City

Steganography is a vast arena to provide security in digital media at the time of network-level transmission. This research is based on a pioneer steganographic approach introducing Image Authentication using Boustrophedon Transformation (IABT) to ensure secured communication. The research experiment is performed using ten benchmark images of 512 × 512 dimensions and the performance result of each of the images has been analyzed thoroughly. In order to implement this transformation technique to the cover images, each of the cover media is decomposed into 1 × 3 blocks. These decomposed spatial components are transformed into frequency components by implementing the Boustrophedon transformation. Confidential data in a bit stream format is embedded into each of the transformed coefficients. Furthermore, the Inverse Boustrophedon transformation is used for converting the transformed embedded coefficients back to the pixel components. This process is kept on repeating until the entire bit stream of the hidden information is inserted into the carrier media to produce a steganography image. Moreover, the performance factor of the proposed method is examined in terms of PSNR, MSE, IF, and Payload, which shows overall better performances of the IABT.

IoT-based Dual Technology Motion Detector

Home security has become a prime concern in recent years. As technology is emerging every second, abundant IoT-based smart surveillance systems have been developed and implemented with many modern features to keep the home safe. The proposed project is an extended approach to the existing home security system. The project aims to reduce false alarm rates of the existing home security system by using a Dual Technology motion sensor. Dual-technology sensors are sensors that combine two different types of technology. The dual-technology sensor uses both PIR and ultrasonic sensors, activating the trigger only when both sensors detect any motion of an object. The proposed setup effectively reduces false alarms caused by inanimate objects. The trigger caused by the Dual Technology sensor sends a message alert to the specified phone number through the Twilio Programmable Messaging API. Ultrasonic technology is sensitive to motion toward and away from the detector; passive infrared technology (PIR) is sensitive to motion across the field of view. The future implications of the project are great as it can also be used in the Automation of lighting in homes, offices, and libraries.

An IoT-based Smart Plug Energy Monitoring System

An IoT-based Smart Plug Energy Monitoring System

Marine water aquaculture and an IoT-based smart fish dryer of the future: a sustainable environmental approach

Marine water aquaculture and an IoT-based smart fish dryer of the future: a sustainable environmental approach

Blockchain enabled security mechanism for preventing data forgery in IoT-based smart homes

Conventional homes have grown to be smart homes (SM) with the assistance of the Internet of Things (IoT). A unified gateway connects all Internet-enabled devices. Those gateways need to be secured by the usage of a sturdy safety method due to the fact safety is very vital. In order to conquer those problems, this observation shows employing Blockchain Technology (BC) for secured houses to make certain information private and safe. To cope with the safety problem with SH gateways, this paper proposes a solution. A reliable Q-learning algorithm with Blockchain assistance is proposed for the SH gateway to save data. three sections make up the protocol Q Learning Algorithm with Blockchain Support (QLABS). First, a Lattice cryptosystem based on Cipolla’s extended Euclidean distance algorithm is used to record and store user data (CEED-LC). Second, while preserving data secrecy and integrity, the proposed cryptosystem offers a small Key Generation (KG) with excellent security. Third, with the assistance of QLABS protocol agreement, the blocks are created with a specific gateway enrolment-ID. The testing results demonstrate how highly safe the suggested framework is against the vulnerabilities by obtaining a higher throughput along with a higher Packet Delivery Ratio (PDR) value. The results show that the recommended approach tops other existing algorithms in terms of PDR and throughput.

Robust and Lightweight Remote User Authentication Mechanism for Next-Generation IoT-Based Smart Home

Robust and Lightweight Remote User Authentication Mechanism for Next-Generation IoT-Based Smart Home

Smart Agriculture Fencing Using IOT

This project present the design of Smart agriculture fencing using IOT to tackle the problems of agricultural sector like animals attack. Animals like wild boars, elephant, tiger and monkeys etc. Cause serious damage to crops by animals running over the field and trampling over the crops. In the crop fields trespassing by animals leads to destruction of crops. The implementation of an IoT-based smart agriculture fencing system for protecting crops against wild animals. The data collected by these sensors is processed using a microcontroller, and the farmer is alerted in real-time via a mobile application or SMS. The system also includes a deterrent sounds to scare away the animals. Overall, the smart agriculture fencing system offers a reliable and efficient solution to prevent crop damage caused by wild animals.

Machine Learning-based Classification Approach for Improving Renewable Energy Management in IoT-based Smart Grids

Machine Learning-based Classification Approach for Improving Renewable Energy Management in IoT-based Smart Grids

Design of an IoT-based smart healthcare eco system for precise ageing illness detection and prognosis

The article aims to provide a comprehensive review of the current state of smart healthcare systems, focusing on important topics such as the use of wearables and smartphones for health monitoring, the application of AI and machine learning for illness detection, and the integration of assistive frameworks like social robots for ambient-supported living. One of the key aspects discussed in the study is the software integration architectures required for the development of smart healthcare systems and the seamless integration of data. Analytics and various AI technologies are utilized in conjunction to create these systems. The article examines the different approaches employed in the development of these systems, highlighting the contributions of each framework, the overall functioning methodology, performance outputs, and comparative advantages and limitations. Moreover, the article also addresses current research challenges and identifies prospective future directions. It emphasizes the shortcomings of existing smart healthcare systems and proposes strategies for introducing innovative frameworks to overcome these limitations. The analysis aims to provide detailed insights into recent advancements in smart healthcare systems, enabling specialists to contribute to the field with their expertise. Overall, the article serves as a valuable resource for understanding the current landscape of smart healthcare systems, shedding light on recent breakthroughs, and encouraging further research and development in this rapidly evolving field.

A Security-Enabled Safety Assurance Framework for IoT-Based Smart Homes

The exponential growth of the Internet of Things (IoT) has paved the way for safety-critical cyber-physical systems to enter our everyday activities. While such systems have changed the way of our life, they brought new challenges that can adversely affect our life and the environment. Safety and security are two such challenges that can hamper the widespread adoption of new IoT applications. Due to a large number of connected devices and their ability to control critical physical assets, intended attacks on them and/or unintended failure events such as mechanical failure of devices, communication failure, and unforeseen bad interactions between connected devices may cause an IoT-based system to enter into unsafe and dangerous physical states. By considering the importance of safety and security of IoT systems, in this article, we present a security-enabled safety monitoring framework for IoT-based systems. In the proposed framework, we utilize design-time system analysis to create an executable monitoring model that enables run-time safety assurance provision for a system via collecting and analyzing operational data and evidence to determine the safety status of the system and then taking appropriate actions and securely communicating the safety status and recommended actions to the system users to minimize the risk of the system entering into an unsafe state.

Developing a Sustainable IoT-based Smart Weather Station for Real Time Weather Monitoring and Forecasting

Weather forecasting is an important aspect of many industries and activities, from agriculture to transportation. The main motive of this research is to design a sustainable smart weather station to track temperature, humidity, wind speed, and other weather parameters, and send the data to the IOTA network via a Wi-Fi module. The GISMO micro controller provides the necessary processing power to collect and store data, while the Wi-Fi module allows for real-time monitoring and remote access to the data. The data collected by the smart weather station can be used for weather forecasting, climate research, and disaster management. The proposed model is used to develop the accurate weather information in real-time, making it an ideal solution for individuals and organizations looking to monitor local weather conditions. By leveraging IoT technology, smart weather stations offer numerous advantages over traditional weather monitoring systems. The research mainly focuses to enable seamless data collection from multiple sensors placed strategically in various locations, providing a comprehensive view of weather conditions across different areas.

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

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%.

IoT-Based Smart Wireless Communication System for Electronic Monitoring of Environmental Parameters with a Data-Logger

As the transition toward automated industrial environments gains momentum, the necessity for dependable, real-time environmental monitoring systems becomes increasingly critical. Addressing this urgent need, this paper unveils a sophisticated IoT-based wireless communication system specifically engineered for electronic monitoring of key environmental parameters. An Arduino Nano microcontroller is central to the system’s design, which interfaces with a diverse suite of sensors to monitor temperature, humidity, gas concentrations, and even emergency conditions like fire hazards. Leveraging the capabilities of an nRF24L01 wireless module, the system transmits these multi-faceted sensor readings in real time to a control unit. Once received, the data is vividly displayed on an (organic light emitting diode) OLED screen and logged onto a Micro-SD card for subsequent analysis and historical trend monitoring. Additionally, the system features audio-visual alerts through an electric buzzer and LED, providing instantaneous warnings in critical situations. Notably, the sensor data undergoes rigorous validation by being compared with reference values obtained from NASA’s official databases, ensuring high accuracy and reliability. This comprehensive solution significantly advances industrial safety and efficiency, serving as a robust tool for monitoring and responding to environmental changes in real time.

Garbage Content Estimation Using Internet of Things and Machine Learning

Much garbage is produced daily in homes due to living activities, including cooking and eating. The garbage must be adequately managed for human well-being and environmental protection. Although the existing IoT-based smart garbage systems have gained high garbage classification accuracy, they still have a problem that they provide a small number of garbage categories, not enough for reasonable practices of household garbage separation. This study presents a new smart garbage bin system, SGBS, embedded with multiple sensors to solve the problem. We deployed temperature, humidity, and gas sensors to know the condition and identify the garbage content disposed of. Then, we introduce a new garbage content estimation method by training a machine learning model using daily collected fuse sensor readings combined with detailed household garbage contents annotations to perform garbage classification tasks. For evaluation, we deployed the designed SGBS in five households over one month. As a result, we confirmed that the leave-one-house cross-validation results showed an accuracy of 91% in 5 kitchen waste contents, also, 89% in 5 paper/softbox contents, and 85% in the 8 garbage categories for the classification tasks.

Examining IoT-Based Smart Campus Adoption Model: An Investigation Using Two-Stage Analysis Comprising Structural Equation Modelling and Artificial Neural Network

Examining IoT-Based Smart Campus Adoption Model: An Investigation Using Two-Stage Analysis Comprising Structural Equation Modelling and Artificial Neural Network

Retracted: A Versatile and Ubiquitous IoT-Based Smart Metabolic and Immune Monitoring System.

[This retracts the article DOI: 10.1155/2022/9441357.].

Cyber-Security of Embedded IoTs in Smart Homes: Challenges, Requirements, Countermeasures, and Trends

Connected computers and sensors transmit data across the Internet to solve problems and generate new services (IoT). Smart homes use IoT, for example. Smart home technology can monitor temperature, detect smoke, regulate lighting automatically, and install smart locks. It also poses additional security and privacy problems, such as accessing user data through surveillance equipment or false fire alarms. Smart homes are vulnerable to numerous sorts of assaults. This survey emphasizes IoT. We discuss IoT's design, objects, and standards. We also address the tiered Internet of Things framework and smart home security concerns. In this article, researchers examine IoT-based smart home difficulties and offer solutions.

IOT Based Smart Farming Application

Smart agriculture is one of the Internet of Things' most important uses. Water, fertilizer, and crop yield waste are all reduced via smart agriculture. The manual detection of specifications like temperature, moisture, and humidity in the existing agricultural system drives up labor costs, and continuous monitoring is not possible. The irrigation procedure is carried out automatically in this study utilizing various sensors, which reduces manual work. It is suggested to utilize a sensor-based monitoring system for crop fields. It would entail gathering information on the soil moisture, humidity, and temperature. Automation of irrigation is possible by keeping an eye on all these variables. Unquestionably, smart farming is a key facilitator in providing more food with less resources for a growing global population. While this is essential to feeding the world's expanding population responsibly, smart farming also offers producers and communities throughout the world additional advantages. Farmers may raise yields and improve environmental management by using these strategies. By monitoring the field, IoT-based smart agriculture enhances the overall farming system. The Internet of Things in agriculture helps farmers save time and lessen the usage of resources like water thanks to sensors and connections. electricity, internet-connected temperature monitoring.

Integrating IoT-Based Devices for Monitoring The Humidity and Temperature In the Boarding House Space

Boarding houses provide the service of renting out living quarters. A good boarding house maintains a pleasant temperature and humidity level. High humidity may damage the interior and the people who live in it; therefore, it must be monitored more closely. Simpang Kost is the boarding house the author used as an object of research on monitoring temperature and humidity using an application called Smart Life. The goal of this study is to use IoT-based sensors to monitor and automatically solve temperature and humidity concerns in various rooms. Smart temperature and humidity sensors are IoT-based. This device measures the temperature and humidity in the room. According to the collected data, three categories of rooms have issues with high humidity presentation levels, so the authors offer several solutions to address these issues. The dehumidifier functions as an actuator, starting when the humidity in the room rises and stopping when the humidity in the room reaches the standards. An exhaust fan is also linked to an IoT-based smart breaker and serves as an actuator. When the room temperature rises above a certain threshold, this tool will activate and turn off automatically.