IoT-based Smart Research Articles

IoT based Smart Electricity Monitoring and Control Using ESP-Mesh Networking

The "IoT-Based Smart Electricity Monitoring and Control System Using ESP-MESH Networking" is a state-of- the-art project that aims to transform the way we manage and consume electricity in our homes. In today's world, our homes are equipped with a wide range of electrical appliances, each with varying levels of power consumption. Without an effective system to monitor and control these appliances, they can lead to excessive electricity usage. This not only results in high energy bills but also contributes to a significant carbon footprint, which is harmful to our environment. The Power consumption by the overall house can be monitored using this device and control the other electronic devices, able to monitor the power consumption in different rooms and able to monitor the current power by specific devices. User can check their Power usage from anywhere and at any time interval. The information can be monitored from mobile applications and also be monitored even on a web page. The Main can be turned on/off using a relay from anywhere. The Home application can also be controlled from anywhere through the mobile application. Keywords— ESP-MESH Networking, Power Consumption, Energy efficiency, Carbon footprint, Relay control, Energy management

EFFECTS OF ADOPTING INTERNET OF THINGS (IOT) BASED SMART WASTE BIN IN NIGERIA: A REVIEW OF SCHOLARS POSITIONS

The present inherent hurdles to optimising the Internet of Things (IoT) in the Nigerian economy include maintenance and deployment costs, reluctance to change, technological infrastructure, and connection. In order to meet the urgent demand for creative waste management solutions, this study intends to investigate the effects of implementing IoT-based smart garbage bins in Nigeria. The basis for this quasiexperimental study was replies gathered from scholarly publications in the topic by researchers using a Likert scale. The results show that academics are unified in their support of the use of IoT in waste management, pointing to its potential to lower costs, increase efficiency, lessen environmental risks, and create an eco-friendly atmosphere that stimulates economic growth. In line with environmental objectives, scholarly viewpoints also address challenges such as maintenance expenses, technological know-how, residents' adaptation, cybersecurity risks, and incorrect waste disposal. The study suggests that stakeholders, including individuals and government organisations, work together to maintain personal and community cybersecurity, satisfy technology needs, and establish an eco-friendly environment in light of these results. Even if the study's predictions seem bright, further research is still necessary to solve gaps and investigate unexplored areas of IoT-based waste management in Nigeria.

EMAKAS: An efficient three-factor mutual authentication and key-agreement scheme for IoT environment

The adoption of IoT in healthcare revolutionizes remote patient monitoring and healthcare efficiency. Yet, it brings notable security and privacy challenges, particularly in resource-constrained environment. We propose a secure and efficient three-factor lightweight mutual authentication and key agreement scheme, designed for IoT-based smart healthcare systems, addressing these critical concerns. The scheme employs a fuzzy extractor, a one-way hash function, Elliptic Curve Discrete Logarithm and XOR operations for efficient cryptographic transformations, creating a robust framework for secure data handling. The scheme's design focuses on security and privacy while minimizing computational demands, making it ideal for resource-constrained IoT devices. We utilized both informal and formal security analyses to validate our scheme, employing the Random Oracle Model (ROM), Scyther tool and Burrows-Abadi-Needham (BAN) logic. The security and performance analysis showed that our scheme offers more security features across 15 defined criteria with minimal communication and computational costs compared to other related schemes. The scheme is not only robust against security threats but also practical for implementation in IoT healthcare environment, offering a solution for secure IoT communication by achieving mutual authentication and key agreement with minimized computational requirements.

A comprehensive survey on an IoT-based smart public street lighting system application for smart cities

The swift advancement and updating of urban lighting systems, along with the incorporation of smart and Internet of Things (IoT) infrastructure, have opened up numerous opportunities for technological progress across various facets of life. This paper offers a comprehensive overview on the development of smart public street lighting infrastructure tailored for IoT applications in smart cities. Initially, the focus lies on transitioning from conventional lighting to Light-Emitting Diodes (LEDs) technology in street lighting. Complementing this transition, the incorporation of the wireless networked sensors and controllers ensures dynamic brightness control in operational zones, envisioning substantial energy savings. Furthermore, the notion characterizing smart cities denotes incorporating modern digital infrastructures to develop innovative functionalities and connect various application, following the IoT paradigm. The key findings from the proposed study have enhanced knowledge regarding smart public street lighting application. This system integrates smart poles equipped with LEDs lamps technology, smart sensors, communication network and monitoring unit, leveraging current technological advancements in IoT applications. The implementation of IoT-based smart public street lighting systems presents several challenges, including integrating diverse sensors and actuators ensuring robust device communication, secure data management, and effective system scaling and maintenance. Despite these challenges, this system significantly advances smart city infrastructure by enhancing energy efficiency, safety, and sustainability. However, addressing their high initial costs, data privacy and security concerns, and ongoing maintenance are crucial in future studies to realize their full potential in smart cities.

Enhanced IDS with Deep Learning for IoT-Based Smart Cities Security

Enhanced IDS with Deep Learning for IoT-Based Smart Cities Security

Revisiting the Extended IoT Use Behavior Model Among Senior NCD Patients for Smart Healthcare in Malaysia

The growth of the senior population and the rise in chronic illnesses such as non-communicable diseases (NCDs) are placing significant strain on modern healthcare systems. The demand for resources from hospital beds to doctors and nurses is high in Upper-Middle-Income countries like Malaysia. The use of IoT technologies can give aged people at home uninterrupted access to healthcare resources and better control over their health. This study aims to analyze the key antecedents of the actual behavior of IoT technologies among aged NCD patients in Malaysia under a framework covering the unified theory of acceptance and use of technology model, technology acceptance model, and theory of planned behavior. This cross-sectional study used a purposive sampling technique and data were analyzed using SmartPLS. The outcome is expected to help the government and healthcare policymakers develop awareness among the senior community on the use of IoT-based smart wearables for routine self-monitoring of NCDs.

Implementasi Smart Brankas Berbasis Internet Of Things Pada PT. Umbi Teknologi Indonesia

The IoT-based smart safe represents a cutting-edge innovation in secure valuables storage. The aim of this research is to develop, implement and evaluate an intelligent and secure system connected to the Internet of Things (IoT). The development method involves the integration of hardware and software design with sensor technology and network connectivity. Implementation is achieved by deploying the smart safe within home or office environments connected to the internet. System evaluation is conducted through a series of tests focusing on security, functionality, and reliability. The research findings demonstrate that IoT-based smart safes offer enhanced security access, real-time monitoring, and user convenience through mobile applications. These findings have significant implications for improving secure storage across various contexts, from households to businesses

Distributed intelligence for IoT-based smart cities: a survey

Distributed intelligence for IoT-based smart cities: a survey

An effective hybrid attention model for crop yield prediction using IoT-based three-phase prediction with an improved sailfish optimizer

Internet of Things (IoT)-based smart agriculture approaches have been adopted to achieve promising results in terms of food productivity. Moreover, high cost and technological complexity are major problems in existing agricultural management systems. Thus, a new IoT-based smart agriculture farming model is implemented in three phases namely crop detection, crop disease detection, and crop yield prediction. In the first phase, the plant leaf images are gathered from the Kaggle source PlantifyDr. The collected images are passed to the deep feature retrieval phase, where the Visual Geometry Group (VGG16) is utilized to calculate feature (f1). Then, a Hybrid Attention-based Crop Type Detection Network (HA-CTDecNet) is used for detecting the crop types, in which the parameters are optimized by Attack Power Improved Sailfish Optimizer (API-SFO). In the second step of crop disease detection phase, the input images are fed to Otsu Thresholding for Abnormality segmentation. The deep attributes from the segmented images are haul out using VGG16 and attain the feature (f2). The extracted features are forwarded to the Hybrid Attention-based Crop Disease Detection Network (HA-CDDecNet), where the constraints are optimized using API-SFO. In the third crop yield prediction phase, the data related to crop location are collected from the crop yield forecast dataset. Then, the collected data and the extracted features (f1) and (f2) are concatenated and fed to the Principle Component Analysis (PCA). Then, these features are given to the Hybrid Attention-based Crop Yield Prediction Network (HA-CYPreNet) with the parameter optimization of API-SFO. The investigation is accepted to verify the effectiveness of the suggested IoT-based smart agriculture model with the traditional approaches by considering distinct measures. The Mean Absolute Error (MAE) of the crop yield prediction model is improved with 35% than CNN, 27.5% than LSTM, 17.5% than GRU, and 7.5% than LSTM_GRU while considering the learning percentage value as 55.

IoT-Based Smart Glove for Pollution Monitoring and Potholes mapping using Node-MCU

In a nation where the majority of transport is characterized by road transport and the large number of vehicles moving on the expansive road networks, the pressuring issues of potholes and pollution their stemming wear and tear, and the health hazards caused due to that have posed formidable challenges to the authorities as well as the individuals moving around also. To address these critical concerns of society we have introduced an IoT-based (Node-MCU) smart glove that continuously collects the real-time data of pollution and the exact locations of the potholes. This innovative solution not only provides enhanced pothole detection along the provided route but gives insights of the air quality along their routes. introducing the remembrance factor and managing the previous data on potholes and pollution along with the severity index to prioritize hazardous repairs on the route. All Together the smart glove ensures the driver's safety by delivering real-time data of the conditions of the routes.

IOT Based Smart Grid to Remotely Monitor and Control Renewable Energy Sources using Cloud Computing

Abstract: The growing demand for clean and sustainable energy sources necessitates advancements in grid structure. This project explores the potential of an IOT-based smart grid to remotely monitor and control renewable energy sources. In this, we use solar energy as a renewable energy source, and with the help of cloud computing, we monitor and control different types of loads. The paper highlights the key components, functionalities, and benefits of such a system.The increasing demand for sustainable energy solutions fuels the development of smart grid technologies. This project proposes an IOT (Internet of Things)-enabled smart grid architecture for remote monitoring and control of renewable energy sources. The core concept involves integrating sensors with renewable energy sources like solar or wind turbines. These sensors collect real-time data on energy generation, environmental conditions, and system health. This data is then transmitted over a network to a central control system or a cloud platform for further analysis. By implementing remote control capabilities, grid operators can: regulate energy production based on demand; integrate storage solutions for surplus energy; and improve overall grid stability and reliability. The project concludes by outlining the benefits of this approach, including a reduced carbon footprint, cost savings, and a more sustainable energy future.

Security and privacy in IoT-based Smart Farming: a review

Security and privacy in IoT-based Smart Farming: a review

Lightweight and privacy-preserving device-to-device authentication to enable secure transitive communication in IoT-based smart healthcare systems

Lightweight and privacy-preserving device-to-device authentication to enable secure transitive communication in IoT-based smart healthcare systems

A Review of IoT-Based Smart City Development and Management

Smart city initiatives aim to enhance urban domains such as healthcare, transportation, energy, education, environment, and logistics by leveraging advanced information and communication technologies, particularly the Internet of Things (IoT). While IoT integration offers significant benefits, it also introduces unique challenges. This paper provides a comprehensive review of IoT-based management in smart cities. It includes a discussion of a generalized architecture for IoT in smart cities, evaluates various metrics to assess the success of smart city projects, explores standards pertinent to these initiatives, and delves into the challenges encountered in implementing smart cities. Furthermore, the paper examines real-world applications of IoT in urban management, highlighting their advantages, practical impacts, and associated challenges. The research methodology involves addressing six key questions to explore IoT architecture, impacts on efficiency and sustainability, insights from global examples, critical standards, success metrics, and major deployment challenges. These findings offer valuable guidance for practitioners and policymakers in developing effective and sustainable smart city initiatives. The study significantly contributes to academia by enhancing knowledge, offering practical insights, and highlighting the importance of interdisciplinary research for urban innovation and sustainability, guiding future initiatives towards more effective smart city solutions.

Evaluating the Impact of Controlled Ultraviolet Light Intensities on the Growth of Kale Using IoT-Based Systems

Incorporating Internet of Things (IoT) technology into indoor kale cultivation holds significant promise for revolutionizing organic farming methodologies. While numerous studies have investigated the impact of environmental factors on kale growth in IoT-based smart agricultural systems, such as temperature, humidity, and nutrient levels, indoor ultraviolet (UV) LED light’s operational efficiencies and advantages in organic farming still need to be explored. This study assessed the efficacy of 15 UV light-controlling indoor experiments in three distinct lighting groups: kale cultivated using conventional household LED lights, kale cultivated using specialized indoor UV lights designed for plant cultivation, and kale cultivated using hybrid household and LED grow lights. The real-time IoT-based monitoring of light, soil, humidity, and air conditions, as well as automated irrigation using a water droplet system, was employed throughout the experiment. The experimental setup for air conditioning maintained temperatures at a constant 26 degrees Celsius over the 45-day study period. The results revealed that a combination of daylight household lights and indoor 4000 K grow lights scored the highest, indicating optimal growth conditions. The second group exposed to warm white household and indoor grow red light exhibited slightly lower scores but larger leaf size than the third group grown under indoor grow red light, likely attributable to reduced light intensity or suboptimal nutrient levels. This study highlights the potential of indoor UV LED light farming to address challenges posed by urbanization and climate change, thereby contributing to efforts to mitigate agricultural carbon emissions and enhance food security in urban environments. This research contributes to positioning kale as a sustainable organic superfood by optimizing kale cultivation.

Auto-metric Graph Neural Network for Attack Detection on IoT-based Smart Environment and Secure Data Transmission using Advanced Wild Horse Standard Encryption Method

Smart cities (SCs) are being constructed with the huge placement of the Internet of Things (IoT). Real-time enhancements to life quality based on comfort and efficiency. The key concerns in most SCs that immediately impact network performance are security and privacy. Numerous approaches are proposed for secure data transmission, but the current methods do not provide high accuracy and it provide high computational time. To resolve these problems, an Auto-metric Graph Neural Network for Attack Detection and Secure Data Transmission using Optimized Enhanced Identity-Based Encryption in IoT (AGNN-AWHSE-ST-IoT) is proposed. Primarily, the input data is taken from the NSL-KDD dataset. The input data is gathered with the aid of NSL-KDD is pre-processed using three steps, crisp data conversion, splitting, and normalization. Then the Pre-processed input is fed into the Colour Harmony Algorithm (CHA) based feature selection to select the important features. After feature selection, the preferred features are given to the AGNN classifier. After classifying, the data is given to Enhanced Identity-Based Encryption (EIBE), and it is optimized using Wild Horse Optimizer (WHO) for transmitting the data more safely. The outcomes of the normal data are displayed using the LCD monitor. The AGNN-AWHSE-ST-IoT method is implemented in PYTHON. The AGNN-AWHSE-ST-IoT method attains 8.888%, 13.953%, 19.512% higher accuracy, 2.105%, 6.593%, 8.988% higher cumulative accuracy, 54.285%, 54.285%, 52.941% lower encryption time, 8.2%, 3.3%, 6.9% lower decryption time, 11.627%, 10.344%, 6.666% higher security level and 60.869%, 70% and 64% lower computational time than the existing approaches such as SBAS-ST-IoT, BDN-GWMNN-ST-IoT and DNN-LSTM-ST-IoT respectively.

Design and Implementation of an IOT-based Smart Home Automation System in Real World Scenario

INTRODUCTION: Automation developments have significantly increased general convenience in modern technology. Our study focuses on developing and deploying an Internet of Things (IoT)–enabled smart home automation system that maximizes energy efficiency and improves convenience in residential settings. Our technology provides homes with an intelligent environment by smoothly combining several sensors, actuators, and communication protocols. We explore the challenges of creating a reliable and useful smart home systemthat works well in everyday situations. We investigate the difficulties, architectural issues, communication protocols, and security features specific to these systems.OBJECTIVES: This project aims to use a database server and Wi-Fi module to develop an effective and reasonably priced smart home automation system. It improves accessibility and convenience by enabling smartphone voice control of household appliances.METHODS: This project was developed using various implementation techniques, including wireless home automation via GSM technology, speech recognition-based systems, Blynk, and the Internet of Things. These techniques added up to a reliable and effective smart home system.RESULTS: Although it improved user routines, the IoT-based smart home system had latency problems. Interoperability issues are still present. Improvements were guided by user input. Using Blynk, our software manages loads remotely. CONCLUSION: A home automation system that is inexpensive and locally supplied can effectively operate household appliances. It is adaptable, scalable, and a component of future smart homes. IoT applications in real life revolutionize convenience and efficiency in living environments.

Development of IOT-based Smart Anti – Theft Latch

In our day to day lives, security is of utmost concern. Everyone wants to be as secured as possible. This paper develops an Internet of Things (IoT)-based smart security system and supports the concept of a "IOT close to life and easy to use”. One of the many useful applications of IoT is home security; among other things, we're leveraging it to build affordable home security systems. The IoT is a network of physically connected objects that can exchange data and communicate with one another without the need for human intervention. The simplicity of setup, low cost, and minimal maintenance are some of this system's key benefits. This system is executed with the help of Raspberry Pi 3B. Face recognition system used determines if the individual is lively enough to receive authorization. If an unauthorized access is detected then the buzzer will be triggered and an alert notification will be sent on an app which is designed for the owner which will also include a photo detected by the camera and with an option to reset the buzzer. This system comprises of dual authentication that is Radio Frequency Identification (RFID) key and a face recognition system. This approach is able to protect the homes from intrusions by continuous detection of the suspicious actions using soft computing, that is, Convolutional Neural Network (CNN) algorithm for face recognition.

Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review

Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review

IoT-based smart net energy meter with advanced billing feature for residential buildings including solar PV system

Electricity consumption is rising across all industries. Residential electricity use dominates the sector. Solar photovoltaic (PV) systems on residential roofs are increasing quickly, notably in Dhaka, Bangladesh. PV power generation is high at peak sun irradiance. Due to light loads, residential structures use less electricity. PV system surplus electricity may be transmitted to the national grid. Residential customers may sell power to the government, lowering their electricity expense. Traditional energy meters make it difficult to calculate PV system consumption by load and grid injection. This is possible with net metering. Thus, this study presents an internet of things (IoT)-based smart net energy meter for home users to provide surplus solar PV power and consume grid electricity when needed. With the government's new power tariff rate, the net bill will be calculated automatically. A dedicated mobile application is used to monitor all the activities. The billing statement will be generated automatically, and the payment of that bill will be payable using a redirect link with the same mobile application. The suggested smart net energy meter will inform SMS/mobile app users of gas, smoke, and tempering. The suggested meter's performance and efficacy were evaluated using software simulation and hardware analysis.