IoT Technology Research Articles

Energy 4.0: AI-enabled digital transformation for sustainable power networks

The considerable developments in green technology have changed several industries, including the energy business and power sector. These advances have transformed power generation and distribution. Artificial intelligence (AI), machine learning, computer vision, blockchain, and the internet of things (IoT)have advanced significantly in recent years. These advances have transformed the energy business and expanded its applications. In this concise yet comprehensive analysis, energy and power systems are examined from an eco-friendly perspective and emerging research trends are highlighted. The increasing use of renewable energy sources necessitates infrastructure restructuring, which presents the potential for grid development and AI usage for intermittent and distributed generation. The integration of AI, IoT, and blockchain technologies into energy and power systems offers opportunities to improve efficiency, sustainability, and dependability. Application-specific research is comprehensively examined to investigate the present state-of-the-art and limitations of various technologies. The focus in this study is the importance of implementing a complete policy and planning framework to support a significant and efficient shift toward advanced grid systems. The convergence of AI, information and communication technology , and energy systems offers unparalleled prospects affecting the trajectory of energy generation, distribution, and consumption in the future. This study makes a valuable contribution to the wider academic discussion on utilizing digital transformation to create sustainable and intelligent energy ecosystems by integrating insights acquired from several fields.

IoT Based Crop Monitoring System

The IoT-Based crop monitoring System is a cutting-edge solution designed to enhance agricultural productivity and efficiency by utilizing sensor technology and automation. The core objective is to automate the irrigation process based on real-time soil moisture data. The systememploysanESP8266 module to facilitate the seamless exchange of data between various sensors and Firebase, a real-time cloud database. This interconnected system comprises two primary fields, each equipped with soil and soil moisture sensors, temperature sensors, smoke detectors, UV light, buzzer, solenoid valves, and mini water pump. The ESP8266 module serves as the central hub for collecting data from the sensors and transmitting it to the Firebase database. The data includes crucial information such as soil moisture levels, temperature, smoke detection, and UV light intensity. Furthermore, the system integrates alarm functionality through the buzzer and precise irrigation control via the solenoid valve and mini water pump. The data stored in Firebase is accessible through a web application built using React.js and Node.js. This web interface provides users with a graphical representation of the gathered data, allowing them to monitor the agricultural conditions in real-time. The main key features are Data Visualization in which users can remotely monitor soil moisture levels and receive real-time updates on crop conditions through a web application. Resource Efficiency by automating irrigation, this model optimizes water usage, reducing waste and improving water conservation. This paper not only showcases the integration of IoT technology in agriculture but also serves as a practical and environmentally responsible solution for modern farming practices.

Smart Medicine Remainder Box for Elderly People with IoT

This project focuses on developing a Smart Medicine Reminder Box for elderly people, leveraging IoT technology to enhance medication adherence. The system utilizes an Arduino AT Mega microcontroller to gather data from various sensors. An IR sensor is employed to detect the presence of medicine in the compartments, ensuring accurate monitoring of medicine intake. To provide timely reminders, a Real-Time Clock (RTC) module is integrated, triggering a buzzer alert when it is time for medication. The ESP8266 Wi-Fi module connects to the Blynk platform, sending alert notifications to caregivers or family members via a smartphone app. Additionally, a GSM module is included to make call alerts, ensuring critical reminders are not missed. This multi-faceted alert system is designed to support elderly individuals in maintaining their medication schedules, thereby improving health outcomes, and reducing the risk of missed doses

Task offloading with enhanced Deep Q-Networks for efficient industrial intelligent video analysis in edge–cloud collaboration

With the rapid development of 5G and IoT technologies, industrial video surveillance systems face increasingly significant challenges in processing large-scale data. Traditional cloud-centric video analysis methods often lead to processing delays and inefficiencies in resource allocation due to centralized data processing. In this context, the emergence of edge computing, especially in edge–cloud collaborative intelligent video analysis, offers an innovative solution. Within this architecture, task offloading strategies focus on intelligent computation task distribution to minimize latency and optimize resource utilization. However, existing methods frequently overlook key application-specific metrics in video analysis, such as accuracy and timeliness. This study introduces a novel Edge–Cloud Collaborative Industrial Video Intelligent Analysis Architecture (ECIVA) based on the GRU-Enhanced Deep Q-Network (GEDQN). This architecture integrates the strengths of Gated Recurrent Units (GRU) for processing sequential data with the efficiency of Deep Q-Networks in optimizing task-offloading decisions. By incorporating a token bucket mechanism, the architecture optimally manages the task offloading rate within the constraints of limited network bandwidth, thus ensuring high accuracy and real-time responsiveness in video analysis. Empirical evaluation with offshore drilling platform video data shows that the architecture achieves 87.5% mean Average Precision (mAP) at a 0.5 IoU threshold and increases system throughput by 39.2% over traditional cloud-centric methods.

Design & Development of Smart Fire Safety System For e-Vehicles Using IOT

The project concept of An autonomous fire safety mechanism for EVs is an inventive way of considering a less cost-effective design and a more convenient application for e-vehicle safety. Day by day, the number of fatalities in electric vehicles increases. In this project, our main aim is to reduce the number of accidents with electric vehicles by making a smart fire safety system for electric vehicles. With the rapid adoption of electric vehicles (EVs), ensuring their safety, particularly concerning fire hazards, has become paramount. This abstract presents a conceptual framework for a sophisticated fire safety system tailored specifically for EVs. The proposed system integrates advanced technologies such as battery monitoring systems (BMS), temperature and smoke sensors, machine learning algorithms, active fire suppression mechanisms, emergency shutdown features, remote monitoring capabilities, user alerts, and compliance with safety standards. By combining these elements into a cohesive system, EV manufacturers can significantly mitigate the risks associated with fires in electric vehicles, enhancing overall safety for drivers, passengers, and emergency responders. With this technique, the fire extinguisher automatically puts out the fire when it detects flames or smoke coming from the vehicle's fire zones. Numerous factors can cause an electric vehicle to catch fire. The lithium-ion battery is the primary cause of flames in electric vehicles used within them. Installing an autonomous fire extinguishing system with Internet of Things (IoT) technology can reduce potential financial loss from a fire while also raising vehicle and human safety standards. Additionally, as soon as an accident is discovered, the nearby authorities or emergency services are notified, allowing them to get to you sooner. KEYWORDS:- Accident prevention, emergency alert, controller, fire incident, IOT technology, etc.

Early Detection of Onion Spoilage Utilizing IoT and AI during Storage and Transportation

This paper introduces an innovative approach to reducing crop wastage by converting traditional onion warehouses into smart warehouses using IoT and AI technologies. We propose an IoT-based monitoring solution that continuously analyses and communicates the health status of onion stock in real time. Additionally, our methodology aims to support farmers by providing them with essential information about their onion stocks, enabling informed decision-making. Traditionally, farmers rely on sensory cues such as onion odor to detect decomposition. However, we propose a technological intervention utilizing image processing and AI-driven sensory mechanisms to enhance the onion detection process. Specifically, cameras are employed to identify sprouted onions, while AI algorithms analyze sensory data to detect rotten onions. Our prototype achieves an impressive 87% efficiency in identifying sprouted onions using camera-based systems. Furthermore, the response of the gas sensing system in detecting rotten onions under prescribed chamber dimensions yields encouraging results. Through the integration of IoT and AI, our methodology offers a promising solution to minimize crop wastage and promote sustainable agricultural practices.

A dataset of ambient sensors in a meeting room for activity recognition

As IoT technology advances, using machine learning to detect user activities emerges as a promising strategy for delivering a variety of smart services. It is essential to have access to high-quality data that also respects privacy concerns and data streams from ambient sensors in the surrounding environment meet this requirement. However, despite growing interest in research, there is a noticeable lack of datasets from ambient sensors designed for public spaces, as opposed to those for private settings. To bridge this gap, we design the DOO-RE dataset within an actual meeting room environment, equipped with three types of ambient sensors: those triggered by actuators, users, and the environment itself. This dataset is compiled from the activities of over twenty students throughout a period of four months. DOO-RE provides reliable and purpose-oriented activity data in a public setting, with activity labels verified by multiple annotators through a process of cross-validation to guarantee data integrity. DOO-RE categorizes nine different types of activities and facilitates the study of both single and group activities. We are optimistic that DOO-RE will play a significant role in advancing human activity recognition technologies, enhancing smart automation systems, and enabling the rapid setup of smart spaces through ambient sensors.

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

AbstractNature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

IoT Based Antenna Positioning System

An IOT-based antenna’s position system aimed at optimizing connectivity. Through IoT-enabled actuators, the system enables remote adjustment of antenna orientation, managed by a central control unit. Real-time monitoring ensures accurate alignment for optimal signal reception, enhancing communication reliability. Key advantages include remote accessibility, allowing users to control antennas from anywhere with internet connectivity, and scalability, facilitating seamless integration into existing infrastructure. By leveraging IoT technology, the system empowers users with enhanced control over wireless communication networks, offering efficient and reliable antenna positioning solutions across diverse applications.

Virtual and Remote Robotic Laboratory

The Virtual Remote Robotic Laboratory (VRRL) represents a paradigm shift in scientific experimentation, offering researchers unprecedented access to remote laboratory facilities and robotic systems. This paper explores the design, implementation, and benefits of VRRL systems in enhancing access and collaboration in scientific research. VRRL leverages cloud computing, IoT technologies, and advanced user interfaces to enable researchers to remotely control and interact with robotic systems and laboratory equipment from anywhere with an internet connection. Through intuitive user interfaces, researchers can seamlessly navigate virtual environments, manipulate robots, monitor experiments in real- time, and analyze data, all without physical presence in the laboratory. Key features of VRRL include robust security protocols to protect sensitive data, flexible experimentation capabilities to accommodate diverse research needs, and collaboration tools to facilitate knowledge sharing and teamwork among researchers worldwide. By breaking geographical barriers and providing on-demand access to laboratory resources, VRRL empowers researchers to conduct experiments more efficiently, accelerate scientific discovery, and foster interdisciplinary collaboration

Development of Smart Fire Safety System for Vehicles Using IOT

Today, fire accidents often occur in homes and industrial environments, where climate change and human negligence can be predicted. The proposed prototype is designed to detect such fire accidents and remotely extinguish the fire from the user, thus reducing the risk to firefighters. The main idea of ​​this concept is to propose a model that focuses on the cost-effective design and implementation of an automatic fire protection system for electric vehicles. In this system, the fire extinguisher is activated when it detects flame/smoke in the vehicle fire area and extinguishes the fire automatically. There are many reasons for an electric vehicle that can cause fire accidents. The main cause of electric car fires is the battery used in them. There have been many cases of cars catching fire for the reasons mentioned above and ending with the entire vehicle reduced to ashes. The installation of an automatic extinguishing system allows to minimize the financial losses that can arise from a fire and to increase the safety level of the vehicle, passengers and other road users. Keywords : Fire Situation, Controller, Emergency Alert, Accident Prevention, IOT Technology etc.

Forest fire risk monitoring using fuzzy logic and IoT technology

Forest fire risk monitoring using fuzzy logic and IoT technology

WSN-Based Data Acquisition System for Collecting Environmental Pollution Factors for Green City

Implementation with a portable, intelligent weather station is to upgrade the system for environmental management and monitoring. This gadget measures and assesses meteorological and air quality data by real-time implementation through the Internet of Things technology to provide important environmental condition insights. The monitoring station's hardware parts include the ESP32 board, MQ2, MQ3, and MQ135 sensors for gas detection. Such an intelligent weather monitoring station can easily be deployed in different environments due to its portability and versatility. This small station size and its wireless link will enable effective data collection and transfer and integration into other systems of the present day. The existing environmental monitoring system takes the temperature and humidity and measures ammonia, benzene, alcohol, smoke, and carbon monoxide. Therefore, data is brought to the monitoring station, where it communicates in real time for the users to review and analyse on the online platform Blynk. It enables users to easily make data presentations and create notifications and dashboards using a mobile app of Blynk and the web platform, enabling them to create interfaces that meet their satisfaction easily. Users may also upload the uploaded data to a web server for remote access and analysis, which eases scalability and flexibility. Portable intelligent weather monitoring station has significant areas of influence on applications like public health, urban planning, and environmental monitoring, among others. It thus provides decision-makers with information on pollution management and climate resilience. This provides a sustainable and resilient future; it is approachable since the interface used in the system is accessible, befitting the two settings: urban and rural. The Intelligent Portable Weather Monitoring Station provides great usefulness in the disclosure, development, innovation of technology, and practice in the quest for meeting challenges of the environment with the promotion of sustainability. This not only allows smarter and more sustainable city development with effective environmental monitoring and management that includes IoT technologies but can also allow flexible data transmission facilities.

Experimental Study on Automated Controller of Street Light Management System using IoT

In order to ensure public safety and support nighttime activities, street lighting is crucial to the urban landscape, and this study starts by contextualizing its importance. It then goes on to analyze the shortcomings of the current street lighting systems, which are typified by their static control methods and poor environmental response.These shortcomings underscore the urgent need for a paradigm shift towards more intelligent and adaptive solutions. The core proposition of this project lies in the design and implementation of an advanced street light management system empowered by IoT technology. At its heart, the system comprises a sophisticated network of interconnected devices, including sensors, actuators, and a central control unit. These components collaborate synergistically to capture, analyze, and act upon real-time data gleaned from the surrounding environment. Through the application of cutting-edge data analytics and machine learning algorithms, the system autonomously adjusts lighting levels in accordance with fluctuating parameters such as ambient light levels, traffic density, weather conditions, and pedestrian activity. By dynamically optimizing energy usage while ensuring adequate illumination, the system not only enhances operational efficiency but also contributes to cost reduction and environmental sustainability. Moreover, the proposed system incorporates proactive maintenance features, enabling early fault detection and remote diagnostics. This capability minimizes downtime and enhances system reliability, thereby mitigating the operational challenges associated with traditional street lighting infrastructure. The project further explores the conceptual framework of IoT, elucidating its transformative potential in revolutionizing urban infrastructure management. By seamlessly integrating physical devices with digital intelligence, IoT facilitates the creation of interconnected ecosystems wherein data-driven decision-making and automation converge to drive unprecedented efficiency and innovation

Implementation of Internet of Things for Condition-Based Maintenance in Elevator Systems: An Analytical Case Study

The aim of this paper is to show how IOT technology can allow highly distributed elevator equipment servicing by using remote-monitoring technology to ease a move from tradi- tional corrective maintenance (CM) and time-based maintenance (TBM) to more prophetic, condition-based maintenance (CBM) in evidence to accomplish different advantages.The Literature review can indicate that CBM has merit over conventional CM and TBM from a theoretical outlook, but it turns on constant monitoring enhancement via advanced IOT technology. A detailed case history was implement to bring practical proof that IOT allows elevator adamant to attain CBM.Through, a theoretical context, the CBM of elevators put together exper- tise.The venture lies in data collection, data analysis and decision making in real-world context of business. The key outcome of this research recommends that in the case of elevators the IOT can be commercialized and would improve the welfare and accuracy of equipment. It might, so, be understandable from technological, process and economic aspect. Our long term physical world case study reveal an actual way of building the CBM of elevators worldwide. Incorporating Internet of things and other modern technology will improve the safety and authenticity of elevator equipment,longer service life, minimize disruption or difficulty and business interference as a result of equipment downtime and to eliminate major repairs, thus result in low maintenance cost. A key contribution in this study stand in the empiric confirmation of the advantages and difficulties of CBM through Internet of things comparative to conventional CM and TBM in the instance of elevators. The writer had faith that this study is well timed and will be worth to company working on the same research or business strategies.These are form on condition based maintenance, IOT, Time-based maintenance and Corrective maintenance.

A New Intelligent Home Automation with Integration of Solar and Grid

This project presents a groundbreaking paradigm shift in residential energy management and automation, offering an innovative solution that seamlessly integrates two diverse energy sources: solar and conventional grid (EB) power. With solar energy taking precedence as the primary power supply, meticulously controlled by the versatile ESP32 microcontroller, the system ensures a consistent and reliable energy supply by intelligently switching to grid power when solar resources are scarce, seamlessly reverting back to solar power as soon as it becomes available again. In addition to its dynamic energy management capabilities, the project incorporates an LCD screen to provide homeowners with real-time insights into their energy consumption patterns, offering a comprehensive overview of usage data from both energy sources. This invaluable information empowers users to make informed decisions about their energy usage habits, fostering a more conscious and efficient approach to power consumption. Furthermore, the integration of the Blynk 2.0 IoT platform elevates the project to new heights by enabling remote monitoring and control of the system. Through this intuitive interface, users can effortlessly manage their energy usage from anywhere using their smart phones or computers, ensuring maximum flexibility and convenience. Expanding beyond traditional energy management, the project delves into the realm of home automation with the incorporation of an additional ESP32 microcontroller. This feature-rich functionality empowers users to remotely control lighting fixtures, enhance safety measures with gas detection capabilities utilizing MQ2 sensors, and efficiently monitor water levels using ultrasonic sensors, all seamlessly integrated into the IoT eco system. This holistic solution not only prioritizes renewable energy sources and automates power source transitions but also leverages the transformative potential of IoT technology to create a sustainable and intelligent approach to energy management. Powered by ESP32 technology, this project serves as a trailblazer for the future of smart, efficient, and eco-friendly homes, setting a new standard for residential energy management and automation.

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

Smart Power Monitoring System Using IoT

The "Smart Power Monitoring System using IoT" constitutes a pioneering solution poised to revolutionize the landscape of power management in both residential and industrial settings. Anchored by the ESP8266 NODEMCU microcontroller, this system seamlessly integrates an array of state-of-the-art sensors and modules, all working in concert to deliver real-time monitoring, controlling capabilities. The predominant objective of this innovative system is multifaceted: to elevate energy efficiency, bolster security measures and enable remote monitoring through the dynamic fabric of the IoT. The ESP8266 NODEMCU, functioning as the system's central hub, orchestrates a symphony of components, including a current sensor for precise power consumption insights and a PIR sensor to intelligently detect human motion for enhanced automation. The inclusion of a GSM module ensures timely communication of critical alerts to users via SMS, underscoring the system's responsiveness. Additionally, a relay module facilitates the remote control of appliances, promoting user-friendly power management. A half-horsepower induction motor acts as a tangible load indicator, providing users with a visual representation of power fluctuations. The system, powered by a stable 9V 1 Amp DC supply, culminates its capabilities by leveraging the “ThingSpeak” platform to graphically display power consumption trends, empowering users to make informed decisions for sustainable and efficient energy practices. In essence, the "Smart Power Monitoring System using IoT" emerges as a comprehensive and forward-thinking solution, poised to redefine the paradigm of intelligent power management. Keywords: IoT technology, real-time monitoring, energy consumption analysis.

Enhancing Field Multiplication in IoT Nodes with Limited Resources: A Low-Complexity Systolic Array Solution

Enhancing Field Multiplication in IoT Nodes with Limited Resources: A Low-Complexity Systolic Array Solution

Data acquisition and application of internet of things in financial management

With the rapid development of information technology, Internet of Things technology has become a hot topic in today’s society. This study aims to explore how IoT technology impacts various aspects of financial management, in particular how it changes the collection, processing, and parsing of financial data. Through in-depth literature review, questionnaire survey and experimental design, the research found that IoT technology has a significant positive effect on sales, transaction times and inventory control. But at the same time, data security, privacy concerns and initial technology investment are also challenges that enterprises need to consider when adopting these technologies. Overall, IoT technology offers tremendous opportunities for financial management, but businesses must fully assess the potential risks and benefits when implementing it.