Effective inventory control and prompt gas leak detection is essential for both operational effectiveness and safety in industrial kitchens and storage facilities. In order to track ingredient levels and identify gas leaks in real time, this paper offers an Internet of Things-based solution that combines an microcontroller, ultrasonic sensor, gas sensor, and GSM module. While the gas sensor continuously checks for the presence of dangerous gases, the ultrasonic sensor gauges the amount of substances in containers. When inventory levels fall below a predetermined threshold, the controller sends warnings to a website, enabling remote monitoring and triggering restocking procedures. To ensure a prompt response to possible threats, the controller instructs the GSM module to send instant SMS and phone call alerts to selected persons in the case of a gas leak. This creative method improves safety, decreases human error, and increases accuracy by substituting an automated, real-time system for conventional manual checks. This solution, which makes use of IoT technology, offers a scalable, effective, and user-friendly way to keep an eye on safety and inventory in industrial facilities and commissary kitchens, which eventually helps to increase productivity and manage hazards.

This paper presents the design of an advanced assistive stick for visually impaired individuals, integrating ultrasonic sensors, GPS tracking, and GSM communication to enhance mobility. Traditional walking sticks have limitations in detecting obstacles, which can hinder independent navigation. The proposed solution improves upon conventional designs with ultrasonic sensors for faster and more accurate obstacle detection, reducing accident risks. GPS tracking provides real-time location data, aiding navigation in unfamiliar areas, while GSM technology enables emergency alerts and location sharing with caregivers. Additionally, the device includes a feature to help users locate the stick if misplaced. By enhancing safety, independence, and inclusivity, this smart navigation tool supports equitable opportunities and improved well-being. It contributes to sustainable development goals and aims to enhance the autonomy and quality of life for individuals with visual impairments.

Managing waste efficiently in metropolitan cities is a growing challenge, often leading to overflowing garbage bins, environmental pollution, and increased operational costs. Trash Tracker is a smart waste management system that leverages IoT and real-time monitoring to optimize garbage collection. Each bin is fitted with ultrasonic sensors that track fill levels empty, half, or full and automatically notify garbage collectors when bins are nearly full. To make collection more efficient, the system uses the determine the shortest route to full bins, reducing fuel usage and labor costs. Additionally, Google Maps integration allows real-time tracking of bin locations, ensuring better coordination. By preventing waste overflow and optimizing collection, Trash Tracker helps keep cities cleaner, improves public health, and enhances operational efficiency. This project promotes smart infrastructure, environmental sustainability, and healthier urban living.

The growing demand for energy-efficient solutions in smart homes has led to the development of IoT-based energy-saving and monitoring systems. This project focuses on utilizing the micro controller to monitor and control household energy consumption in real time. By integrating sensors, which like ultrasonic and cloud connectivity, users can optimize their energy usage, reduce wastage, and lower electricity bills.

This work offers the design of an IoT-based access control system using the R307 optical biometric sensor that increases the use of fingerprints for secure access. This system is developed to provide a safe and effective means of securing the restricted areas so adaptable for the different places such as offices, homes, and industries. The hubs of the system are the R307 biometric sensor that scans and verifies the user's fingerprints to unlock the access. The fingerprints are then recorded in the system, only the security personnel they allow access to the facility. This biometric form of identification not only enhances security by rendering door lock and unlocking impossible without the human feature being scanned but also does away with conventional security devices such as keys and cards which are always easy to misplace, copy or even have stolen. The system boasts integration with the IoT, hence its strength due to features such as remote control. Added to that is the provision for attempt log, and in most cases, real-time notifications; thus, the system is portable and expandable. This was especially true for the large-scale scenarios where access points are concurrently utilized. Besides offering highly secure access control models that integrate the fingerprint technology, the system comes with an easily understandable module for enrolling the fingerprints besides other administrative options that allow easy management of the system by both the system administrators and the ordinary users. Apart from this, the IoT framework leaves scope for further development. It is possible to integrate with other security systems or attach new sensors in order to raise the factors for authentication.

Currently, excessive and unnecessary water usage is a significant issue. When water is wasted, it cannot be reused and is lost entirely, leading to various consequences. The impacts of water loss are severe, as many places around the world lack access to safe drinking water [1]. Cities often draw water from natural sources, and if these sources are not replenished, local species that rely on or inhabit these environments may perish [2]. Additionally, wastage limits the availability of water for others' needs. The visible impacts of excessive water loss are numerous. The affected ecosystems may suffer significant damage [3]. Although wastewater treatment plants exist to process and purify used water, the loss still imposes economic burdens due to the extensive resources and processes required to restore water for consumption [4]. In this study, we aim to develop an Automatic Water Filling System and Water Quality Measurement System that operates efficiently for water filtration systems [5]. To achieve this, we employed Ultrasonic Sensors, an IoT device used for distance measurement, in combination with a Solenoid Valve to control water flow, both of which are integrated into our framework. Additionally, we used TDS Measurement to determine the total dissolved solids, including inorganic and organic substances, in water. The system can be controlled via the application and provides notifications through LINE Notify. The prototype was evaluated based on criteria for water quality measurement and the results obtained from water level control.

Preserving their harvest from wild animals and unauthorized intrusions is a significant difficulty for farmers. The Farm Guardian, a solar-powered, IoT-enabled automated crop protection system, helps solve this. This technology uses an microprocessor alongside touch sensors and ultrasonic sensors to detect movement and touch. Should dangers be discovered, the HCSR04 ultrasonic sensor's continuous object distance monitoring sets off a buzzer and warning system. A solar-powered electric fence keeps animals or trespassers off the perimeter, hence enhancing security by means of a slight deterrent shock. the system sends alerts and actual data to an Android phone belonging to a farmer, therefore enabling remote monitoring effective operation with a 12V solar photovoltaic power source changed to 5V DC for the control unit and sensors. Output modules for buzzers, LED indicators, a servo motor, and a user interface oLED screen constitute the system. Automated, affordable, environmentally friendly means of crop protection using renewable energy is provided by Farm Guardian. The suggested strategy maintains ecologically sustainable farming methods, secures the farm efficiently, decreases dependence on traditional fences, and lowers human interference.

The Automatic Garbage Collector is an IoT-based system designed to automate waste collection. A line follower robot, controller, ultrasonic sensors, and a servo motor with electromagnets were all integrated into the system to efficiently detect, gather, and move trash without the need for human assistance. A smart dustbin with an ultrasonic sensor detects the fill level and sends a signal to via MQTT. Controller activates an controlled line follower robot, which navigates to the bin. Real-time updates were made possible with MQTT communication, guaranteeing prompt trash disposal. An ultrasonic sensor detects obstacles, triggering a buzzer for safety. The robot, equipped with a 360° servo motor and electromagnets, lifts the filled bin, replaces it with an empty one, and transports the garbage to a central disposal area. This system improves efficiency, reduces human effort, optimizes waste management, and ensures a cleaner environment. The research addresses inefficiencies in traditional waste collection, such as delays and high labor costs, by implementing a fully automated approach. The system's real-time communication and autonomous operation make it a scalable solution for smart cities.

This paper gives a detailed overview of the methodology and the details of how we built a “Smart Cargo Ship” using an CONTROLLER and various simple sensors. The ship monitors important factors like engine temperature, distance to obstacles, light presence on the dock, presence of water (which might lead to sinking of ship). When the Smart ship senses a danger, it automatically sounds a buzzer, activates lights or motors according to conditions. The goal of this study is to develop a safe and efficient “Smart cargo ship” using Internet of Things (IOT) Technology.

A vision-enabled smart navigation system combines wearable glasses, cameras, LIDAR, and ultrasonic sensors to provide real-time directions for the visually impaired. Using a solar battery, it operates on low-power consumption for long durations. Precise audio outputs are provided with system adaptability to the environment of the user for a comfortable experience. Its user-friendly design enhances autonomy, safety, and mobility and reduces the frequency of constant recharging. Advanced sensor technologies combined with green power, the system makes it possible for an environmental and reliable navigation system. It is ideal to enhance the life of the blind person on a daily basis and keep innovation and accessibility in proportion.

This paper presents a developed and tested autonomous robotic vehicle with a track sensing and obstacle avoidance system, using an L298 motor driver and an controller. The vehicle is designed to move autonomously in different environments while detecting obstacles and making avoidance decisions. It is fitted with an HC-SR04 ultrasonic sensor, which continuously measures the distance for the presence of an obstacle and changes the path of the vehicle accordingly. The motor driver L298 controls the two DC motors, which aid in controlling the movement of the robot accurately. The microcontroller then processes this information from the ultrasonic sensor and instructs the motor driver to make adjustments to achieve a smooth navigation and obstacle avoidance in real-time. Designed for handling various types of terrains and obstacles, the robot showcases very advanced techniques of collision avoidance. The control software is written in the tonny and C++ programming, integrating sensor information with motor commands. This project is for improving efficiency and autonomy in terms of robotic vehicles, with absolutely no manual intervention to the least possible while improving operational reliability.

An electronic device such as a radar is one which uses electromagnetic waves to measure the height, range, direction, or the speed of an object, which could be either static or in motion. There is a growing demand for efficient object detection systems in robotics, autonomous vehicles, and smart surveillance. This paper presents an ultrasonic sensor-based system for object detection in 360-degrees. The system is supposed to provide continuous rotation during which the environment would be fully scanned so that immediate detection of different objects of metallic as well as non-metallic can be done. Experimental results demonstrate the system's capability to accurately detect objects at various distances and orientations with a focus on minimizing measurement errors and enhancing detection efficiency. Finally, the proposed method significantly improves the reliability of object detection in dynamic environments.

This paper introduces a cost-effective smart posture detection system aimed at promoting ergonomic sitting habits and preventing musculoskeletal disorders. The system integrates ultrasonic sensors for backrest proximity measurement and load cells for seat weight distribution analysis. By analyzing these inputs, it identifies posture irregularities and provides real-time feedback for corrective action. Testing results demonstrate the system's reliability, achieving a detection accuracy of 99% across users with varying body types and postures. The proposed hybrid approach ensures simplicity, affordability, and scalability, making it a viable solution for homes, workplaces, and schools. This research contributes to the growing field of smart health monitoring systems, offering a practical alternative to traditional posture correction methods.

This study explores the evolution of home security and automation systems, driven by the growing need for safety and convenience in residential spaces. It analyzes various systems employing microcontroller technologies like Raspberry Pi. These systems incorporate dual-password authentication, remote notifications via GSM modules, and IoT capabilities to monitor environmental factors such as motion, gas leaks, and temperature. By examining features like PIR sensors with cameras, RFID-based access control, and mobile applications for alerts, the study highlights innovative technological integrations in modern security solutions. While many systems showcase robust functionalities, concerns regarding false alarms and user discretion are significant. Overall, the research emphasizes the transformative role of IoT, GSM, and RFID technologies in home security.

The goal of this solar-powered livestock fencing with Internet of Things (IoT) based system is to protect crops from animal encroachment in a non-toxic and ecologically friendly manner. The system integrates sensors such as PIR and ultrasonic to detect animal movement and triggering a low-voltage electric fence with a repeller device to deter animals without causing harm. A camera provides real-time images, but GSM and Wi-Fi modules enable remote monitoring and notifications through mobile devices. The system, which is fueled by renewable solar energy, offers a sustainable solution for smart agriculture through automated and networked technology, improving crop protection, reducing animal injuries, and increasing farm management efficiency.

In order to improve cleanliness and minimize manual involvement, this study proposes an automated hygiene management system for septic tanks and public toilets. The system incorporates PIR sensors to identify restroom occupancy, sophisticated odor sensors to monitor air quality, and high-pressure water jets for effective cleaning. An automated aroma dispenser is activated when restroom stink levels surpass a threshold of 15 parts per million. The machine starts more sprays at 10- and 20-minute intervals if the stink doesn”t go away. If the odor persists, maintenance staffs are alerted, and a complete cleaning cycle is automatically started. Additionally, when no human presence is detected, the PIR sensor initiates automatic flushing following restroom usage. The system regularly checks odor levels in septic tanks between 50 and 100 parts per million. When the threshold is exceeded, contaminants can be pushed out through an automatic drainage system because high- pressure water jets mix the contents. This guarantees effective trash management that doesn't overlook any already-existing materials. The suggested approach addresses typical problems including poor cleaning, odor control, and waste overflow and offers a dependable and affordable way to raise hygiene standards in septic tanks and public bathrooms. It improves resource management, public health, and operational efficiency in sanitary facilities by automating critical procedures.

The “Smart Occupancy Detection and Activity Recognition” project aims to develop an innovative solution for monitoring and managing occupancy and activities within indoor environments, focusing on enhancing energy efficiency and promoting smart building technologies. The project leverages advanced sensors such as passive infrared (PIR) or radio frequency (RF) sensors for occupancy detection, combined with activity recognition algorithms, to provide real-time insights into the presence and actions of individuals in a room. This system is designed to function in various settings, including healthcare facilities, offices, educational institutions, and residential buildings, ensuring that energy usage is optimized by dynamically controlling devices such as lighting, HVAC systems, and other utilities based on occupancy and activity data. The primary goal of this system is to reduce energy waste and ensure that energy-consuming devices operate only when needed, thereby improving energy efficiency. This is achieved through automated control mechanisms that adjust the power consumption of equipment in response to real-time occupancy and activity patterns, ensuring a sustainable and cost-effective approach to building management. Additionally, the system is designed to be scalable and easily integrated with existing infrastructure, offering versatility and applicability across various sectors. The potential applications of this technology are vast, particularly in the context of smart cities, where energy efficiency is a critical concern. By automating building management tasks based on real-time data, this project contributes to the broader goals of sustainability and resource optimization, supporting the on going transition towards smart, energy-efficient infrastructures.

This paper introduces an innovative and budget-friendly IoT-based facial recognition system tailored for attendance tracking and intruder detection, leveraging the power of Raspberry Pi.. The system not only automates attendance records, updating them in real time and sending them via email, Designed with cost efficiency in mind, it operates on low power and uses readily available components, such as an external camera and a PIR sensor for motion detection. This practical solution is particularly well-suited for use in hostels, schools, industrial facilities, and other environments requiring secure, contactless systems. Future plans for enhancement include incorporating advanced features like liveness detection, expanding the dataset for higher accuracy, and upgrading the hardware for improved portability and seamless IoT integration. These upgrades aim to position the system as a cutting-edge tool for modern surveillance needs.

This research paper inscribes an alarming need for effective bridge health monitoring and controlling amid growing concerns over structural integrity and safety. India has been inherited by n number of rail bridges and roadway bridges over valleys, rivers, hills etc. since british era. Many bridges in urban India near rivers face deterioration over time despite remaining operational. This poses risks to users due to factors like heavy vehicles, rising water levels, pressure variations, and heavy rainfall all potentially leading to collapses and disasters. Apart from these usual factors, earthquake is also one of the most disastrous factor which poses a great risk to the safety and structural integrity of bridges. Hence, it is of an utmost importance that periodic audits of structural integrity and safety of these bridges is critically evaluated. In this study we put forward development of a smart bridge system equipped with a network of sensors controlled using IoT system. We have developed a model prototype of a smart bridge wherein, sensors monitor and responds to variables such as water levels and structural deflections, vibrations, object detection etc. on a real time basis. The sensors we used included flex sensors which monitor deflections, IR sensors to detect objects, water sensors monitoring water level and vibration sensors. In addition to the conventional parameter evaluation, application of the modern sensor based techniques in combination with IoT controlling provide an enhanced system for monitoring of structural integrity and safety of the bridges. Should any monitored parameter exceed its safe threshold signifying a risk of collapse, the system triggers alerts via a monitoring system and activates automatic barriers. Additionally, the controlling system codes provide multidimensional approach of monitoring. This proactive approach aims to enhance safety and prevent potential disasters caused by bridge failures. Daily analysis carried out on the data stored on the cloud. While this situation takes place automatically an alert is sent through application to nearby areas.

As environmental concerns and fire hazards continue to grow, there is an increasing demand for automated systems that can monitor and mitigate these risks in real time. This paper presents the design and implementation of an Enhanced Environmental Monitoring System which can integrate multiple sensors to track environmental parameters such as temperature, humidity, air quality and smoke. It also incorporates fire detection and hazard mitigation mechanisms. The system uses an array of sensors including DHT22, MQ series gas sensors, LDR, IR flame sensors and FC-28 to continuously monitor the environment. In the event of a fire or hazardous condition, the system triggers appropriate actions such as activating a water pump, cooling fan or a buzzer. Additionally, the system is equipped with cloud-based connectivity which enables remote monitoring and control through a web interface. This approach not only ensures real-time hazard detection but also enhances safety, resource management and environmental sustainability. The proposed system demonstrates a practical and scalable solution for intelligent environmental management in homes, offices and industrial areas.

Urban parking systems face critical challenges, including inefficient slot detection, traffic congestion, and security vulnerabilities like unauthorized access. This paper proposes an IoT-based smart parking system that integrates infrared (IR) sensors and RFID technology to enable real-time parking slot detection and enhanced security. The system employs IR sensors to monitor slot occupancy with a response time of < 2 seconds, relaying data via controller for real-time user updates. Simultaneously, an EM18 RFID reader authenticates users, preventing unauthorized entry. Experimental results demonstrate 95% detection accuracy and 1.2-second authentication latency, while scalability tests confirm viability for deployments in malls, hospitals, and other high-demand areas. By automating slot detection and access control, the system reduces manual intervention, optimizes parking efficiency’ and addresses key urban mobility challenges. Future work includes integrating GPS for dynamic slot navigation and pre-booking capabilities. rban parking systems face critical challenges, including inefficient slot detection, traffic congestion, and security vulnerabilities like unauthorized access. This paper proposes an IoT-based smart parking system that integrates infrared (IR) sensors and RFID technology to enable real-time parking slot detection and enhanced security. The system employs IR sensors to monitor slot occupancy with a response time of < 2 seconds, relaying data. Simultaneously, an EM18 RFID reader authenticates users, preventing unauthorized entry. Experimental results demonstrate 95% detection accuracy and 1.2-second authentication latency, while scalability tests confirm viability for deployments in malls, hospitals, and other high-demand areas. By automating slot detection and access control, the system reduces manual intervention, optimizes parking efficiency, and addresses key urban mobility challenges. Future work includes integrating GPS for dynamic slot navigation and pre-booking capabilities.

In the modern era of transportation using ordinary vision to detect cracks in track puts passengers in danger because these problems must go unnoticed for accidents to happen. To improve track evaluation our system employs infrared sensors combined with microcontrollers GSM, GPS and cloud computing for monitoring railway tracks in real time. IR sensors mounted along the tracks scan for damaged areas without human intervention including tiny unnoticeable fractures. The microcontroller analyzes the crack data and sends alerts using a GSM module to both local railway stations and maintenance teams. The system sends precise GPS data about the crack's location along with its severity to help repair teams respond faster. The cloud-based system stores track condition data securely so maintenance teams can use it to plan future maintenance and monitor track status over time. When repairs do not happen within the required 48-hour period the system activates a second alert system to higher authorities so they can take necessary action to avoid train accidents. The system helps prevent railway accidents and saves money by spotting track cracks fast while making repairs more secure.

The Smart Safety System is designed to enhance road safety by monitoring both the driver and passenger activities and ensuring proactive measures are taken to prevent accidents. The system leverages sensors and real-time data monitoring to ensure driver sobriety and attentiveness while also alerting the driver about potential passenger hazards. The system primarily focuses on two areas: driver safety and passenger detection. On the driver’s side, an alcohol sensor detects any sign of intoxication, and an eye-blink sensor monitors the driver’s focus, especially if distracted by using a phone. If intoxication is detected, the vehicle is automatically disabled through the motor control system, and an alert is triggered via an alarm and notification sent to a web-based platform. If the driver is distracted or drowsy, a warning alarm is activated. On the passenger side, an IR sensor and an ultrasonic sensor detect whether passengers are improperly standing or not seated, and a notification is displayed to the driver. The system integrates an with several sensors, including alcohol, eye-blink, IR, and ultrasonic sensors. The collected data is transmitted wirelessly through a Wi-Fi module to a cloud platform, such as ThingSpeak, and then displayed on a website built with Java, HTML, and JavaScript. The website provides a login interface to enroll driver information and monitor alcohol sensor readings. In case of driver intoxication, the system triggers the suspension of the vehicle by turning off the motor. This smart safety system provides an efficient, real-time mechanism for preventing road accidents by monitoring driver behavior and ensuring passenger safety.

In modern healthcare, real-time patient monitoring is essential for timely diagnosis and intervention. This paper presents the development of a Patient Monitoring System using LabVIEW, integrating multiple sensors and wireless communication for efficient data acquisition and visualization. The system utilizes controller, DHT11 and MAX30100 sensors to measure temperature, humidity, heart rate, and SpO₂ (oxygen saturation) levels. The acquired physiological data is interfaced with LabVIEW to provide real-time visualization and analysis. To enable wireless transmission, Amplitude Shift Keying (ASK) modulation is applied to the sensor data, which is then transmitted and received using a Universal Software Radio Peripheral (USRP). This system enhances remote patient monitoring by ensuring real-time, reliable, and secure data transmission, making it suitable for telemedicine and IoT-based healthcare applications.

The integration of IoT technology provides real time, data-driven monitoring that reduces waste and maintains the quality of the food, changing the way food is preserved and managed. This research focuses on the development and implementation of an Arduino-based IoT device for monitoring freshness, temperature, and humidity in storage food environments. Equipped with an MQ-4 methane sensor and DHT11 sensor, the system detects spoilage indicators and environmental conditions and opens an exhaust fan if the moisture has been in excess. The BLYNK IoT app provides instant information to the user about storage conditions, thus ensuring efficient and proactive food management. This product finds answers to some of the main food-related storage challenges by offering scalable and effective solutions for enhanced freshness and reduced spoilage.

The increasing demand for efficient and sustainable agricultural practices has driven the adoption of advanced technologies in farming and nursery operations. This paper introduces a Smart Nursery Management System, leveraging sensor-based automation and IoT technologies to optimize environmental conditions critical for plant growth. The system integrates soil moisture sensors, light sensors, and a DHT11 module to monitor and control irrigation, lighting, and ventilation processes, ensuring resource efficiency. An microcontroller serves as the system's core, enabling real-time data processing and remote control via a user-friendly application. Comparative analysis with traditional nursery management methods reveals significant improvements in water usage efficiency (25% reduction), labor reduction (30% less manual intervention), and enhanced plant growth rates (15% increase in biomass production). The paper also discusses the system’s limitations, potential for expansion, and a cost-benefit analysis, concluding that IoT-based solutions hold transformative potential for nursery management. Future research will focus on integrating advanced sensors and machine learning to enhance predictive capabilities and adaptability.

In recent years, the fusion of sensor technology with embedded systems has proven its worth in the domain of personalized healthcare. The purpose of the In-home healthcare system lies in its continuous monitoring of the vital signs of a person and the environmental conditions of the surroundings as well, thereby ensuring the early detection of health issues. This results in swift preventive measures to be undertaken. The proposed system is the integration of multiple sensors namely- The pulse sensor for monitoring the BPM (Beats per minute), DHT11 for room temperature, DS18B20 for body temperature, and MQ135 for air quality on Raspberry Pi Pico. The data from the sensors is continuously monitored and analyzed, detects anomalies, and generates alarms for the same. The sensor data collected is continuously displayed on the OLED for easy monitoring. The system’s compact and portable design enables efficient functionality while minimizing space requirements, offering a novel approach to space-constrained applications.

This paper presents the design and implementation of a low-cost IoT-based smart agricultural system utilizing the microcontroller and LoRa technology. The system integrates multiple sensors, including those for soil moisture, temperature, and humidity, to monitor essential environmental parameters. It also supports real-time image capture for crop assessment using the iot module. Data collected by the sensors is processed by the microcontroller and transmitted wirelessly via LoRa to a central gateway, which connects to the cloud for storage and analysis. This architecture provides flexibility and long-range communication, making it suitable for large agricultural fields. An intuitive dashboard enables farmers to visualize real-time data and control actuators remotely, facilitating automated irrigation and other field operations. The proposed system emphasizes affordability, scalability, and ease of replication, offering a practical solution for enhancing productivity and sustainability in agriculture.

The indispensable task of ensuring the safety of workers in coal mining is a crucial issue because of dangerous underground conditions such as toxic gas emissions, high temperatures and humidity, structural vibrations, etc. However, traditional monitoring technologies do not support real-time transmission, long-distance communication, and energy-efficient requirements, which cannot meet the needs of underground mining. In overcoming these shortcomings, this article proposes an IoT-based safety monitoring system equipped with LoRaWAN hardware, able to deliver continuous real-time monitoring of miners' environmental and physiological conditions. This contains three sensors; DHT11 (temperature & humidity), MQ2 (gas detection), SW-420 (vibration monitoring), and pulse sensor (heart rate tracking). These sensors are incorporated into a wearable device connected to a centralized control hub through LoRaWAN, ensuring long-distance, low-power, and anti-interference communication in underground scenarios. The data is uploaded to ThingSpeak, a cloud-based IoT analytics platform and real-time visualization, anomaly detection and historical trend analysis is done on the uploaded data. All sensor information are processed within the control unit and alert users as soon as possible as long as a measured parameter exceeds pre-defined limits. Also, the system can also track location in real time, helping emergency responders act quickly and rescue. Experimental results show that the system detects environmental hazards and worker health and anomalies, with low latency and high accuracy, with respect to the data transmission. This system improves the safety of workers, efficiency in operations, and preparedness for emergencies in the mining space by using the combination of IoT, LoRaWAN, and cloud-based analytics. We plan to build upon these results with AI-based predictive analytics and edge computing for optimum performance and scalability.

In India the demand of EVs are continuously increasing as a necessity for the future, to efficiently implement this system we require proper charging infrastructure. To reduce the charging time of existing charging system we can use battery swapping system. This paper presents the development of a smart battery swapping system which is designed for two 3.7V 4Ah batteries. The system continuously monitors the SOC, battery temperature, and potential fire hazards. Using a DHT11 sensor for temperature tracking and flame detection, the system incorporates visual and audible alarms and relay control for safety. Additionally, the system sends SMS via SIM800L if any charging slot is empty, ensuring real-time management and efficient use.

Maintenance of accessible freshwater and conservation of aquatic ecosystems are concerns that are inseparable from conservation of water quality. Therefore, there is a need for new technologies that support water quality monitoring in real time because the existing technologies are costly, have low scalability and can only be applied in limited areas. In this study we offer a wireless water quality monitoring system via microcontroller. This system consists of turbidity sensor, pH sensor, Total Dissolved Solids (TDS) module, and a temperature and humidity sensor DHT11 for measuring the water quality parameters in real time. To allow for the data to be easily viewed through a web browser, a user interface is compiled using the HTTP protocol and the data gathered by the system wirelessly transferred using the ESP-NOW protocol. In comparison to the contemporary approaches to water quality monitoring, the effectiveness of the suggested approach is higher, and the mentioned errors of sensors are less, and the degree of accuracy is higher also. This makes its use ideal when it comes to monitoring water bodies on a frequent basis due to the scalability and the reduced likelihood of errors hence making the preservation of water bodies, and their ecosystems affordable in real-time.

Green agriculture now relies on technology and there are numerous hurdles that have to be met, including soil erosion, global warming, and food availability. CA together with improvement of soil carbon and soil quality are of central importance to health of the soils and enhanced agricultural yields. Conservation tilted is the practice of leaving a portion of the soil residue after harvest, crop rotation is the practice of growing different crops in one area sequentially and nutrient managed is the practice of applying nutrients necessary for plant growth in correct quantities at the right time frequencies they’ re all beneficial to the soils as they reduce environmental degradations ,improves productivity of the soils. In this regards, IoT based solutions are seen as a promising solution for monitoring and managing soil and environment status. Through the application of the Capacitive Soil Moisture Sensor V1.2 and DHT11 Temperature and Humidity Sensor real time data can be obtained in regard to soil moisture, temperature and humidity. Information collected from these sensors is fed into cloud computing solutions which enhance optimal watering and gains Yield optimization and more appropriate decisions making. The ability of this system to acquire, log and sort the data acquired via the Arduino microcontroller and specialized libraries is seamlessly enhanced by the integration of the two. The analyzed data indicates reliable associations between the measured characteristics of soil moisture and environmental factors that can be helpful to design effective recommendations for improving the state of the soils and using water resources for sustainable agriculture.

With the rapid advancement of science and technology and increasing societal awareness, environmental security issues have gained the focus of social concern, prompting people to pay more attention to the safety and security of living and working environments. This paper presents the design and implementation of a multifunctional anti-theft alarm system based on the sensor. The system utilizes the microcontroller as the central controller, processing signals from the human infrared sensor and the DHT11 sensor. It records intrusion events in an external memory chip and displays real-time temperature, humidity, and clock information. The system is practical and user-friendly, incorporating multiple functional modules, including infrared alarms, environmental monitoring, and clock display. Additionally, the system exhibits high sensitivity, enabling accurate data monitoring and providing a reliable security mechanism.

A smart agricultural informatics platform integrated with Internet of Things (IoT) aims to revolutionize farming practices through a decentralized communication framework, the primary goal is to establish a knowledge-driven agricultural network in which farmers can discuss precision agronomy strategies, percentage know-how, and collaborate in real time to address challenges. The device carries a meteorological analytics module, powered by way of a RESTful Weather API, handing over a one-week climatic outlook to resource in dynamic choice-making. Advanced sensing technologies, including capacitive soil moisture sensors, electrochemical pH sensors, thermistors, and the DHT11 virtual sensor array, allow continuous tracking of soil physicochemical parameters and microclimatic situations for actionable insights. These features are wrapped in a mobility platform cell software program designed using the Flutter SDK, providing farmers with a modular UI/UX architecture that provides access to real-time farm diagnostics of their machines. Visualization of all facts using sensors. and predictive analytics for resources optimization Benefits of distributed computing and analytical aspects. When taken together this innovative platform helps farmers optimize spatial and temporal crop planning. Increase the sustainability of agricultural ecosystems and promote resilient, fact-driven agriculture.

This paper presents an IoT-based autonomous farming method for lowering the mortality rate of 7-10-day-old chickens by preventing the flow of infection and continuously monitoring and controlling some very pivotal environmental parameters. In this system, an microcontroller is used as the central unit which manages and controls all the sensors, ensuring healthier environmental conditions within the farm. The system uses several sensors like the temperature and humidity sensor DHT11. To create a user-friendly interface, real-time data is displayed via a monitor presenting the consumers with instant status of the crucial parameters. Moreover, this system will integrate two specialized robotic arms, identifying inactive or dead chickens and separating them to a designated place from the hatching area. This is proven to be significantly efficient in minimizing infection rates and ensuring a healthier surrounding for the remaining chickens. The incorporation of a buck converter allows high-power sources to step down so that low-power devices can run smoothly. In the implementation phase, we bought 250 chickens which from 222 chickens survived yielding a favorable cost benefit ratio of 0.48 indicating the economic validity of the system. The results of this study show that advanced sensor systems and robotic technology can be used in poultry farming to improve farm productivity reducing mortality rates.

Air pollution is an emerging issue all over the world, affecting health and the environment. This article suggests an IoT-based air quality monitoring and air purifier system to address this issue. The system utilizes as the main controller, providing Wi-Fi connectivity for IoT integration. It supports an MQ135 sensor to detect hazardous gases like CO2, NH3, and NOx, and a DHT11 sensor to detect temperature and humidity. An indicates real-time air quality data, making it user-friendly. To purify air, UV LEDs are utilized for killing bacteria and viruses, while a CPU fan is utilized for air circulation. A relay module is used for controlling the UV LEDs and the fan based on air quality inputs. A 12V adapter and connectors drive the system to ensure stable usage. Information is transmitted to a cloud platform and can be monitored and analyzed remotely. The system is economical, energy-efficient, and expandable for use indoors. The system continuously monitors air quality and automatically purifies the air when pollutant concentrations exceed a threshold. Testing proved reliable sensor reading, effective air cleaning, and robust IoT connectivity. The system can be installed in homes, offices, and small industries with a full solution for air quality management. The future development is to add more sensors, power optimization, and purification process improvement for bigger spaces. This technology has the potential to significantly improve public health and indoor air quality.

“Disaster Track” is an AI-driven system designed for real-time monitoring and instant alerts in emergency situations. The system is built around an microcontroller, which continuously collects environmental data from multiple sensors, including the DHT11 for temperature and humidity, the BMP180 for barometric pressure and temperature, and a gyroscopic sensor for seismic activity detection. This data, combined with manually inputted information, is processed by an to generate highly accurate weather forecasts and seismic alerts. Notifications are then transmitted to users via a GSM SMS module, ensuring timely emergency warnings. Additionally, while an integrated IoT platform enables remote data storage and access, facilitating further analysis and improved disaster response planning.

Health monitoring has become an essential system in modern healthcare for tracking real-time vital parameters. This device offers a cost-effective health monitoring solution with a live tracking feature, using an Arduino Uno (microcontroller) integrated with various sensors, including the DHT11 for temperature measurement, which also tracks the environmental humidity level. Additionally, the system incorporates the (GPS module) for location tracking, which is particularly useful for tracking patient with medical condition and the AD8232 (ECG sensor) for continuous heart rate monitoring of the patient. The system features an LED-based alert mechanism, which activates to signal abnormal conditions detected by the DHT11 sensor, particularly temperature-related anomalies. This device also has a panic button which helps the individual to notify the caretakers or healthcare providers when they experience any abnormality. Controller processes all the data collected from these sensors and presents it effectively. The obtained data is collected by the Thing speak app through real-time. The GPS module also helps to pinpoint the patient’s location, making the system ideal for health monitoring applications such as elder care or remote patient management. The system can be monitored remotely, which reduces the frequent visit of the caretaker or healthcare professionals and helps with the update of the patient through real-time. With its portability and affordability, this system stands out as a highly practical and cost-efficient option, enabling easy and continuous patient monitoring from anywhere.

A network of computing devices and sensors, the Internet of Things (IoT) rapidly exchanges data to assess circumstances and propose new services. Intelligent home technologies rely on IoT. Contemporary smart home systems offer automatic lighting control, smoke detectors, temperature sensors, and smart lock installations. But these technologies raise serious privacy and security concerns. Security flaws let hackers access sensitive data, send off false alarms, and take control monitoring systems. Smart home technology is vulnerable to many threats because consumers are wary of it. Recommended steps are preprocessing, sensing, and model training. Preparing raw, unstructured data for analysis. DHT11 humidity and temperature sensors and HC-SR501 PIR motion detectors collect data. Increasing smart home system accuracy reduces security concerns and boosts user trust, which may drive adoption.

The CalmNest smart cradle addresses the growing need for automated infant care, particularly for working parents who require reliable, responsive monitoring to ensure their baby's comfort and safety. Utilizing an CONTROLLER 33 BLE Sense processor with built-in sensors-including cry detection, environmental monitoring, and proximity sensing-CalmNest automatically responds to the baby's needs. The cradle gently swings when the baby cries, maintains optimal temperature by activating an exhaust fan when needed, and alerts parents to moisture or caregiver presence through Bluetooth-enabled notifications. Developed using platforms like MICROCONTROLLER and Edge Impulse, CalmNest achieves a 92.6% cry detection accuracy, with additional enhancements for stability in temperature and humidity monitoring through a DHT11 sensor and OLED display. Performance analysis confirms that CalmNest successfully balances automated comfort and safety, creating a smart parenting solution that reduces manual intervention and supports a secure, nurturing environment for the baby.

The paper deals with the development of Internet of Things (IoT) which is transforming our day-to-day life. The smart irrigation system developed utilizing the Tonny platform, guarantees smooth Internet of Things connectivity, permitting remote monitoring and management of irrigation procedures in real time. The system continuously monitors soil and environmental parameters by integrating an microcontroller with a DHT11 humidity sensor and a soil moisture sensor. The water pump is turned on by a relay module to automate irrigation when the soil moisture content falls below a set level. Furthermore, a real-time sensor data monitoring LED display is included on-site, allowing customers to examine current conditions right there at the place. Users now have the flexibility to remotely monitor and modify irrigation settings worldwide because to the Internet of Things capacity. In order to address both over- and under-irrigation, the system maximizes water utilization, automates water distribution, and effectively manages water reserves based on real-time data. This technique works well with drip, sprinkler, and surface irrigation systems. It is especially helpful in places where 2G connectivity is scarce, such poor countries. This adaptable and scalable technology increases agricultural output and functions as a vital instrument for contemporary precision agriculture across a variety of geographic regions by preserving water, cutting waste, and improving network stability. By addressing critical challenges in modern irrigation, this technology promotes water conservation, making it a valuable tool for sustainable agriculture.

Transforming smart building management into a digital twin technology is operational efficiency and safety enhancement. This paper presents a digital smart building management system comprising an Raspberry Pi Model designed to successfully achieve communication between sensors and device-related activities. Focused features include gas detection alerts, theft detection based on vibration, air quality monitoring (MQ135), occupancy detection (IR sensors), adaptive lighting control (LDR), and temperature/humidity control via DHT11. Early hazard detection is ensured from fire sensors. Real-time data processing with ThingSpeak integrated it contributed to continuous real-time monitoring. Emergency response would thus use a GSM module. The L293D driver controlled ventilation with both a CPU fan and DC motor. Feedbacks are relayed via real time on an oLED display too. This is a truly reliable, efficient, and safe building management system made possible through integrating digital twin technology with an IoT-based automation mechanism.

In this Research, we proposed a novel approach to develope an energy-efficient home automation system based on IoT technology which can be operated through Google Assistant and Espressif Systems Platform for Rapid Integration and Deployment of IoT Devices with Cloud Connectivity (ESP Rainmaker) app, enabling seamless user experience and customization. The proposed system provides automated control of home appliances and monitors indoor temperature and humidity for providing more comfortable and energy-efficient living environment. The proposed system was then practically implemented using Espressif Systems Processor 32 bit microcontroller two 4 channel relay modules, an IR remote control, a DHT11 temperature and humidity sensor .These low-cost($20.75) hardware makes the system accessible to homeowners seeking to enhance their living spaces in a sustainable and efficient manner. This Research evaluate the significant improvements in temperature and humidity control with an 83% reduction in temperature fluctuations and a 71% reduction in humidity fluctuations. The system resulted in a 22.5% overall reduction in energy consumption, while the water heater showing the most significant reduction at 50% thus reducing the carbon footprint of the household to mitigate the challenges of the climate change.

In recent years, the convergence of technology and daily life has led to significant innovations, among which Augmented Reality (AR) and Virtual Reality (VR) stand out remarkably. AR and VR technologies have transcended their initial applications to permeate various sectors including Home Automation and Internet of Things (IoT) devices. The paper presents the implementation of a Home Automation system, specifically an AR-enabled Plant Watering System using IoT devices, exploring their functionalities and influence in today’s world. The system employs the microcontroller for sensor data acquisition, actuator control, and communication with an AR interface developed using Unity Hub and Vuforia Engine. Real-time data from DHT11 and soil moisture sensors are visualized through an AR interface, enabling intuitive user interaction and control of watering actions. The performance evaluation of our system highlights a sensor accuracy of ±2°C for temperature, ±5% for humidity, and ±3% for soil moisture, with an average response time of 2 seconds. The system demonstrated high reliability with 98% uptime and control accuracy of 95% along with the task success rate of 92% with an average task completion time of 15 seconds. These results underscore the effectiveness of the AR-enabled plant watering system in enhancing user experience and engagement in indoor gardening, while offering significant improvements in accuracy, efficiency, and user interaction.

The Smart Automated Zebra Crossing System operationalizes Passive Infrared (PIR) sensors and Internet of Things (IoT) technology with IP camera-based headcount capabilities to foster pedestrian safety. A data-driven control system protected pedestrian safety by integrating adaptive management structures and intelligent traffic methods. The system actively switches on its crossing systems only after PIR sensors detect approaching pedestrians. IP cameras enabled by advanced computer vision algorithms provide real-time pedestrian counting and density measurement services. Networks based on IoT technology carry system data to produce shifting crosswalk time lengths through dynamic adjustments based on variations in pedestrian numbers. Smart LED warning indicators inform drivers about traffic while adaptive timing systems shorten the time pedestrians and drivers need to wait. Real-time decision capabilities and reduced web dependence emerge from the system's edge computing architecture which ensures operational reliability. Through solar power technology all system components underwent upgrades to become durable in varying weather conditions and maintain sustainable operation in any environment. The gathered system data helps analytical experts predict dangerous traffic areas and peak times which supports better precautionary traffic management and future infrastructure development strategies. This modern framework takes pedestrian safety to new heights while efficiently utilizing roads through combination of real-time detection systems and dynamic adaptiveness with data analytical features. Researchers studied architectural elements of the system together with implementation methods and performance results in order to show how this technology will transform smart transportation infrastructure in the future.

This paper shows system that uses the Internet of Things (IoT) to detect fire and motion detection system which is designed to work in real time and provide emergency alerts within the given region instantly. The system combines fire detectors, small computer units and allowing all parts to communicate in real time whenever needed. The fire detection module employs smoke and temperature sensors to ensure that a fire outbreak in its initial state is detected with high precision. While passive PIR motion sensors bring about the motion detection module to interact with its users. This server helps security officials take quick actions when alerts are triggered. The tests show that the system is very accurate in detecting events like fire and movement. This means it can help prevent problems during a disaster by responding quickly.

Security and observation are fundamental in different applications, counting private security, mechanical checking, and smart city administration. This paper presents a cost-effective, remote human location framework that coordinating an Inactive Infrared (PIR) sensor, Worldwide Framework for Portable Communications (GSM) module, and the stage for real-time checking. The framework identifies human development utilizing the PIR sensor, captures pictures through the sends moment alarms through GSM, empowering clients to get notices on their versatile gadgets. By dispensing with the require for complex wiring, the framework improves effectiveness and ease of arrangement. Outlined to be power-efficient, versatile, and user-friendly, it guarantees dependable security observing. Test comes about show tall precision in human location and quick caution transmission, making it reasonable for different security applications. A comparative investigation with existing frameworks illustrates made strides in discovering unwavering quality and decreased reaction timecite1. This inquire about contributes to the progression of farther reconnaissance arrangements and lays the establishment for future upgrades, such as fueled acknowledgment and cloud-based capacity for improved security administration.

This research work describes the design and application of an automatic train detection and gate control system using the microcontroller and Passive Infrared (PIR) motion detectors. The system is designed to improve safety and automation at rail level crossings through the detection of train movement and the control of the gate accordingly. Two PIR sensors are placed in appropriate locations to monitor the entry and exit of a train. According to sensor inputs, the microcontroller turns on LEDs for visual indication and servo motors for gate operations. The suggested design reduces human error, offers real-time response, and is energy-efficient with low power consumption. The experimental outcomes confirm that the system identifies the train presence and executes gate operations in a timely and guaranteed fashion, and thus it is a good solution for urban as well as rural railway crossings.

This paper presents the design and implementation of an IoT-enabled smart home automation system utilizing the microcontroller to enhance home management through real-time remote control. The proposed system integrates various sensors, including temperature, humidity, motion (PIR), light, and raindrop sensors, to automate tasks based on environmental conditions, thereby improving energy efficiency and convenience. Additionally, the system incorporates an RFID-based door locking mechanism for enhanced security and supports voice commands for hands-free operation, catering to individuals with mobility limitations. A web interface allows users to monitor and control connected devices remotely via Wi-Fi, offering a seamless and user-friendly experience. Emphasis is placed on secure communication protocols to safeguard user data, while a modular architecture ensures scalability for future enhancements. This solution demonstrates the potential of IoT technologies to create energy-efficient, secure, and accessible smart home systems, addressing modern demands for sustainable living.

Agriculture contributes one-third of India's Gross Domestic Product, and a large portion of the population depends on agriculture for subsistence. Farmers' annual income is heavily based on the yield of the crops they produce, which has been steadily declining owing to a variety of issues, one of which we are focused on is bird intrusion in crop fields. The Farmers face massive difficulty in protecting their crops from invading swarms of birds. The most notable birds in this regard are peacocks and parrots, which destroy the harvest in agricultural farm fields such as Corn, Ground nut, etc., Considering a statistical survey of farmers on the proportion of crop damage caused by birds, we would like to present a Internet of Things (IoT) based automated bird detection and repeller system. This work aims to chase peacocks and parrots from the crop fields. The proposed approach has two modules: birds' intrusion detection using a Passive Infrared (PIR) sensor and a Repeller Unit, which consists of an audio amplifier and megaphone to make predator noises that will drive the birds away from the field.

Visually impaired individuals face challenges in mobility, object recognition, and text reading. Existing assistive tools provide partial solutions but lack full integration. This paper presents a low-cost, multifunctional assistive device in the form of wearable glasses, integrating real-time text recognition, obstacle detection, and remote assistance. Built around a Raspberry Pi Zero 2W, the system features an Arducam IMX519 autofocus camera and a tactile button interface for user-friendly control. Real-time audio feedback enhances situational awareness, promoting greater independence. The device was tested at the Rehabilitation Center for the Visually Impaired in Levoča. Performance evaluations confirm high text recognition accuracy and improved navigation support. A comparative analysis highlights its affordability and functionality over commercial alternatives. While effective, the device requires processing speed optimization and hardware miniaturization. This research contributes to affordable and inclusive assistive technology, improving accessibility for visually impaired individuals.

Drowsy driving is a leading cause of fatal traffic accidents worldwide. Drowsy driving has emerged from modern societal trends such as long working hours, heavy reliance on vehicles, and insufficient sleep. Despite considerable efforts by researchers to develop efficient driver drowsiness detection systems, none so far has been widely adopted due to their high cost, intrusive nature, and ineffectiveness in challenging real-life situations. This paper presents a novel, real-time, non-intrusive, and cost-effective driver drowsiness detection system leveraging vision transformers (ViT). Our approach detects the driver’s face from each video frame and classifies the driver’s state as either ‘drowsy’ or ‘alert’ based on the entire facial image, as opposed to previous systems that rely on analyzing specific facial features. We demonstrate that the proposed Vision Transformers-based Driver Drowsiness Detection (ViT-DDD) system surpasses existing state-of-the-art methods, particularly in challenging scenarios such as drivers wearing glasses or sunglasses, or in different lighting conditions. The model was trained and evaluated on two widely used public drowsiness detection datasets, achieving classification accuracies of 98.89% on the NTHU-DDD dataset and 99.4% on the UTA-RLDD dataset. Furthermore, the system was successfully deployed on a Raspberry Pi microcomputer, integrated with an infrared, a GSM/GPS module, and a buzzer to alert the driver and report the drowsiness condition to the vehicle owner. Testing the prototype yielded highly promising results, with the system’s strong performance attributed to the ViT-DDD system and advanced hardware. The promising test results suggest the potential of this system in significantly reducing accidents caused by drowsy driving, with future work aiming to expand its capabilities and integration into broader vehicular systems.

The growing demand for advanced power monitoring underscores the limitations of traditional smart meters, which often lack detailed insights into power quality. This capability has become increasingly important as modern electrical systems are more vulnerable to disturbances and inefficiencies. This paper presents the design and development of a low-cost, real-time power quality and energy monitoring device (PQEm), specifically engineered for easy installation in any electrical setup, whether for bulk metering or submetering of individual units or appliances. The PQEm system integrates a highly precise multiphase analog front-end (AFE) power quality IC with a system-on-a-chip (SoC) platform, enabling the collection of real-time data on key energy metrics such as active, reactive, and apparent power, while also detecting a wide spectrum of power quality disturbances, including sags, swells, interruptions, and voltage imbalances, with a detection latency of less than 5 ms. The PQEm device includes time-frequency analysis to enhance visualization and characterization of voltage and current data. It also supports long-term raw data recording at the native sampling frequency, a feature typically reserved for high-end power quality analyzers. The device has been calibrated and validated through both laboratory testing and real-world applications, offering a cost-effective and user-friendly solution that addresses critical gaps in existing smart metering systems while empowering consumers to monitor power quality issues with greater precision.

Internet of Things (IoT) has revolutionized various industries with interconnected smart embedded devices featuring sensors and actuators. However, providing a sustainable power supply for these devices is a significant hurdle. The finite lifespan of traditional batteries necessitates frequent replacements, leading to increased maintenance costs, downtime, and environmental concerns associated with battery disposal. Energy harvesting offers an admirable alternative by converting ambient energy into usable electrical energy, enabling IoT devices to operate autonomously and sustainably, reducing maintenance needs, and enhancing reliability. Despite their diversity, energy harvesters face common limitations, such as variable energy availability, limited power output, and high initial costs. Recognizing these limitations is essential for informed decisions regarding their use in sustainable IoT applications and their communication performance. Due to the lack of a comprehensive and inclusive study in this context, this article gives an extensive survey on energy-harvesting technologies and their applications in IoT. By drawing insights from over 400 recent publications, this article first introduces a taxonomy by categorizing the existing technologies based on their energy sources into five general domains, i.e., photovoltaic, vibration and kinetic, radio frequency, thermo electric, and chemical and biological. Then, it expands this categorization into 11 subdomains and delves into the working principles of every technology. Besides surveying potential IoT applications, this comprehensive study covers simulation-facilitating mathematical models, pros, cons, and commercial-off-the-shelf modules for each harvesting technology. It also includes evaluative simulations to analyze their output power behavior, focusing on their impact on wireless communication performance. Finally, by discussing the existing research trends, the survey aims to provide informed decision making and foster the development of innovative, sustainable IoT solutions for a green future.

The pulse serves as an early indicator of metabolic disorders and provides a measure of cardiovascular and pulmonary function. Monitoring pulse activity can help detect and prevent cardiovascular diseases and related conditions. Advances in sensor technology have enabled the integration of wearable devices into various research fields, offering an objective method for assessing both physical and mental health. This paper introduces an intelligent analysis system for wearable pulse detection using flexible piezoelectric sensors. The system’s hardware includes piezoelectric sensors, a signal amplifier, and a controller, which together ensure the reliable acquisition of radial pulse wave signals. To process the collected data, the system applies ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-WPD (Wavelet packet decomposition) and cubic spline interpolation algorithms. These techniques help eliminate noise interference and correct baseline drift. An adaptive threshold detection algorithm is also employed to extract key time-frequency features from the pulse signals. The experimental outcomes demonstrate that the system is capable of effectively monitoring the human radial pulse wave, achieving noise reduction and single-period segmentation of the acquired signals, extracting time-domain characteristics of the pulse, and analyzing the frequency distribution of the pulse. These results indicate that the system’s ability to conduct continuous monitoring and real-time pulse wave analysis may significantly contribute to the enhancement of physiological health monitoring and facilitate the early detection and prevention of diseases.

With the increasing emphasis on sustainable agriculture, there is a need to stress on technological advancement which can optimize the growth of plants under controlled conditions. We are proposing here an automated greenhouse monitoring system having a low cost which makes sure that parameters like temperature, humidity, soil moisture and the light intensity are constantly supervised by the Automatic Control. The specific system described in this paper includes several sensors and is a completely automated, cost-effective Raspberry Pico Microcontroller. Use the Internet (IoT) of things to provide remote monitoring and management so that users can access live data so that they can adjust anywhere. All agricultural processes are dealt with responsible management due to automation so there is a practical move towards targeted assistance. The greenhouse design which was drawn looking at the working life was aimed at suiting different climatic conditions and sustaining the various culture cycles. This is meant to encourage sustainable farming by providing a cheap and simple solution that improves greenhouse management. The system achieves high accuracy in monitoring key parameters, with temperature and humidity measurements accurate up to 98%, while keeping constant real-time visuals on the parameters. This stable environment positively impacts plant productivity while promoting responsible resource conservation. The results demonstrate the practical potential of IoT in agriculture, highlighting its application in enhancing productivity and sustainability.

With the rise of smart technologies, numerous tasks can now be automated without manual intervention, optimizing resource use in various applications. This work introduces-IrriSmart-a test bed for a smart, automatic irrigation system designed for efficient indoor plant care, particularly suited for indoor gardens, nurseries, and greenhouses. IrriSmart leverages advanced capacitive soil moisture sensors, which offer increased durability and accuracy by measuring soil dielectric permittivity, avoiding the corrosion and sensitivity issues associated with traditional resistive sensors. It integrates the microcontroller, a DHT11 sensor for temperature and humidity monitoring, and a DC pumping motor, all coordinated via a relay module. Additionally, it also has a water flow sensor for precise water regulation. IrriSmart uses the IoT platform to effectuate real-time remote monitoring and control from any internet-connected device. The sensor data is also logged, providing users with insights into plant conditions and system performance. The key advantages of the proposed IrriSmart system include reduced maintenance requirements, comprehensive environmental data collection, and remote operability, making it a robust, cost-effective solution for automated irrigation in controlled environments.

To automate several household tasks, it is proposed to have an automation system that combines an microcontroller with a laser and light-dependent resistor (LDR) along with some other sensors. The system detects the entry and exit of a person, senses the internal environment and takes necessary actions. This approach demonstrates a simple yet effective method for automating home functions based on motion detection. The light, temperature and air quality are constantly monitored and corrected to provide a necessary ambiance, that offers convenience, energy savings and enhanced home management.

This paper introduces an advanced IoT-driven system aimed at identifying LPG gas leaks and fire hazards while providing automated safety mechanisms and real-time monitoring the mq2 sensor is utilized for detecting variations in gas concentrations and the DHT11 sensor monitors temperature fluctuations to identify potential fire risks an controller serves as the core controller processing data from the sensors and initiating safety responses such as triggering an audible alarm rotating a servo motor to shut off the gas supply and powering an exhaust fan to vent the leaked gas to enable remote oversight the controller module communicates with the controller and transmits data to the IoT platform allowing users to monitor conditions in real-time additionally the system integrates GSM-based functionality to deliver alerts via SMS or calls complementing app notifications and emails this comprehensive design ensures rapid response to emergencies offering a reliable and practical safety solution for environments vulnerable to gas leaks and fire outbreaks.

The rapid growth of industry and cities in today's world has made it more important to keep an eye on air quality in real-time. This promotes environment and public health protection. The proposed model includes air quality using the (IoT) Internet-of-Things and by calibration of sensors. This system gives real time, up to the minute air quality info. It brings together different sensors like DHT11, MQ135 and BMP180 for the analysis of the amount of carbon dioxide (CO2) present, and other parameters like humidity, temperature, and pressure with the combination of micro-controller which is a low-cost System-on-Chip (SoC) which includes several features like WiFi Connectivity and low power consumption. It lets the sensors talk to a cloud-based. Quick warnings and predictions tell people whenever their surrounding gets polluted, so officials and locals can act accordingly. It appears that this device can be used to measure the air quality for both indoor and outdoor system and also close to polluted areas, where safety measures can be implemented and precaution can be taken for household near to industrial area. This will assist individuals in determining what safety measures are necessary for those who are close to polluted areas to the certain range of 15m in all directions. This tech fix helps manage air quality and gives communities easy-to-use info about their surroundings to take action.

Traditional methods of orchid disease detection rely on manual inspection, which is both labor-intensive and inefficient. Modern smart solutions integrate Internet of Things (IoT) and Our approach leverages IoT for real-time monitoring of critical environmental factors, such as temperature and humidity, to enhance disease prediction accuracy, with a specific focus on Phalaenopsis orchids. We propose OrchidTalk-v2, a novel and dynamically adaptable system designed to improve disease risk monitoring. OrchidTalk-v2 integrates an intelligent spore germination sensor with Continuous Wavelet Transform (CWT) for feature extraction, feeding the data into a three-dimensional Convolution LSTM network model. The system achieves a precision rate exceeding 93% and provides early alerts for disease outbreaks with a recall rate above 92.75%. This marks a significant advancement in prediction accuracy for orchid disease detection within controlled greenhouse environments.

Power Quality Analyzers (PQA) play an important role in monitoring and controlling health of the electrical systems. They can report the fluctuations in the field measurements with different power quality issues as well as due to load variations. Internet of Things (IoT) is a potential technology to design Smart PQAs for remote monitoring and easy integration of the field information on the cloud platform using gateway units. This paper focuses on the development of a Smart PQA system using low-cost IoT hardware and software design solutions. The hardware development is completed using microcontroller in combination with Wi-Fi gateway and SIM900A GSM gateway. The real-time field data is gathered at the ThingSpeak platform for future analysis, while GSM-based design ensures timely alerts to the end users for any major fluctuation in the power supply. The performance of the proposed low-cost system has been compared with the readings obtained from FPGA-based conventional PQA and standard Fluke Meter when connected to different loads. The proposed system output values are tabulated and compared using graphs. The proposed system is expected to be useful for the critical assessment, monitoring, and control of power quality parameters in various commercial and residential premises.

Rising concern over livestock emissions and their contribution to climate change demands innovative and cost-effective monitoring solutions. This study introduces and validates a low-cost, LoRa based system for the continuous monitoring of methane (CH4) and carbon dioxide (CO2) emissions in cattle. Unlike previous fixed installation systems, our design features a movable cattle collar equipped with gas sensors, enabling real-time data collection directly from the emission source. The system combines advanced sensor technology with long-range LoRa communication, thereby providing a scalable solution for remote agricultural environments. The experimental results demonstrate that the cattle collar system achieves higher accuracy in detecting CO2 emissions owing to its proximity to the emission source, outperforming fixed systems. The closed feeding slot design showed a comparable accuracy for CH4 detection. Both systems proved capable of reliable data transmission over distances of up to 400 m, making them ideal for large-scale deployments. In terms of cost, our system is significantly more affordable than other commercially available systems. The collar’s portability, low power consumption, and long battery life make it well-suited for continuous monitoring in both indoor and outdoor settings. This study highlights the potential of a movable, low-cost solution for monitoring livestock emissions, contributing to agricultural sustainability and climate change mitigation by providing accurate, real-time data for effective emission management strategies.

The wireless body area network (WBAN) integrates wearable devices and Internet of Things (IoT) sensors in the human body, enabling real-time monitoring of physiological parameters for improved healthcare. Ensuring accurate and reliable data transmission is crucial to maintain system performance. To address this, we propose a novel anomaly detection framework that uses a two-stage convolutional transformer network (ConvTransformer) architecture, specifically designed to handle both point anomalies and contextual anomalies. In the first stage, we trained a ConvTransformer model to distinguish between human data and point anomalies. These out-of-range values may indicate abrupt irregularities in individual sensor readings. After identifying and filtering out point anomalies, the second stage applies another ConvTransformer model to the remaining data to detect contextual anomalies. These are more complex and involve simultaneous irregularities in multiple physiological signals (for example, heart rate, body temperature, and electrocardiogram), which may suggest more significant health concerns. This two-stage detection approach ensures more precise and robust anomaly detection. The first model achieved 99.66% accuracy in detecting point anomalies, while the second model reached nearly 99.76% accuracy in identifying contextual anomalies, showcasing the efficiency of the ConvTransformer architecture in WBAN applications for detecting anomalies.

Organizational vehicle density management and automated interventions without humans are gaining traction with traffic monitoring and control systems based on the Internet of Things (IoT). Regarding controlling pollution and regulating vehicle density, the current traffic control technologies are ineffective regarding real-time adaptability, efficient classification techniques, and scalable infrastructure. Therefore, a system that uses the IoT is necessary for the real-time identification and regulation of automobiles that release excessive pollution. As a result, the study suggests a TCSP-PC, or Traffic Control System Process for Pollution Control, to manage waste in smart cities. This control system incorporates capsule networks for vehicle categorization based on emission behaviour and employs IoT sensors for data collecting. This system primarily seeks automobiles that cause much pollution during their travels by monitoring their emission infractions. To initiate initial pollution control, such vehicle data is used to detect infractions at any interval. The IoT paradigm aids this process through traffic control regulations. Additionally, capsule networks are used to learn the emission behaviours of both environmentally good and polluting automobiles on the roadside. Through capsule networks that categorize vehicles according to their emission behaviour on the roadside, traffic control systems can monitor and control autos. The vehicles may be categorized as either environmentally friendly or polluting. The system finds polluting vehicles, organizes them using capsule networks, and then suggests pollution restrictions based on the IoT. Utilizing pre-existing smart city resource data, this novel approach combines technological concerns with rules about traffic control and environmental impacts. Comparative analysis with existing models (VEQM, MPTC-PRP, MWIS, and CONV-BI-LSTM) demonstrates significant improvements in impact verification (+12.48%), classification rate (+15.36%), and traffic control efficiency (+121.5 vehicles/km). Additionally, the system achieves an average 9.77% enhancement in data analysis rate and a notable reduction in processing time (-2.06s), ensuring real-time responsiveness. These findings highlight the potential of TCSP-PC to optimize urban traffic flow, reduce emissions, and improve decision-making in smart city infrastructure.

This study investigates the problematic characteristics of contemporary methods for remote and portable patient monitoring. The consideration is based on recent breakthroughs in information technology and progressive strategies for processing and storing biomedical data. The proposed system represents the Medicine 4.0 concept's next technological leap. Existing methods for remote and portable monitoring of a patient's status have several vital disadvantages in system flexibility and the convenience of processing and evaluating biomedical data, according to an analysis of these systems. The authors have created a new concept for a Remote Patient Monitoring System (RPMS) that allows for undetectable wear during the patient's daily activities. Small modules comprising a microcontroller and a collection of medical sensors transfer data in real-time via wireless Internet of Things (IoT) technologies to a cloud service for the attending physician's processing and visualization convenience. Based on the proposed concept, the authors created a structural diagram of the experimental RPMS and its built prototype. Amazon Web Services (AWS) is used for the real-time processing of biomedical patient data and its subsequent analysis using a graph-based information visualization system. The performed experimental procedure confirmed that the developed experimental RPMS has minimal latency in transmitting data to AWS; it can alert both the patient and the physician about the need for emergency intervention or treatment adjustments, even if critical indicators are detected. Additionally, the proposed system can incorporate components of expert systems and Artificial Intelligence (AI) systems. The authors advocate using the accomplished system for functional diagnostics specialists, paramedics, and cardiologists in medical facilities and the military medical system for rapid diagnosis and direct monitoring of troops' health state on the battlefield.

Monitoring air quality in smart campuses is essential for safeguarding the health of the academic community, as air pollution in university environments can lead to respiratory and cardiac issues. While the Internet of Things (IoT) offers a suitable solution for monitoring and notifying about air quality in smart campuses, it is of paramount importance that these notifications occur in real-time to ensure information reaches users with minimal latency. This paper presents an architecture for collecting, storing, and notifying about atmospheric pollutants in a smart campus and analyzes the use of Real-Time Operating Systems (RTOS) for managing the system. The evaluation focuses on IoT technologies, represented by the end node, gateway, and cloud server. The experimental results show that using RTOS reduces the average transmission interval by 37.5% and achieves higher transmission and reception throughput compared to non-real-time operating systems. For instance, in a monitored location, the transmission and reception throughput reached approximately 178.23 and 164.18 with RTOS, compared to 110.97 and 99.41 without RTOS, respectively.

The word Internet of Things (IoT) is designed for diverse sensing devices that are considered to capture real-world data and initiate corresponding actions. Sensor nodes consumed their built-in battery capacity allowing them to perform several tasks and act together with each other. Optimizing energy and extending the lifetime of wireless sensors are highly concerned with data transmission in a network. The use of energy at the cluster level to prolong the network’s life, and the sensors’ battery needs a mechanism during the transmission of data in a wireless network. There is also multiple research techniques presented for modelling IoT-based smart systems, but energy savings with efficiency and to validate smart agriculture systems have not been earlier adopted and focused. Our primary purpose is to save, enhance, and optimize the network life of wireless networks in smart agriculture by using an ensemble formal approach with IoT. Our research is based on three phases: firstly, we proposed an algorithm for saving energy consumption and data aggregation in a smart agriculture case to use the NS2 Simulator for experimental results. Secondly, we also developed a model using an activity diagram and transformed it into the formal language of TLA+ (Temporal Logic of Action). Lastly, the correctness properties of a model are verified by using the TLC (Temporal Logic Checker) with the model-checking capability of the TLA+ toolbox. The result of our proposed technique has been evaluated and shows an efficient performance in terms of energy consumption, delay, and network life.

One of the leading causes of early health detriment is the increasing levels of air pollution in major cities and eventually in indoor spaces. Monitoring the air quality effectively in closed spaces like educational institutes and hospitals can improve both the health and the life quality of the occupants. In this article, we propose an efficient indoor air quality (IAQ) monitoring and management system, which uses a combination of cutting-edge technologies to monitor and predict major air pollutants like CO2, PM2.5, TVOCs, and other factors like temperature and humidity. The aim is to create an intelligent environment for IAQ. The data is captured and monitored using an Internet of Things network of sensors, manufactured by ourselves, in different lecture rooms at the university. A long short-term memory neural network is proposed to forecast IAQ. Then a decision tree regressor is used to identify the relationships between temperature, humidity and different pollutants like CO2 and PM2.5.

Agriculture is the backbone of every country as it fulfills the necessities of human beings as well as animals and also supports the country’s economy in terms of revenue by exporting food products to the international market. But due to rapid growth of industries and population food and water shortage is becoming a very critical issue. Especially in Pakistan shortage of water and unpredicted weather conditions are affecting agricultural areas, and as a result, food shortage is increasing. A solution to handle the water shortage is a controlled watering system for crops. In this paper, we describe two different IoT-based systems for crop soil moisture, soil temperature, and outdoor temperature and humidity monitoring, one is Wi-Fi based while the other is a GSM-based system. This helps us monitor the soil moisture content and other parameters in real-time which will help agriculture researchers and farmers to use water according to system reports. The system uses an ARIMA (autoregressive integrated moving average) machine learning model to predict the soil moisture for the next ten days which will help the farmers to arrange the water in Hyetal areas where canal water is not reachable. The predictions are compared with real-time data and the success rate of the ARIMA ML model is from 80−90% . The proposed research will open the door for agriculture researchers to gather new datasets and predictions by adopting this proposed research which will be beneficial to farmers to cultivate the crops and fruits in hyetal lands according to data recommendation.

This paper presents a theoretical overview of peat land forest fire management, focusing on the integration of IoT-based monitoring systems and Fire Weather Index (FWI) for improved fire prediction and mitigation. The paper examines the impact of wildfires on carbon loss in Southeast Asian peat land forests and reviews existing IoT solutions for monitoring environmental conditions, such as temperature, humidity, and soil moisture. Furthermore, the paper surveys the current advancements in FWI systems, The research discusses challenges related to integrating IoT with FWI systems and considers critical factors such as topography, altitude, and human activities. By synthesizing existing literature, this paper aims to outline the current state of research and propose directions for future work to enhance fire risk prediction and management in peatland ecosystems.

Ambient signatures related to the power grid offer an under-utilized opportunity to verify the time and location of sensing data collected by the Internet-of-Things (IoT). Such power signatures as the Electrical Network Frequency (ENF) have been used in multimedia forensics to answer questions about the time and location of audio-visual recordings. Going beyond multimedia data, this paper investigates a refined power signature of Electrical Network Voltage (ENV) for IoT sensing data and carries out a feasibility study of location verification for IoT data. ENV reflects the variations of the power system's supply voltage over time and is also present in the optical sensing data, akin to ENF. A physical model showing the presence of ENV in the optical sensing data is presented along with the corresponding signal processing mechanisms to estimate and utilize ENV signals from the power and optical sensing data as location stamps. Experiments are conducted in the State of Maryland of the United States to demonstrate the feasibility of using ENV signals for location authentication of IoT data.

Ensuring safe and independent mobility for visually impaired individuals requires efficient obstacle detection systems. This study introduces an innovative smart knee glove, integrating IOT technologies for real-time obstacle detection and alerting. The system is equipped with ultrasonic sensor, PIR sensor and a buzzer, with data processing managed by an microcontroller. The paper aims to address s by integrating ultrasonic and PIR sensors to detect obstacle types, distances and motion. A buzzer provides auditory alerts, warning users of obstacles, particularly in critical situations. Future work can expand on this model to assist individuals who are both visually impaired, further enhancing their usability and impact.

Illegal cutting and smuggling of valuable trees, such as sandalwood, are one of the serious threats to the environment and the economy. The project is the development of a smart real-time monitoring system for the prevention of illegal cutting or theft of these trees using IoT and GSM technologies. The main controller for the system is controller that receives readings from the sensors and transmits them to the cloud. The application generates real time notifications to the administrators, while an offline SMS notification is provided by the GSM module (SIM800C) in case of no network coverage. The system is affordable and scalable, and it can be used to protect valuable tree species such as teak, rosewood, and timber, supporting forest conservation.

Health is an essential part of existence. However, a lot continued to lack access to adequate healthcare services. It is attributed to constraints in the technology employed in hospitals and barriers to accessing these facilities. The Internet of Things (IoT) and wearables consist of tiny embedded devices equipped with sensors that gather data from their environment. In contemporary society, individuals have introduced complexity and bustle into their lives, sometimes neglecting their health owing to demanding schedules. Contemporary health monitoring technologies can prove onerous and burdensome for patients, resulting in inadequate adherence and postponed identification of health concerns. The IoT is currently a prominent issue, providing several solutions across various sectors, particularly in healthcare. The Internet of Things (IoT) is utilized in healthcare for illness detection as a preventive measure, disease treatment as a therapeutic solution, and disease monitoring as part of the healing process. Wearable gadgets have been created within the IoT framework to assist individuals in obtaining appropriate treatment. This study employs the ESP8266 WiFi Module to provide IoT connections, alongside heartbeat and DS18B20 sensors for assessing patient heart rate and body temperature. This technique employs a GPS module to accurately ascertain the precise position data of the relevant persons. The measurement system, comprising wearable sensors, continuously monitors patient indicators. Subsequently, transmit the measured signals to the Android interface using a wireless connection. Should the established critical values for the patient be surpassed, the corresponding values along with the patient's real-time location are transmitted to both family members and the physician by email and Twitter notice. The wearable measuring device enables patients to remain active within their social surroundings, facilitating a confident lifestyle.