Development of Smart Coal Miners Helmet

Conventional fire protection systems, characterized by low installation costs, often lack the sophistication to provide optimal protection, especially in high-rise buildings. This study aims to refine the operational productivity concerning fire prevention and evacuation by embracing a detailed fire safety framework that leverages Internet of Things (IoT) capabilities. This prototype integrates SMS and phone call alerts to facilitate timely response in case of fire detection. The system utilizes three key sensors: a KY-026 flame sensor module, an MQ-2 gas and gas sensor, and an LM35 temperature sensor. Testing results indicate significant sensor value variations between normal and fire conditions. The KY-026 flame sensor module, for instance, exhibited an average reading of 137.3 under normal conditions and 895.2 during fire detection. Similarly, the MQ-2 sensor recorded 1234.7 ppm and 4237.8 ppm, respectively. The LM35 temperature sensor measured 28.34°C and 48.46°C under normal and high-temperature conditions. Despite the sensors showcasing commendable efficacy, they displayed a minor error margin fluctuating between 0.04% and 1.08%.

Nowadays, the variation of the diseases revolution had become a major thread to the human kind population. The repercussion of this uncontrolled situation had led to the increasing trend regarding to the number of the patient those visited the hospital in order to obtain a treatment from the professional in the medical sector. As the number of the patient is keep increasing by the day, the number of hospital staff still not able to meet the requirement in providing the optimum service to all of their patients. The development of this project is to reduce the burden bear by the hospital staff especially the doctor by the implementation of the IOT technology. The purpose of this study is to develop a system based on the ESP8266 microcontroller that internally installed a WIFI shield provide internet connectivity easily for the user to measure the heart rate and the body temperature. By the help of Arduino environment platform, the IOT based project can be developed. This project will focus in capturing and measure the two parameters from the human body which are the heart rate and the body temperature. The sensor used are heart rate sensor, MAX30100 and the body temperature sensor, MLX90614. As the results, this system will process the raw data from the two sensors into a desire value and display the data on the OLED display and the application display. The implementation of IOT technology to this system will provide a continuous data streaming through thinger.io website and showing a live development of patients for the user who is has the permission access by only using their own smart devices. Based on the finding, its shows that the comparison percentage deviation between heartbeat sensor and blood pressure monitor is 3.37% and the comparison percentage deviation between body temperature sensor and digital thermometer is 0.54%. The Graphical User Interface through Internet of Things (IOT) application system is proven to monitor accurately for heartbeat sensor measurement and body temperature sensor measurement.

The increase of emergency situations that cause mass panic in mass gatherings, such as terrorist attacks, random shooting, stampede, and fires, sheds light on the fact that advancements in technology should contribute in timely detecting and reporting serious crowd abnormal behaviour. The new paradigm of the ‘Internet of Things’ (IoT) can contribute to that. In this study, a method for real-time detection of abnormal crowd behaviour in mass gatherings is proposed. This system is based on advanced wireless connections, wearable sensors and machine learning technologies. It is a new crowdsourcing approach that considers humans themselves as the surveillance devices that exist everywhere. A sufficient number of the event’s attendees are supposed to wear an electronic wristband which contains a heart rate sensor, motion sensors and an assisted-GPS, and has a wireless connection. It detects the abnormal behaviour by detecting heart rate increase and abnormal motion. Due to the unavailability of public bio-dataset on mass panic, dataset of this study was collected from 89 subjects wearing the above-mentioned wristband and generating 1054 data samples. Two types of data collected were: firstly, the data of normal daily activities and secondly, the data of abnormal activities resembling the behaviour of escape panic. Moreover, another abnormal dataset was synthetically generated to simulate panic with limited motion. In our proposed approach, two-phases of data analysis are done. Phase-I is a deep machine learning model that was used to analyze the sensors’ collected readings of the wristband and detect if the person has indeed panicked in order to send alerting signals. While phase-II data analysis takes place in the monitoring server that receives the alerting signals to conclude if it is a mass panic incident or a false positive case. Our experiments demonstrate that the proposed system can offer a reliable, accurate, and fast solution for panic detection. This experiment uses the Hajj pilgrimage as a case study.

This paper presents a case study in the creation and utilization of an Internet of Things (IoT) device. An Arduino-based gas leak detector is utilized for the demonstration of IoT technologies and their advantages. The gas leak detection device focuses on detection of Liquified Petroleum gas. Upon detection of a leak, the device can communicate over the internet using cellular networks, thus helping to alleviate Wi-Fi and ethernet failure issues such as loss of power in a natural disaster. Because of the device’s resilience, it can help ensure the user is notified of the emergency as soon as possible. This automated gas leak monitoring system is reliable and will not be put out of action in a disaster or emergency situation.

As the construction of coal-fired power bases in China accelerates,the amount of ecological damage and environmental pollution caused by this and their subsequent operation and servicing becomes more serious. It should be a priority to establish the ecological and environmental risks posed by coal-fired power bases,to assess their impact on ecosystems and human health,and to develop reasonable strategies for the protection,governance,and restoration of the environment. A sustained and methodical program of ecological and environmental monitoring is the basis for solving these problems. However,currently,the monitoring of technical systems still presents several operational shortcomings,such as low levels of automation,high operating costs,and poor spatial and temporal coverage. At the same time,significant developments in the Internet of Things are improving both the production efficiency of data collection and the way in which data are captured. Accordingly,we believe that the Internet of Things could be one of the more effective ways of solving the problems inherent in the current monitoring of technical systems,in respect of the sensing layer,transport layer,supporting layer,application layer,and client layer. For the study area in the current work,we used Xilinguolemeng in InnerMongolia,an area that is rich in coal resources and one of the principal regions for coal-fired power base operation in China. To obtain the reference information regarding the problems of the atmospheric environment,meteorological data,surface water,groundwater,and soil environment,we needed to deploy the appropriate number of relevant sensors around the study area,after having established the principles for selecting the most suitable sensors. The data were transferred to the central platform via wireless sensor networks and the general packet radio service. Then,we analyzed these data and determined the state of the ecological systems and environmental pollution. At the same time,the platform provided the following functions: monitoring of environmental quality,environmental impact assessment,environmental management and decision-making support, ecological efficiency analysis, and environmental emergency response. The government,especially the environmental protection department,and the company can both easily obtain ecological and environmental information using a technical system based on the Internet of Things. The advantages of the Internet of Things system are as follows. First,it provides a large amount of high-quality data on the ecology and environment because of the avoidance of errors caused by manual operation. Second,the data are available for analysis in near real time. This means that problems can be identified quickly and that data are handled online,such that the builders and managers can respond quickly,helping in the implementation of ecological restoration and environmental protection policies and measures. Finally,the cost of monitoring and management is reduced. Therefore,technical systems for the ecological and environmental monitoring of national large-scale coal-fired power bases,which are based on the Internet of Things,can satisfy the requirements for providing high-quality ecological and environmental data at relatively low cost. Because of the large number of coal-fired power bases,ecological and environmental monitoring based on the Internet of things,offers the advantages of low cost,high precision,high universality,and widespread appeal.

The article examines the application of Internet of Things (IoT) technologies to enhance safety levels in the construction industry. The main causes of accidents at construction sites are analyzed, and innovative solutions based on IoT devices are proposed. As an example, the intelligent «Smart Helmet» with integrated sensors for environmental monitoring and worker condition assessment is considered. The advantages of IoT solutions, their impact on reducing injury rates, and examples of successful implementation in various countries are studied. The work also examines methodological aspects of evaluating the effectiveness of IoT system implementation and provides recommendations for their adaptation in Kazakhstan.

Internet of Things is a phenomenon of information technology not only playing a role in the economic field, but also in the field of education. Today every teacher, especially at Vocational High Schools (SMK) is required to teach information technology, one of which is the Internet of Things. As such, it meets the needs of the industrial world which requiring skills and competitiveness in the technology from the vocational school graduates. This encourages teachers to always update knowledge about the development of information technology. However, not all information technology can be taught optimally, especially sensors material and its implementation on the Internet of Things. This occurs as the Internet of Things is a fairly new technology. In addition, the time for teachers to learn the latest technology is limited due to unsupported facilities and administrative tasks that need to be completed immediately. This has an impact on the minimum level of teacher mastery for sensors that support Internet of Things technology including teachers at the SMK Walisongo Semarang. Through the Community Service (PkM) program at the Universitas Semarang which was held on Tuesday, November 2, 2021, the PkM team expected an increasing knowledge of the implementation of sensors supporting Internet of Things technology for teachers at SMK Walisongo Semarang. There are 4 types of sensors used in this PkM program, namely proximity sensors, humidity sensors, temperature sensors, and light sensors which will be presented using the practical method. The PkM that has been carried out, it is found that the results of this service is that the knowledge of teachers on Internet of Things sensors has increased up to 75%.. It is hoped that this program will be a will increase teacher knowledge at the SMK Walisongo Semarang and can help them create vocational graduates who have the skills and competitiveness in the world of technology.

In the last decades the health care developments highly rise the level of ages of world population. This improvement was accompanied by increasing the diseases related with elder like Dementia, which Alzheimer’s disease represents the most common form. The present studies aim to design and implementation a medical system for improving the life of Alzheimer’s disease persons and ease the burden of their caregivers. AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patient’s future health, and recommend better treatments. AI goes beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. Diagnosis is about identifying disease. By building an algorithm we can diagnosis chest X-ray and determine whether it contains disease, another algorithm that will look at brain MRIs and identify the location of tumours in those brain MRIs health of the patients lab values and their demographics and use those to predict the risk of an event. A Smart IOT Interactive Assistance is a technological device that continuously monitors the stability of an Alzheimer’s patient, indicates their position on a map, automatically reminds them to take their prescriptions, and has a call button for any emergencies they could experience during the day. The system has two components, one of which the patient wears and the other of which is an IoT platform application utilized by the caregiver. The motion processing unit sensor, GPS, heart rate sensor with microcontrollers, and LCD display were used to construct the wearable device. An Internet of Things (IoT) platform supports this device, allowing the caregiver to communicate with the patient from any location.

In this rise in the technology era, many industrial revolutions have emerged in their routine functions. One such technology is the “Internet of Things”. This is further named as “Industrial Internet of Things (IIOT) “when this technology is purely applied across any application in industries. Here in this paper, one of the real-time challenges prevailing in the oil and gas refinery industry is taken, which is “oil or gas leakage along the pipeline”. The gas leakage is also meant as a loss of energy happening during transmission. As of now, such leakages are detected only by breakdown maintenance; the testing of the tube is done only after a huge loss has occurred. This traditional method can be replaced with a live detection mechanism employing the Internet of Things for monitoring the health condition of the pipe continuously. In other words, it means that the detection of leakage can be determined at the initial stage itself, rather than allowing it to result in a huge loss of energy. The audio signal of the gas flow in the healthy tube (without leakage) is trapped, and it is being considered as the train data. A similar audio signal of the gas flow in the leakage tube (i.e., the tube with leakage) is also captured and considered as test data. This test data and train data are fed to Edge Impulse Software for anomaly detection using the k-means algorithm. This method of predictive maintenance, using Edge Impulse, would convert the gas tube into a live detection mechanism throughout its length and alert with an early warning mechanism whenever gas leakage occurs.

The life of every person is getting simplified by the continuous development of different technologies. The age of using manual systems is gone. Now, everyone is dependent on automatic systems. For this, nowadays, the Internet of thing (IoT) is playing a vital role in making things easier and simpler for us. It acts as a developing network by considering a number of objects which can share information among each other and fulfill our work. A huge number of applications are coming forward in this technology. One of the applications of IoT is home-automated system. IoT works more efficiently to save electric consumption. It uses many wireless devices for controlling the functioning of wireless devices used in it. All the gadgets or nodes used in home automation are controlled remotely. All the home appliances were controlled using this automation technology. A large number of wireless sensors are used to detect the room temperature, leakages of gas, short circuit, etc. All these sensors work together and communicate with each other using messages by GSM and then send it to the local-based server installed in the house. Thus, the server coordinates all the sensors to make the home fully automated.KeywordsIoTHome automationWi-fiSensorsGSM

Several internal and external dangers might threaten a laboratory, the institution, and the public if not addressed properly. These hazards are addressed by our extensive laboratory monitoring system. This device detects elevated temperatures, gas leaks, fires, and solution pH. Elements of this system include an Esp32 microcontroller, LCD, Arduino Uno, mq2 gas sensor, DS18B20 temperature sensor, flame sensor, relay, water pump, and pH sensor for acidity measurement. The Arduino IDE controls sensors. Once the temperature reaches 50 degrees Celsius, an alarm, red LED, and mobile phone notification activate. Gas leaks activate a green LED, an alarm, and a message. Flame sensors notify and start water pumps when fires occur. Gas leaks and fires activate a buzzer. The IoT offers essential incident reporting and remote laboratory monitoring. The code is written in Arduino C++.

In smart cities, prioritizing child safety through affordable technology like the Internet of Things (IoT) is crucial for parents. This study seeks to investigate different IoT tools that can prevent and address accidents involving children. The goal is to alleviate the emotional and financial toll of such incidents due to their high mortality rates. This study considers articles published in English that use IoT for children's healthcare. PubMed, Science Direct, and Web of Science databases are considered as searchable databases. 273 studies were retrieved after the initial search. After eliminating duplicate records, studies were assessed based on input and output criteria. Titles and abstracts were reviewed for relevance. Articles not meeting criteria were excluded. Finally, 29 cases had the necessary criteria to enter this study. The study reveals that India is at the forefront of IoT research for children, followed by Italy and China. Studies mainly occur indoors, utilizing wearable sensors like heart rate, motion, and tracking sensors. Biosignal sensors and technologies such as Zigbee and image recognition are commonly used for data collection and analysis. Diverse approaches, including cloud computing and machine vision, are applied in this innovative field. In conclusion, IoT for children is mainly seen in developed countries like India, Italy, and China. Studies focus on indoor use, using wearable sensors for heart rate monitoring. Biosignal sensors and various technologies like Zigbee, Kinect, image recognition, RFID, and robots contribute to enhancing children's well-being.

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0–600 m) and heights above the ground (0–2 m) in an open environment and indoor (1st Floor–3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.

Fire is a reoccurring problem in the Philippines, and is costing the government billions of pesos in property damage along with several hundred fatalities every year. Existing fire alarm systems are prone to false alarms because it relies solely on detecting smoke. Unmonitored heat and gas leakages were two of the top causes of fire incidents in previous years. Incorporating the MQ-5 and MQ-2 gas sensors with the AMG8833 thermal imaging camera will allow for an accurate fire monitoring system that is less prone to false alarms. Using the two gas sensors will allow for the monitoring of LPG, Butane, and CH4. Also method of interpolating the display of the AMG8833 from 8x8 pixels to 70x70 was developed and sensor data was sent wirelessly to ThingSpeak. The thermal camera and the gas sensors both correlated to accurately assess fire hazards. A wireless communication with the user was used on the system to shorten the time of response of the fire fighter when fire alarm is triggered. The sensors are connected wirelessly over 6LoWPAN and uses ANN (artificial neural network) for forecasting possible future sensor reading and identification. A best validation performance of 65.3892 at epoch 72 was achieved running the Matlab Neural Network Toolbox using the Scaled Conjugate Gradient Algorithm.

The increasing use of wireless communication technologies has made IoT and machine-to-machine (M2M) connections crucial to a wide range of industries. Smart buildings are an excellent example of an IoT/M2M application. Issues such as a lack of a convergent solution, high costs, restricted wireless transmission range, user-unfriendly interfaces, and the inability to integrate IoT and M2M technologies. Consequently, this article suggests a more effective method for for Machine-to-Machine (M2M) and The Internet of things smart building systems: heterogeneous wireless networks that include both WSNs and MCNs. The suggested system is a cost-effective embedded system that includes Several functional actuators, sensors, and modules for Arduino and NodeMCU, ESP32CAM boards allow for data collection and control across a variety of heterogeneous communication methods like Bluetooth, Wi-Fi, and global system for Mobile (GSM). All collected data is issued to the ThingSpeak platform, allowing the building system to be monitored via the Thing Speak webpage or the UR Smarthome app. One of the researchs most important results is that it can give the server precise information with extremely little delay, allowing users to quickly control and monitor remotely the proposed system that consists of several innovative services names indoor applications(fire detection and gas leakage detection, intrusion alarm by ultrasonic, medicine reminder by RTC module, light and fan control, smart door) and outdoor applications(garden irrigation by soil moisture and DHT11, air quality, smart parking ). Our free mobile application UR Smarthome, which is a local server that controls and monitors the building from a distance, is used to plan and execute all of these services that provides remote control and monitor of the building via Bluetooth/ Cellular networks and Wi-Fi access. With its customizable features, this IoT/M2M smart building system can be tailored to the demands of its users, enhancing their quality of life and safety while using less energy. In addition, identifying and lowering risks, it assists in preventing the loss of resources and human lives. KEYWORDS— Converged networks, heterogeneous networks (HetNets), Internet of Things (IoT)/machine-to-machine (M2M), mobile application, mobile cellular networks (MCNs), smart building, wireless sensor networks (WSNs)