IOT Empowered Helmets: Pioneering Safety in the Mining Sector

Mining sector is the most prominent to accidents. The accident rate in mining is about 87% .Mining activity involves a lot of tedious and time-consuming tasks as well. Out of the various risks associated with mining there exists many different types of hazardous gases like carbon monoxide, hydrogen sulphide,methane and excess of carbon dioxide. Mining also includes the risk of tremors and landslides as well. Along with that there is the danger of falling of large and bulky objects as well. On top of that removal of helmet is even more dangerous. Moreover ,monitoring each and every miner is also an important factor when it comes to mining. As a result there are lot of risks associated with the same. At times these risks can be life-threatening and result into fatal error .Lifesaving helmet for miners is an IOT based complete protection of miners. As the traditional model of smart helmet doesn’t take all the above mentioned safety threats into consideration our aim through this proposed system is to develop a complete safety equipment in the form of helmet to safeguard miners from the various dangers present in the mining industry

An IoT Based Worker Safety Helmet Using Cloud Computing Technology

Miner safety and security is a major challenge around the world due to the exposure to toxic gases that are frequently released in underground mines. Miners' health is adversely affected primarily by toxic gases, which endanger the workers' lives. Furthermore, human sensory abilities do not detect these dangerous gases. As a result, this paper proposes a safety monitoring system that includes a temperature sensor, humidity sensor, and gas sensors to detect harmful gases and alert miners to those harmful gases using the smart helmet they wear. These gases are transmitted to the control station via the cloud using Internet of Things devices. The station monitors parameters like temperature, humidity, and toxic gases like methane and carbon monoxide to detect any abnormalities and alert the miner via a buzzer on the helmet. The data is processed by the Thing Speak cloud, which enables users to communicate via internet-connected devices and displays a field graph of the transmitted data

Industrial Internet of Things based Collaborative Sensing Intelligence: Framework and Research Challenges

The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a Collaborative Sensing Intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed as well

Smart Wearable Gadget for Miners Using IOT

Safety is the most important part of any kind of assiduity is safety. In extreme circumstances, safety-related negligence could result in the destruction of expensive clothing or the loss of human life. Every min-ing diligence adhere to a few basic preventative measures in order to avoid any generally unwelcome wonders. The most important component at this time is communication in order to continuously monitor various pa-rameters and take the appropriate actions as a result to avoid any risks linked with the product or the management of mortal funds. A stable and wide-range effective communication system between personnel in the mine and the control centre must be built in order to increase safety in un-derground mines. The cable communication network technology is inef-fective within underground mines. Here we can tackle the matter of acci-dents which end with death of several people per annum. It is discovered that the speed of fatality within the coal pit industry is almost six times the speed for all private industries. And most of those accidents are because of toxic gases, fires, and a lack of rescue systems. By implementing mine surveillance gadgets, which may be used within the mine and detect the number of various gases, fall, emergency detection and report to them. This article focuses on the design and analysis of the smart wearable gadget for miners in the mining industry using IoT

BcBIM: A Blockchain-Based Big Data Model for BIM Modification Audit and Provenance in Mobile Cloud

Building Information Modeling (BIM) is envisioned as an indispensable opportunity in the architecture, engineering, and construction (AEC) industries as a revolutionary technology and process. Smart construction relies on BIM for manipulating information flow, data flow, and management flow. Currently, BIM model has been explored mainly for information construction and utilization, but rare works pay efforts to information security, e.g., critical model audit and sensitive model exposure. Moreover, few BIM systems are proposed to chase after upcoming computing paradigms, such as mobile cloud computing, big data, blockchain, and Internet of Things. In this paper, we make the first attempt to propose a novel BIM system model called bcBIM to tackle information security in mobile cloud architectures. More specifically, bcBIM is proposed to facilitate BIM data audit for historical modifications by blockchain in mobile cloud with big data sharing. The proposed bcBIM model can guide the architecture design for further BIM information management system, especially for integrating BIM cloud as a service for further big data sharing. We propose a method of BIM data organization based on blockchains and discuss it based on private and public blockchain. It guarantees to trace, authenticate, and prevent tampering with BIM historical data. At the same time, it can generate a unified format to support future open sharing, data audit, and data provenance

Integrating IoT technologies for an "intelligent" safety management in the process industry

Abstract IoT (Internet of Things) technologies are wide spreading in several industrial sector due to a combination of increasing technical performance together with decreasing purchase prices: thus, new tools are been evaluated for adoption in new fields of application, like safety at work. In recent years, several projects and prototypes as well as industrial solutions have been developed using IOT technologies especially to dynamically managing safety levels at complex workplaces. The aim of this study is to describe a prototype system where the so called Smart Objects (SOs) - integrating different IoT technologies- interact in a working environment through a digital platform for managing different type of hazards – e.g. involving safety of plants as well as workers - usually influencing safety levels especially in process industry. The fields of application of the proposed system vary from tracking periodic mandatory maintenance and analyzing aging of equipment, processing or containing hazardous materials, to remote tracking of hazardous conditions of workers

Optimization of the Health and Safety Protocol for Covid-19 through the use of the QR Code in the Tailing Disposal System Project, Quellaveco - Moquegua, 2020

The purpose of this research work is to optimize the occupational health and safety protocol by increasing distancing and mitigating physical contact to prevent the spread of Covid-19, using QR catches to identify personnel. Contrasting the sanitary measures to protect the health and integrity of the personnel of the operations area of the Tailing Disposal System -Quellaveco project. For the study of the database, factor analysis is used as a multivalent statistical method, with this method the number of 997 daily reports was analyzed, addressing the study of the structure of correlations between a large number of variables, factor analysis assists in the selection of a representative subgroup of variables and creates new variables called factors, when performing the analysis and treatment of 24 variables with the SPSS 25 software, the variables with the correlation matrix were reduced to 13, and with the total variance explained, a matrix different from that of identity was defined, reducing the dimensionality of the problem from 13 to 5 factors

Missouri S&T Magazine Summer 2019

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