721 research outputs found
Internet of Things Architectures for Enhanced Living Environments
Ambient Assisted Living (AAL) is an emerging multidisciplinary research area that aims to create
an ecosystem of different types of sensors, computers, mobile devices, wireless networks, and
software applications for enhanced living environments and occupational health. There are
several challenges in the development and implementation of an effective AAL system, such as
system architecture, human-computer interaction, ergonomics, usability, and accessibility.
There are also social and ethical challenges, such as acceptance by seniors and the privacy and
confidentiality that must be a requirement of AAL devices. It is also essential to ensure that
technology does not replace human care and is used as a relevant complement.
The Internet of Things (IoT) is a paradigm where objects are connected to the Internet and
support sensing capabilities. IoT devices should be ubiquitous, recognize the context, and
support intelligence capabilities closely related to AAL. Technological advances allow defining
new advanced tools and platforms for real-time health monitoring and decision making in the
treatment of various diseases. IoT is a suitable approach to building healthcare systems, and it
provides a suitable platform for ubiquitous health services, using, for example, portable sensors
to carry data to servers and smartphones for communication. Despite the potential of the IoT
paradigm and technologies for healthcare systems, several challenges to be overcome still
exist. The direction and impact of IoT in the economy are not clearly defined, and there are
barriers to the immediate and ubiquitous adoption of IoT products, services, and solutions.
Several sources of pollutants have a high impact on indoor living environments. Consequently,
indoor air quality is recognized as a fundamental variable to be controlled for enhanced health
and well-being. It is critical to note that typically most people occupy more than 90% of their
time inside buildings, and poor indoor air quality negatively affects performance and
productivity.
Research initiatives are required to address air quality issues to adopt legislation and real-time
inspection mechanisms to improve public health, not only to monitor public places, schools,
and hospitals but also to increase the rigor of building rules. Therefore, it is necessary to use
real-time monitoring systems for correct analysis of indoor air quality to ensure a healthy
environment in at least public spaces. In most cases, simple interventions provided by
homeowners can produce substantial positive impacts on indoor air quality, such as avoiding
indoor smoking and the correct use of natural ventilation.
An indoor air quality monitoring system helps the detection and improvement of air quality
conditions. Local and distributed assessment of chemical concentrations is significant for safety (e.g., detection of gas leaks and monitoring of pollutants) as well as to control heating,
ventilation, and HVAC systems to improve energy efficiency. Real-time indoor air quality
monitoring provides reliable data for the correct control of building automation systems and
should be assumed as a decision support platform on planning interventions for enhanced living
environments. However, the monitoring systems currently available are expensive and only
allow the collection of random samples that are not provided with time information. Most
solutions on the market only allow data consulting limited to device memory and require
procedures for downloading and manipulating data with specific software. In this way, the
development of innovative environmental monitoring systems based on ubiquitous technologies
that allow real-time analysis becomes essential.
This thesis resulted in the design and development of IoT architectures using modular and
scalable structures for air quality monitoring based on data collected from cost-effective
sensors for enhanced living environments. The proposed architectures address several
concepts, including acquisition, processing, storage, analysis, and visualization of data. These
systems incorporate an alert management Framework that notifies the user in real-time in poor
indoor air quality scenarios. The software Framework supports multiple alert methods, such as
push notifications, SMS, and e-mail. The real-time notification system offers several advantages
when the goal is to achieve effective changes for enhanced living environments. On the one
hand, notification messages promote behavioral changes. These alerts allow the building
manager to identify air quality problems and plan interventions to avoid unhealthy air quality
scenarios. The proposed architectures incorporate mobile computing technologies such as
mobile applications that provide ubiquitous air quality data consulting methods s. Also, the
data is stored and can be shared with medical teams to support the diagnosis.
The state-of-the-art analysis has resulted in a review article on technologies, applications,
challenges, opportunities, open-source IoT platforms, and operating systems. This review was
significant to define the IoT-based Framework for indoor air quality supervision. The research
leads to the development and design of cost-effective solutions based on open-source
technologies that support Wi-Fi communication and incorporate several advantages such as
modularity, scalability, and easy installation. The results obtained are auspicious, representing
a significant contribution to enhanced living environments and occupational health.
Particulate matter (PM) is a complex mixture of solid and liquid particles of organic and
inorganic substances suspended in the air. Moreover, it is considered the pollutant that affects
more people. The most damaging particles to health are ≤PM10 (diameter 10 microns or less),
which can penetrate and lodge deep within the lungs, contributing to the risk of developing
cardiovascular and respiratory diseases as well as lung cancer. Taking into account the adverse
health effects of PM exposure, an IoT architecture for automatic PM monitoring was proposed.
The proposed architecture is a PM real-time monitoring system and a decision-making tool. The
solution consists of a hardware prototype for data acquisition and a Web Framework developed in .NET for data consulting. This system is based on open-source and technologies, with several
advantages compared to existing systems, such as modularity, scalability, low-cost and easy
installation. The data is stored in a database developed in SQL SERVER using .NET Web services.
The results show the ability of the system to analyze the indoor air quality in real-time and the
potential of the Web Framework for the planning of interventions to ensure safe, healthy, and
comfortable conditions.
Associations of high concentrations of carbon dioxide (CO2) with low productivity at work and
increased health problems are well documented. There is also a clear correlation between high
levels of CO2 and high concentrations of pollutants in indoor air. There are sufficient reasons
to monitor CO2 and provide real-time notifications to improve occupational health and provide
a safe and healthy indoor living environment. Taking into account the significant influence of
CO2 for enhanced living environments, a real-time IoT architecture for CO2 monitoring was
proposed. CO2 was selected because it is easy to measure and is produced in quantity (by people
and combustion equipment). It can be used as an indicator of other pollutants and, therefore,
of air quality in general. The solution consists of a hardware prototype for data acquisition
environment, a Web software, and a smartphone application for data consulting. The proposed
architecture is based on open-source technologies, and the data is stored in a SQL SERVER
database. The mobile Framework allows the user not only to consult the latest data collected
but also to receive real-time notifications in poor indoor air quality scenarios, and to configure
the alerts threshold levels. The results show that the mobile application not only provides easy
access to real-time air quality data, but also allows the user to maintain parameter history and
provide a history of changes. Consequently, this system allows the user to analyze in a precise
and detailed manner the behavior of air quality.
Finally, an air quality monitoring solution was implemented, consisting of a hardware prototype
that incorporates only the MICS-6814 sensor as the detection unit. This system monitors various
air quality parameters such as NH3 (ammonia), CO (carbon monoxide), NO2 (nitrogen dioxide),
C3H8 (propane), C4H10 (butane), CH4 (methane), H2 (hydrogen) and C2H5OH (ethanol). The
monitoring of the concentrations of these pollutants is essential to provide enhanced living
environments. This solution is based on Cloud, and the collected data is sent to the ThingSpeak
platform. The proposed Framework combines sensitivity, flexibility, and measurement
accuracy in real-time, allowing a significant evolution of current air quality controls. The results
show that this system provides easy, intuitive, and fast access to air quality data as well as
relevant notifications in poor air quality situations to provide real-time intervention and
improve occupational health. These data can be accessed by physicians to support diagnoses
and correlate the symptoms and health problems of patients with the environment in which
they live. As future work, the results reported in this thesis can be considered as a starting point for the
development of a secure system sharing data with health professionals in order to serve as
decision support in diagnosis.Ambient Assisted Living (AAL) é uma área de investigação multidisciplinar emergente que visa
a construção de um ecossistema de diferentes tipos de sensores, microcontroladores,
dispositivos móveis, redes sem fios e aplicações de software para melhorar os ambientes de
vida e a saúde ocupacional. Existem muitos desafios no desenvolvimento e na implementação
de um sistema AAL, como a arquitetura do sistema, interação humano-computador, ergonomia,
usabilidade e acessibilidade. Existem também problemas sociais e éticos, como a aceitação por
parte dos utilizadores mais vulneráveis e a privacidade e confidencialidade, que devem ser uma
exigência de todos os dispositivos AAL. De facto, também é essencial assegurar que a tecnologia
não substitua o cuidado humano e seja usada como um complemento essencial.
A Internet das Coisas (IoT) é um paradigma em que os objetos estão conectados à Internet e
suportam recursos sensoriais. Tendencialmente, os dispositivos IoT devem ser omnipresentes,
reconhecer o contexto e ativar os recursos de inteligência ambiente intimamente relacionados
ao AAL. Os avanços tecnológicos permitem definir novas ferramentas avançadas e plataformas
para monitorização de saúde em tempo real e tomada de decisão no tratamento de várias
doenças. A IoT é uma abordagem adequada para construir sistemas de saúde sendo que oferece
uma plataforma para serviços de saúde ubíquos, usando, por exemplo, sensores portáteis para
recolha e transmissão de dados e smartphones para comunicação. Apesar do potencial do
paradigma e tecnologias IoT para o desenvolvimento de sistemas de saúde, muitos desafios
continuam ainda por ser resolvidos. A direção e o impacto das soluções IoT na economia não
está claramente definido existindo, portanto, barreiras à adoção imediata de produtos, serviços
e soluções de IoT.
Os ambientes de vida são caracterizados por diversas fontes de poluentes. Consequentemente,
a qualidade do ar interior é reconhecida como uma variável fundamental a ser controlada de
forma a melhorar a saúde e o bem-estar. É importante referir que tipicamente a maioria das
pessoas ocupam mais de 90% do seu tempo no interior de edifícios e que a má qualidade do ar
interior afeta negativamente o desempenho e produtividade.
É necessário que as equipas de investigação continuem a abordar os problemas de qualidade do
ar visando a adoção de legislação e mecanismos de inspeção que atuem em tempo real para a
melhoraria da saúde e qualidade de vida, tanto em locais públicos como escolas e hospitais e
residências particulares de forma a aumentar o rigor das regras de construção de edifícios. Para
tal, é necessário utilizar mecanismos de monitorização em tempo real de forma a possibilitar
a análise correta da qualidade do ambiente interior para garantir ambientes de vida saudáveis.
Na maioria dos casos, intervenções simples que podem ser executadas pelos proprietários ou ocupantes da residência podem produzir impactos positivos substanciais na qualidade do ar
interior, como evitar fumar em ambientes fechados e o uso correto de ventilação natural.
Um sistema de monitorização e avaliação da qualidade do ar interior ajuda na deteção e na
melhoria das condições ambiente. A avaliação local e distribuída das concentrações químicas é
significativa para a segurança (por exemplo, deteção de fugas de gás e supervisão dos
poluentes) bem como para controlar o aquecimento, ventilação, e sistemas de ar condicionado
(HVAC) visando a melhoria da eficiência energética. A monitorização em tempo real da
qualidade do ar interior fornece dados fiáveis para o correto controlo de sistemas de automação
de edifícios e deve ser assumida com uma plataforma de apoio à decisão no que se refere ao
planeamento de intervenções para ambientes de vida melhorados. No entanto, os sistemas de
monitorização atualmente disponíveis são de alto custo e apenas permitem a recolha de
amostras aleatórias que não são providas de informação temporal. A maioria das soluções
disponíveis no mercado permite apenas a acesso ao histórico de dados que é limitado à memória
do dispositivo e exige procedimentos de download e manipulação de dados com software
proprietário. Desta forma, o desenvolvimento de sistemas inovadores de monitorização
ambiente baseados em tecnologias ubíquas e computação móvel que permitam a análise em
tempo real torna-se essencial.
A Tese resultou na definição e no desenvolvimento de arquiteturas para monitorização da
qualidade do ar baseadas em IoT. Os métodos propostos são de baixo custo e recorrem a
estruturas modulares e escaláveis para proporcionar ambientes de vida melhorados. As
arquiteturas propostas abordam vários conceitos, incluindo aquisição, processamento,
armazenamento, análise e visualização de dados. Os métodos propostos incorporam
Frameworks de gestão de alertas que notificam o utilizador em tempo real e de forma ubíqua
quando a qualidade do ar interior é deficiente. A estrutura de software suporta vários métodos
de notificação, como notificações remotas para smartphone, SMS (Short Message Service) e email.
O método usado para o envio de notificações em tempo real oferece várias vantagens
quando o objetivo é alcançar mudanças efetivas para ambientes de vida melhorados. Por um
lado, as mensagens de notificação promovem mudanças de comportamento. De facto, estes
alertas permitem que o gestor do edifício e os ocupantes reconheçam padrões da qualidade do
ar e permitem também um correto planeamento de intervenções de forma evitar situações em
que a qualidade do ar é deficiente. Por outro lado, o sistema proposto incorpora tecnologias
de computação móvel, como aplicações móveis, que fornecem acesso omnipresente aos dados
de qualidade do ar e, consequentemente, fornecem soluções completas para análise de dados.
Além disso, os dados são armazenados e podem ser partilhados com equipas médicas para
ajudar no diagnóstico.
A análise do estado da arte resultou na elaboração de um artigo de revisão sobre as tecnologias,
aplicações, desafios, plataformas e sistemas operativos que envolvem a criação de arquiteturas
IoT. Esta revisão foi um trabalho fundamental na definição das arquiteturas propostas baseado em IoT para a supervisão da qualidade do ar interior. Esta pesquisa conduz a um
desenvolvimento de arquiteturas IoT de baixo custo com base em tecnologias de código aberto
que operam como um sistema Wi-Fi e suportam várias vantagens, como modularidade,
escalabilidade e facilidade de instalação. Os resultados obtidos são muito promissores,
representando uma contribuição significativa para ambientes de vida melhorados e saúde
ocupacional.
O material particulado (PM) é uma mistura complexa de partículas sólidas e líquidas de
substâncias orgânicas e inorgânicas suspensas no ar e é considerado o poluente que afeta mais
pessoas. As partículas mais prejudiciais à saúde são as ≤PM10 (diâmetro de 10 micrómetros ou
menos), que podem penetrar e fixarem-se dentro dos pulmões, contribuindo para o risco de
desenvolver doenças cardiovasculares e respiratórias, bem como de cancro do pulmão. Tendo
em consideração os efeitos negativos para a saúde da exposição ao PM foi desenvolvido numa
primeira fase uma arquitetura IoT para monitorização automática dos níveis de PM. Esta
arquitetura é um sistema que permite monitorização de PM em tempo real e uma ferramenta
de apoio à tomada de decisão. A solução é composta por um protótipo de hardware para
aquisição de dados e um portal Web desenvolvido em .NET para consulta de dados. Este sistema
é baseado em tecnologias de código aberto com várias vantagens em comparação aos sistemas
existentes, como modularidade, escalabilidade, baixo custo e fácil instalação. Os dados são
armazenados numa base de dados desenvolvida em SQL SERVER e são enviados com recurso a
serviços Web. Os resultados mostram a capacidade do sistema de analisar em tempo real a
qualidade do ar interior e o potencial da Framework Web para o planeamento de intervenções
com o objetivo de garantir condições seguras, saudáveis e confortáveis.
Associações de altas concentrações de dióxido de carbono (CO2) com défice de produtividade
no trabalho e aumento de problemas de saúde encontram-se bem documentadas. Existe
também uma correlação evidente entre altos níveis de CO2 e altas concentrações de poluentes
no ar interior. Tendo em conta a influência significativa do CO2 para a construção de ambientes
de vida melhorados desenvolveu-se uma solução de monitorização em tempo real de CO2 com
base na arquitetura de IoT. A arquitetura proposta permite também o envio de notificações em
tempo real para melhorar a saúde ocupacional e proporcionar um ambiente de vida interior
seguro e saudável. O CO2 foi selecionado, pois é fácil de medir e é produzido em quantidade
(por pessoas e equipamentos de combustão). Assim, pode ser usado como um indicador de
outros poluentes e, portanto, da qualidade do ar em geral. O método proposto é composto por
um protótipo de hardware para aquisição de dados, um software Web e uma aplicação
smartphone para consulta de dados. Esta arquitetura é baseada em tecnologias de código
aberto e os dados recolhidos são armazenados numa base de dados SQL SERVER. A Framework
móvel permite não só consultar em tempo real os últimos dados recolhidos, receber
notificações com o objetivo de avisar o utilizador quando a qualidade do ar está deficiente,
mas também para configurar alertas. Os resultados mostram que a Framework móvel fornece não apenas acesso fácil aos dados da qualidade do ar em tempo real, mas também permite ao
utilizador manter o histórico de parâmetros. Assim este sistema permite ao utilizador analisar
de maneira precisa e detalhada o comportamento da qualidade do ar interior.
Por último, é proposta uma arquitetura para monitorização de vários parâmetros da qualidade
do ar, como NH3 (amoníaco), CO (monóxido de carbono), NO2 (dióxido de azoto), C3H8
(propano), C4H10 (butano), CH4 (metano), H2 (hidrogénio) e C2H5OH (etanol). Esta arquitetura é
composta por um protótipo de hardware que incorpora unicamente o sensor MICS-6814 como
unidade de deteção. O controlo das concentrações destes poluentes é extremamente relevante
para proporcionar ambientes de vida melhorados. Esta solução tem base na Cloud sendo que os
dados recolhidos são enviados para a plataforma ThingSpeak. Esta Framework combina
sensibilidade, flexibilidade e precisão de medição em tempo real, permitindo uma evolução
significativa dos atuais sistemas de monitorização da qualidade do ar. Os resultados mostram
que este sistema fornece acesso fácil, intuitivo e rápido aos dados de qualidade do ar bem
como notificações essenciais em situações de qualidade do ar deficiente de forma a planear
intervenções em tempo útil e melhorar a saúde ocupacional. Esses dados podem ser acedidos
pelos médicos para apoiar diagnósticos e correlacionar os sintomas e problemas de saúde dos
pacientes com o ambiente em que estes vivem.
Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto
de partida para o desenvolvimento de um sistema seguro para partilha de dados com
profissionais de saúde de forma a servir de suporte à decisão no diagnóstico
Min Eng
The Industrial Internet of Things (IIoT), a concept that combines sensor networks and control systems, has been employed in several industries to improve productivity and safety. U.S. National Institute for Occupational Safety and Health (NIOSH) researchers are investigating IIoT applications to identify the challenges of and potential solutions for transferring IIoT from other industries to the mining industry. Specifically, NIOSH has reviewed existing sensors and communications network systems used in U.S. underground coal mines to determine whether they are capable of supporting IIoT systems. The results show that about 40 percent of the installed post-accident communication systems as of 2014 require minimal or no modification to support IIoT applications. NIOSH researchers also developed an IIoT monitoring and control prototype system using low-cost microcontroller Wi-Fi boards to detect a door opening on a refuge alternative, activate fans located inside the Pittsburgh Experimental Mine and actuate an alarm beacon on the surface. The results of this feasibility study can be used to explore IIoT applications in underground coal mines based on existing communication and tracking infrastructure.CC999999/Intramural CDC HHS/United States2018-01-16T00:00:00Z29348699PMC5769960vault:2590
Development and Hardware Implementation of IoT-Based Patrol Robot for Remote Gas Leak Inspection
The Internet of Things Robot (IoTR) is an emerging paradigm that brings together robotic systems with the Internet of Things (IoT) that connect sensors and smart objects pervasively embedded in everyday environments. With the recent developments in robotic system applications, it becomes apparent that the mobile robot has great importance in real-world applications such as navigation and surveillance. One of the most important applications of a mobile robot is patrolling and gas leak detection. This paper proposes a real-time IoT Robot (IoTR) that can be used indoors or outdoors for gas leak detection purposes. The proposed mobile robot is equipped with microphones, speakers, the hub of smart sensors that are necessary for patrolling and gas leak detection, a high-resolution IP video camera for live video streaming, Bluetooth for indoor applications and tracking, and GPS/GPRS for outdoor applications and tracking. The experimental testing of the preliminary prototype confirms the design objectives. The robot has been tested for indoor and outdoor modes; the robot can detect gas leakage and provides a live video streaming of the surrounding area, which can be tracked on Google maps. At the same time, the robot can be controlled remotely through a mobile app or website, the robot can move autonomously and avoid obstacles. The proposed work provides a low-cost IoT robot through the use of the available and cheap components and sensors, which featured a high quality at the same time. Our proposed system exhibits promising gas sensing performance in harsh environments, using intelligent gas sensors that have a fast response (>10s), low cost, high sensitivity, long life, robustness, and physical size
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
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey
The Internet of Things (IoT) is a dynamic global information network
consisting of internet-connected objects, such as Radio-frequency
identification (RFIDs), sensors, actuators, as well as other instruments and
smart appliances that are becoming an integral component of the future
internet. Over the last decade, we have seen a large number of the IoT
solutions developed by start-ups, small and medium enterprises, large
corporations, academic research institutes (such as universities), and private
and public research organisations making their way into the market. In this
paper, we survey over one hundred IoT smart solutions in the marketplace and
examine them closely in order to identify the technologies used,
functionalities, and applications. More importantly, we identify the trends,
opportunities and open challenges in the industry-based the IoT solutions.
Based on the application domain, we classify and discuss these solutions under
five different categories: smart wearable, smart home, smart, city, smart
environment, and smart enterprise. This survey is intended to serve as a
guideline and conceptual framework for future research in the IoT and to
motivate and inspire further developments. It also provides a systematic
exploration of existing research and suggests a number of potentially
significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201
Developing a dynamic digital twin at a building level: Using Cambridge campus as case study
A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. A DT, if equipped with appropriate algorithms will represent and predict future condition and performance of their physical counterparts. Current developments related to DTs are still at an early stage with respect to buildings and other infrastructure assets. Most of these developments focus on the architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, where the value potential is immense. A systematic and clear architecture verified with practical use cases for constructing a DT is the foremost step for effective operation and maintenance of assets. This paper presents a system architecture for developing dynamic DTs in building levels for integrating heterogeneous data sources, support intelligent data query, and provide smarter decision-making processes. This will further bridge the gaps between human relationships with buildings/regions via a more intelligent, visual and sustainable channels. This architecture is brought to life through the development of a dynamic DT demonstrator of the West Cambridge site of the University of Cambridge. Specifically, this demonstrator integrates an as-is multi-layered IFC Building Information Model (BIM), building management system data, space management data, real-time Internet of Things (IoT)-based sensor data, asset registry data, and an asset tagging platform. The demonstrator also includes two applications: (1) improving asset maintenance and asset tracking using Augmented Reality (AR); and (2) equipment failure prediction. The long-term goals of this demonstrator are also discussed in this paper
Implementation of Data Abstraction Layer Using Kafka on SEMAR Platform for Air Quality Monitoring
Urbanization and fast-growing industries causing air quality in urban areas to be bad and even tend to be dangerous. In addition, the largest percentage of energy emissions come from the transportation sector, specifically on road transportation. Therefore, the need for a quality detection system that is capable of distributing and displaying large data information in real-time cannot be resolved by the system currently used by the government. This research offers a solution to the implementation of data abstraction in cloud computing which is built using the concept microservice architecture and integrated with mobile-based sensors to detect air quality in real-time. This solution consists of integrated cloud computing services using Smart Environment Monitoring and Analytical in Real-time (SEMAR) and Vehicles as Mobile Sensor Networks (VaaMSN) to detecting air quality. SEMAR was built with microservice references consist of data abstraction, communication, data analytical with business analytics proccess, data storage with Big data service and also real-time visualization in maps, chart, and table through dasboard website. Through the experiments that we did show that the microservice of data abstraction layer can be installed at the SEMAR stage indicating that the average delay in sending information is around 0.09 ms (90μs), this indicates that the system can be said to be real-time. With specific and real-time locations in data visualization, the government can use this method as an new alternative method of air quality
A Proposal for Architectural Framework Using Internet of Things with Fog Computing for an Air Quality Monitoring System
Air Monitoring becomes a systematic approach for sensitivity and finding out the cir-cumstances of the atmosphere. The major concern of air quality monitoring is to meas-ure the concentration of pollution and other important parameter related to the con-tamination and provides information in real-time to make decisions at right time to cure lives and save the environment. This paper proposes an Architectural Framework for the air quality monitoring system based on Internet-of-Things (IoT) and via Fog computing techniques with novel methods to obtain real-time and accurate measurements of con-ventional air quality monitoring. IoT-based real-time air pollution monitoring system is projected to at any location and stores the measured value of various pollutants over a web server with the Internet. It can facilitate the process and filter data near the end of the IoT nodes in a concurrent manner and improving the Latency issue with the quality of services
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
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