Development of an IoT-based atmospheric environment monitoring system

Kim, Seung Ho; Jeong, Jong Mun; Hwang, Min Tae; Kang, Chang-Ho

doi:10.1109/ictc.2017.8190799

Cited by 43 publications

(17 citation statements)

References 2 publications

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“…In [12], the authors propose an IoT-based atmospheric environment monitoring system which can effectively observe air pollution information without the restriction of place or space. The system consists of atmospheric environment measurement devices, an atmospheric environment analyzer, and a user application.…”

Section: Air Pollution Monitoring Solutionsmentioning

confidence: 99%

An Orthogonal Air Pollution Monitoring Method (OAPM) Based on LoRaWAN

Haiahem¹,

Minet

Boumerdassi

et al. 2020

JSAN

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High accuracy air pollution monitoring in a smart city requires the deployment of a huge number of sensors in this city. One of the most appropriate wireless technologies expected to support high density deployment is LoRaWAN which belongs to the Low Power Wide Area Network (LPWAN) family and offers long communication range, multi-year battery lifetime and low cost end devices. It has been designed for End Devices (EDs) and applications that need to send small amounts of data a few times per hour. However, a high number of end devices breaks the orthogonality of LoRaWAN transmissions, which was one of the main advantages of LoRaWAN. Hence, network performances are strongly impacted. To solve this problem, we propose a solution called OAPM (Orthogonal Air Pollution Monitoring) which ensures the orthogonality of LoRaWAN transmissions and provides accurate air pollution monitoring. In this paper, we show how to organize EDs into clusters and sub-clusters, assign transmission times to EDs, configurate and synchronize them, taking into account the specificities of LoRaWAN and the features of the air pollution monitoring application. Simulation results corroborate the very good behavior of OAPM.

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Section: Air Pollution Monitoring Solutionsmentioning

confidence: 99%

An Orthogonal Air Pollution Monitoring Method (OAPM) Based on LoRaWAN

Haiahem¹,

Minet

Boumerdassi

et al. 2020

JSAN

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“…Por otra parte, se ha diseñado una gran variedad de aplicaciones usando plataformas de IoT en áreas de monitorización de signos vitales [14][15][16][17], cuidado de la salud [18], procesos industriales [19], parámetros ambientales y atmosféricos [20], control de tráfico [21], puentes [22] y sistemas de prevención de inundaciones [23] en hogares y ciudades inteligentes, sistemas de alimentación de animales en granjas [24] y ubicación de infantes en el hogar [25], entre otras. La mayoría de estas aplicaciones usan transceptores wifi y pocas utilizan tecnología LoRa [26,27] o GPRS [28].…”

Section: Investigaciones Recientesunclassified

Sistema de monitorización de puertas y ventanas de un centro de datos con IoT

Luna

Sánchez-Rangel

Cosme-Aceves

2019

ings

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En este trabajo se presenta la construcción de un prototipo de un sistema de monitorización de puertas y ventanas a través de una plataforma IoT. El objetivo fue diseñar un sistema que reporte a un servidor en la nube el cambio de estado, abierta o cerrada, de tres puertas y dos ventanas de la sala de equipos del centro de datos. Los cambios de estado son registrados por el servidor y por medio de una interfaz de usuario se muestran en línea los estados de las puertas y ventanas. La arquitectura del sistema se basa en una red inalámbrica de sensores integrada por un nodo central y cinco nodos de monitorización. Los nodos de monitorización están compuestos por una tarjeta PyBoard, dos sensores digitales de efecto Hall y una interfaz inalámbrica wifi. Al detectar un cambio de estado en puertas y ventanas, los nodos de monitorización lo notifican al nodo central, y este lo transmite al servidor por medio de un punto de acceso wifi. Cuando una puerta o ventana permanece abierta más de un período de tiempo configurable, se envía un mensaje SMS y de WhatsApp a un teléfono móvil. El alcance logrado en la transmisión wifi en la red fue 47 metros con línea de vista.

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“…In clinical environments such as operating rooms (ORs) and intensive care units (ICUs), key events during patient monitoring include: (1) Patient movements while lying in bed and in mobility within the room [1,2]; (2) Bedside monitor alarm triggers and noise pollution [2][3][4][5][6]; (3) Presence, absence and movement of clinical personnel in the patient's vicinity [7][8][9]; and (4) Variations in the ambient light, temperature, and humidity [2,6,10]. In home environments, key events that are generally untracked but are beneficial for patient monitoring include: (1) Patient bodily movement during sleep [11,12]; (2) Patient movement around their residence [13]; (3) Doorbell triggers, smoke-detector triggers, microwave beeps, and phone rings [14]; and (4) Changes in the ambient light, temperature, and humidity [15]. Recently, non-contact sensors or nearables [15][16][17][18][19][20] such as microphones, video cameras, light-intensity sensors, temperature and humidity sensors, are becoming more popular for hassle-free patient monitoring.…”

Section: Introductionmentioning

confidence: 99%

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments

Suresha¹,

Hegde²,

Jiang³

et al. 2022

Preprint

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The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (a) Estimating occupancy and human activity phenotyping; (b) Medical equipment alarm classification; (c) Geolocation of humans in a built environment; (d) Ambient light logging; and (e) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

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Development of an IoT-based atmospheric environment monitoring system

Cited by 43 publications

References 2 publications

An Orthogonal Air Pollution Monitoring Method (OAPM) Based on LoRaWAN

An Orthogonal Air Pollution Monitoring Method (OAPM) Based on LoRaWAN

Sistema de monitorización de puertas y ventanas de un centro de datos con IoT

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments

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