Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair)

Dhingra, Swati; Madda, Rajasekhara Babu; Gandomi, Amir H.; Patan, Rizwan; Daneshmand, Mahmoud

doi:10.1109/jiot.2019.2903821

Cited by 260 publications

(107 citation statements)

References 17 publications

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“…e authors declare that there are no conflicts of interest regarding the publication of this paper. The transmission rate of the secondary system (bit/s/Hz) 5 1 0 1 5 2 0 2 5 3 0 0…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…With the rapid development of wireless communication and networking technologies, an increasing number of devices need to be connected globally and communicate automatically. erefore, the emerging of the Internet of ings (IoT) as a promising paradigm can achieve a fusing of the various technologies in 5G communication systems, which have been widely applied in smart cities, agriculture, and environment monitoring [1][2][3][4][5][6]. Moreover, the medical care and health care are becoming one of the most popular applications based on the IoT [7,8], named the Internet of Medical ings (IoMT), which can collect the data from the medical devices and applications to improve the treatment effect, disease diagnosis, and patient experience, while reducing misdiagnosis rate and treatment cost.…”

Section: Introductionmentioning

confidence: 99%

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Secure Information Transmissions in Wireless-Powered Cognitive Radio Networks for Internet of Medical Things

Tang

Luo

et al. 2020

Security and Communication Networks

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In this paper, we consider the issue of the secure transmissions for the cognitive radio-based Internet of Medical ings (IoMT) with wireless energy harvesting. In these systems, a primary transmitter (PT) will transmit its sensitive medical information to a primary receiver (PR) by a multi-antenna-based secondary transmitter (ST), where we consider that a potential eavesdropper may listen to the PT's sensitive information. Meanwhile, the ST also transmits its own information concurrently by utilizing spectrum sharing. We aim to propose a novel scheme for jointly designing the optimal parameters, i.e., energy harvesting (EH) time ratio and secure beamforming vectors, for maximizing the primary secrecy transmission rate while guaranteeing secondary transmission requirement. For solving the nonconvex optimization problem, we transfer the problem into convex optimization form by adopting the semidefinite relaxation (SDR) method and Charnes-Cooper transformation technique. en, the optimal secure beamforming vectors and energy harvesting duration can be obtained easily by utilizing the CVX tools. According to the simulation results of secrecy transmission rate, i.e., secrecy capacity, we can observe that the proposed protocol for the considered system model can effectively promote the primary secrecy transmission rate when compared with traditional zero-forcing (ZF) scheme, while ensuring the transmission rate of the secondary system.

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“…e authors declare that there are no conflicts of interest regarding the publication of this paper. The transmission rate of the secondary system (bit/s/Hz) 5 1 0 1 5 2 0 2 5 3 0 0…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Secure Information Transmissions in Wireless-Powered Cognitive Radio Networks for Internet of Medical Things

Tang

Luo

et al. 2020

Security and Communication Networks

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“…However, an emergency according to [16] is an ON/OFF variable and all detected emergencies are the same, regardless of where or when they happen. Nevertheless, an emergency can also be alerted providing additional information, as in [17]. In that work, sensors are used to alert people when the pollution level is above a defined threshold, but the level of pollution is also made clear.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Multi-Tier Emergency Alerting System Exploiting Sensors-Based Event Detection to Support Smart City Applications

Costa

Vasques

Portugal

et al. 2019

Sensors

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The development of efficient sensing technologies and the maturation of the Internet of Things (IoT) paradigm and related protocols have considerably fostered the expansion of sensor-based monitoring applications. A great number of those applications has been developed to monitor a set of information for better perception of the environment, with some of them being dedicated to identifying emergency situations. Current IoT-based emergency systems have limitations when considering the broader scope of smart cities, exploiting one or just a few monitoring variables or even allocating high computational burden to regular sensor nodes. In this context, we propose a distributed multi-tier emergency alerting system built around a number of sensor-based event detection units, providing real-time georeferenced information about the occurrence of critical events, while taking as input a configurable number of different scalar sensors and GPS data. The proposed system could then be used to detect and to deliver emergency alarms, which are computed based on the detected events, the previously known risk level of the affected areas and temporal information. Doing so, modularized and flexible perceptions of critical events are provided, according to the particularities of each considered smart city scenario. Besides implementing the proposed system in open-source electronic platforms, we also created a real-time visualization application to dynamically display emergency alarms on a map, demonstrating a feasible and useful application of the system as a supporting service. Therefore, this innovative approach and its corresponding physical implementation can bring valuable results for smart cities, potentially supporting the development of adaptive IoT-based emergency-aware applications.

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“…Recent advances in sensor technology facilitates personal exposure monitoring in a sustainable manner [21], [22]. Mobile applications in android also do the personal air pollution exposure in a stratified manner [23], [24].…”

Section: A Air Pollution Exposure Monitoringmentioning

confidence: 99%

Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

2019

IJITEE

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Pollution exposure and human health in the industry contaminated area are always a concern. The need for industrialization urges to concentrate on sustainable life of residents in the vicinity of the industrial area rather than opposing the industrialists. Literature in epidemiological studies reveal that air pollution is one of the major problems for health risks faced by residents in the industrial area. Main pollutants in industry related air pollution are particulate matter (PM2.5, PM10), SO2 , NO2 , and other pollutants upon the industry. Data for epidemiological studies obtained from different sources which are limited to public access include residents’ sociodemographic characters, health problems, and air quality index for personal exposure to pollutants. This combined data and limited resources make the analysis more complex so that statistical methods cannot compensate. Our review finds that there is an increase in literature that evaluates the connection between ambient air pollution exposure and associated health events of residents in the industrially polluted area using statistical methods, mainly regression models. A very few applies machine learning techniques to figure out the impact of common air pollution exposure on human health. Most of the machine learning approach to epidemiological studies end up in air pollution exposure monitoring, not to correlate its association with diseases. A machine learning approach to epidemiological studies can automatically characterize the residents’ exposure to pollutants and its associated health effects. Uniqueness of the model depends on the appropriate exhaustive data that characterizes the features, and machine learning algorithm used to build the model. In this contribution, we discuss various existing approaches that evaluate residents’ health effects and the source of irritation in association with air pollution exposure, focuses machine learning techniques and mathematical background for epidemiological studies for residents’ sustainable life.

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Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair)

Cited by 260 publications

References 17 publications

Secure Information Transmissions in Wireless-Powered Cognitive Radio Networks for Internet of Medical Things

Secure Information Transmissions in Wireless-Powered Cognitive Radio Networks for Internet of Medical Things

A Distributed Multi-Tier Emergency Alerting System Exploiting Sensors-Based Event Detection to Support Smart City Applications

Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

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