Air Pollution Detection Based on Head Selection Clustering and Average Method from Wireless Sensor Network

Goel, Amnesh; Ray, Sukanya; Agrawal, Prateek; Chandra, Nidhi

doi:10.1109/acct.2012.18

Cited by 12 publications

(9 citation statements)

References 4 publications

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“…The pollution detection process requires the use of a sensing unit at its core. The concerned sensing unit is designed and programmed to sense air pollutants [1]. The presence of air pollutants can be determined by monitoring parameters such as light level, temperature, humidity and pressure.…”

Section: A Pollution Detection -Proposed Methodsmentioning

confidence: 99%

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Analysis of Air Quality Index obtained via Remote Sensing for Lagos State Urban Area, Nigeria

Akinyemi¹,

Periola²,

Shoewu³

et al. 2018

ETRJ

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Advancements in the field of communications are moving towards the internet of things (IoTs). Wireless Sensor Networks (WSN) can be used to monitor environmental conditions such as pollution using independent sensors communicating with each other using the IoTs technology thereby leading to the design of an accurate environmental monitoring system. Air pollution is a critical environmental challenge which is a major concern in cities and will have to be reduced if cities are to be regarded as smart cities. It has negative short and long term effects on human health that would deter people from moving to cities thereby impairing economic growth. It is important to reduce the effect of air pollution. This can be achieved by using WSN nodes to monitor air pollution. WSN nodes deployed around the city and along traffic routes can be used to achieve this objective. This paper proposes the design of WSN network for monitoring air pollution. In this paper, the stationary sensor nodes are deployed around the city to acquire pollution data. The data acquired by stationary sensor nodes is transmitted to mobile nodes located on vehicles. The received data is processed when mobile nodes reach a pollution monitoring system. These data would be stored locally in its memory with a time and location stamp. Mobile nodes placed on public buses, buildings and vehicles would wirelessly collect the data as they pass by the stationary nodes. The data would then be processed once the nodes arrived back at the pollution monitoring system. In the proposed architecture, the stationary nodes run on ZigBee standard while the mobile nodes run on fifth generation (5G) and Long-time evolution (LTE) technology.

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Section: A Pollution Detection -Proposed Methodsmentioning

confidence: 99%

“…Wireless sensor networks (WSNs) comprise distributed autonomous sensors deployed to monitor environmental conditions such as pollution [1]. WSNs are significantly deployed due to the benefits of low costs [2], energy efficiency, and ease of scalable deployment.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Air Quality Index obtained via Remote Sensing for Lagos State Urban Area, Nigeria

Akinyemi¹,

Periola²,

Shoewu³

et al. 2018

ETRJ

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“…Secondly intermediate node (extra overhead off or warding information) to coordinator must be avoided. Intermediate nodes loss up the connection then all routes via that nodes also crashes in normal network [7]and in that scenario network badly suffers, so such problem didn't arises in said scenario there is a direct link between sensor node and cluster head [8]. Finally, when Public transport vehicles reaches back to destination, we collected the data collected by routing nodes deployed on metro bus service during the whole pathway of.…”

Section: Proposed Methods Of Air Pollution Monitoring For Both Indoor mentioning

confidence: 99%

Smart Environment Monitoring System by Employing Wireless Sensor Networks on Vehicles for Pollution Free Smart Cities

Jamil

Mazhar

et al. 2015

Procedia Engineering

111

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With the progression of advancements in technology, several innovations has been made in the field of communications that are transiting to Internet of Things. In this domain, Wireless Sensor Networks (WSN) are one of those independent sensing devices to monitor physical and environmental conditions along with thousands of applications in other fields. As air pollution being a major environmental change that causes many hazardous effects on human beings that need to be controlled. Hence, we deployed WSN nodes for constant monitoring of the air pollution around the city and the moving public transport buses and cars. This methodology gave us the monitoring data from the stationary nodes deployed in the city to the mobile nodes on Public Transport buses and cars. The data of the air pollution particles such as gases, smoke and other pollutants is collected via sensors on the Public transport buses and the data is being analyzed when the buses and cars reach back to the source destination after passing through the stationary nodes around the city. Our proposed architecture having innovative mesh network will be more efficient way of gathering data from the nodes of WSN. It will have lots of benefits with respect to the future concept of Smart Cities that will have the new technologies related to Internet of Things.

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“…It is a product of rapid and heavy industrialization which produces dangerous pollutants detrimental to health and to the environment as well. Among the different types of pollutants, the major ones are: carbon dioxide (CO 2 ), sulfur oxides (SOx), volatile organic compounds (VOC) and particulates such as coarse particles, PM 10 which have diameter between 2.5 to 10 micrometers, PM 2.5 which have diameter less than 2.5 micrometers, ultrafine particles and soot [1]. These pollutants can create cardiovascular, respiratory problems as well as psychological problems such as cognitive impairment ([2], [3], [4]).…”

Section: Introductionmentioning

confidence: 99%

Spatio- Temporal Analysis of Large Air Pollution Data

Tarek

Asaduzzaman

Patwary

2018

2018 10th International Conference on Electrical and Computer Engineering (ICECE)

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Air pollution is one of the most dangerous environmental threats in our planet. Although it is severe in highly populated and industrialized cities of the developing countries, it is also a major concern for developed countries. In developed world, the data are gathered from a large number of air pollution monitoring stations. Therefore, the volume of data are very high and it is not possible to analyze the data efficiently in realtime using the conventional methods. Large scale data mining techniques can help in analyzing those data more efficiently and dynamically. In our paper, we propose a method to mine large amount of air pollution data in order to find air pollution hot spots and time of pollution using clustering methods and timeseries analysis and applied to the air pollution data of PM2.5, PM10 and Ozone in the United Kingdom from 2015-17. The method is able to detect specific pollution zones of those pollutants in the UK. Furthermore, the pollution due to particulate matters was observed to be higher in winter season whereas Ozone pollution was seen to be downwards trending except some areas.

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Air Pollution Detection Based on Head Selection Clustering and Average Method from Wireless Sensor Network

Cited by 12 publications

References 4 publications

Analysis of Air Quality Index obtained via Remote Sensing for Lagos State Urban Area, Nigeria

Analysis of Air Quality Index obtained via Remote Sensing for Lagos State Urban Area, Nigeria

Smart Environment Monitoring System by Employing Wireless Sensor Networks on Vehicles for Pollution Free Smart Cities

Spatio- Temporal Analysis of Large Air Pollution Data

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