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IoT-based air pollution monitoring system for smart villages
Högskolan Kristianstad, Fakulteten för naturvetenskap.
Högskolan Kristianstad, Fakulteten för naturvetenskap.
2021 (engelsk)Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
Abstract [en]

Air pollution is a global issue which has negative impacts not only on the environment but also on human health. Therefore, it is important to design and implement systems to allow cities and villages to monitor air quality so that they take the required actions to maintain a good air quality in the city/village. Since IoT facilitates implementing efficient monitoring systems, many IoT systems have been proposed to monitor air pollution. In this paper, we review different IoT-based systems to monitor air quality. In addition, we do an experiment where we propose and evaluate our system to monitor air pollution in a smart village, Veberöd, utilizing the LoRaWAN and the IoT platform, Yggio, which is already used in the village. Our proposed system is used to monitor temperature, humidity, pressure, PM1, PM2.5, PM10, CO2, and CO. As a result of our experiment, we found that the data received by Yggio was encoded, and Yggio did not provide the decoding functionality to decode the data sent from our devices. Therefore, another IoT platforms were used to decode, visualize, and analyse the data. The results of the experiments shows that as far as PM1, PM2.5, PM10, and CO are concerned, the air quality in the village is good. The results also showed that some LoRaWAN messages were lost and never received on Yggio.

sted, utgiver, år, opplag, sider
2021. , s. 52
Emneord [en]
Air Pollution, LoRaWAN, IoT, Smart Villages, Monitoring
HSV kategori
Identifikatorer
URN: urn:nbn:se:hkr:diva-21952OAI: oai:DiVA.org:hkr-21952DiVA, id: diva2:1561000
Eksternt samarbeid
Smarta Byar
Utdanningsprogram
Bachelor programme in Computer Science and Engineering
Veileder
Examiner
Tilgjengelig fra: 2021-06-08 Laget: 2021-06-05 Sist oppdatert: 2021-06-08bibliografisk kontrollert

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