A IoT-Enabled Multisensor Air Quality Monitoring with Edge-Based AQI Computation and Intelligent Mobile Analytics

References

[1] Balasubramaniyan C, Manivannan D. IoT enabled air quality monitoring system (AQMS) using Raspberry Pi. Indian J Sci Technol. 2016;9(39). https://doi.org/10.17485/ijst/2016/v9i39/90414

[2] Adeagbo AA. IoT based environment monitoring system using ESP32 [technical report]. Edwardsville (IL): Southern Illinois University Edwardsville; 2022.

[3] Omkar N, Rahul S, Vijaykrishna R, Swapna N, Sura MR. Smart environmental monitoring using ESP32 microcontroller. IOSR J Electron Commun Eng. 2024;19(3):7–12. https://doi.org/10.9790/2834-1903010712

[4] Uddin MS, Praharaj MK. IoT based weather monitoring system using ESP32 microcontroller. Int J Adv Res Sci Commun Technol. 2025;5(2):693–701. https://doi.org/10.48175/IJARSCT-29283

[5] Espressif Systems. ESP32 series datasheet, version 3.8. Shanghai: Espressif Systems; 2021.

[6] Aosong Electronics. Digital-output relative humidity & temperature sensor/module DHT22 (AM2302) [datasheet]. Guangzhou: Aosong Electronics.

[7] u-blox AG. NEO-6 u-blox 6 GPS modules data sheet (GPS.G6-HW-09005). Thalwil: u-blox AG; 2011.

[8] Google LLC. Gemini API reference: generating content [Internet]. Mountain View (CA): Google LLC. Available from: https://ai.google.dev/api/generate-content

[9] Google LLC. Dart programming language documentation [Internet]. Mountain View (CA): Google LLC. Available from: https://dart.dev/

[10] flutter_bluetooth_serial_plus [software package]. pub.dev. Available from: https://pub.dev/packages/flutter_bluetooth_serial_plus

[11] World Health Organization. WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Geneva: World Health Organization; 2021.

[12] ASHRAE. Thermal environmental conditions for human occupancy. ANSI/ASHRAE Standard 55-2017. Atlanta (GA): ASHRAE; 2017.

[13] Wakde B, Patil AC, Nithya Shree B, Nihar TN, Varun PS. AI-integrated smart environment monitoring system with real-time Dart dashboard: A unified IoT framework using ESP32, multisensory array, and intelligent alert mechanism. Int J Sci Res Eng Manag. 2026;10(4).

[14] Bakare MS, Abubaker K. IoT-based indoor environmental monitoring system using multi-parameter sensing and ESP32-WROOM integration. Discov Electron. 2026;3. https://doi.org/10.1007/s44291-026-00157-3

[15] Google LLC. TensorFlow Lite guide [Internet]. 2024. Available from: https://www.tensorflow.org/lite/

[16] Jo JH, Jo B, Kim JH, Kim S, Han W. Development of an IoT-based indoor air quality monitoring platform. J Sens. 2020;2020:8749764. https://doi.org/10.1155/2020/8749764

[17] de Medeiros HPL, Girão G. An IoT-based air quality monitoring platform. In: 2020 IEEE International Smart Cities Conference (ISC2); 2020. https://doi.org/10.1109/ISC251055.2020.9239070

[18] Jo JH, Jo B, Kim JH, Choi I. Implementation of IoT-based air quality monitoring system for investigating particulate matter (PM10) in subway tunnels. Int J Environ Res Public Health. 2020;17(15):5429. https://doi.org/10.3390/ijerph17155429

[19] Muhammad K, Ramadhan Putra RR, Purnomo E. Design and implementation of microcontroller-based air filter system with Android application interface. Emitor J Tek Elektro. 2026;26(1):71–81. https://doi.org/10.23917/emitor.v26i1.16143

@article{1724724,
      author = {Nihar T N, Amith C. Patil, B Nithya Shree, Varun P S, Bhagyashri Wakde},
      title = {A IoT-Enabled Multisensor Air Quality Monitoring with Edge-Based AQI Computation and Intelligent Mobile Analytics},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {927-937},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724724.pdf},
      abstract = {Inexpensive air quality monitors based on microcontroller units (MCUs) generally end at sensor data collection and numerical displays, with no further interpretation of data meaning and possible actions left to the user. The paper describes a multisensor air quality monitoring system combining an ESP32-WROOM MCU, DHT22 temperature humidity sensor, analog MQ-series gas sensor, infrared optical dust sensor and a NEO-6M GPS module together with a custom cross-platform Flutter application. Unlike cloud-based IoT systems, which depend on an active Wi-Fi/cloud connection, the sensor node communicates with the application over a dedicated Bluetooth Classic serial connection sending newline-delimited JSON payloads, which are processed by the application to display live data in a dashboard including an overall computed air quality index in addition to individual sensor values and GPS status. The application includes a feature to send an on-demand analysis of the current reading set to a cloud-based generative-AI model (Google Gemini) using a constrained prompt format and receive back a structured non-diagnostic report of environmental risks, potential causes, precaution and short-term forecast. The functional tests have confirmed the consistency of JSON framing for both USB-serial and Bluetooth-serial connections as well as correct response of the system's air quality classification algorithm from a low pollutant baseline reading to high particulates test scenario. Overall, a combination of Bluetooth serial link from a device to a phone and cloud-based generative-AI model analysis proves to be an inexpensive and feasible alternative to IoT cloud infrastructure for contextual air quality information delivery.},
      keywords = {Air Quality Monitoring; Bluetooth; ESP32; Flutter; Generative AI},
      month = {November},
  }