Original Research

Power aware air quality sensing system with efficient data storage capability


Introduction: This paper presents a portable device of Air Quality Monitoring System (AQMS) based on a sensor platform. The purpose of the study is to power awareness, warning indication, and minimal data storage capability.
Materials and methods: AQMS is developed by embedded design methodology. The software part is based on the C programming language. AQMS device is made up of “transmitter” and “receiver” sections through
the Zigbee wireless network. The objective is to collect concentrations of CO, NO2, CO2, humidity, and temperature to check air pollution for health awareness. A power-saving strategy is adopted in the "transmitter" of AQMS through a Demultiplexer circuit. To minimize the space complexity of storage and availability of long-term data, data encoding techniques are implemented.
Results: By implementing switching activity on the sensors in AQMS, the active mode of sensing operations are controlled and a power saving of 18.41% is achieved. Extending the duration of transmission operation
increases data storage in the “receiver” unit. Hence, two encoding techniques are developed where real-time data are encoded in binary form: 2-bit encoding and 3-bit encoding. According to the results, 2-bit encoding
saves 50% of storage space and 3-bit encoding saves 25%, compared to not utilizing any encoding strategy, sacrificing data accuracy by less than 5%.
Conclusion: AQMS design is created with the implementations of low power consumption and low storage. Additionally, an alarming condition is set in AQMS for indicating the level of pollutants in the air to determine the risk level of exposure which is dangerous for human health.

1. Sharma A, Mitra A, Sharma S, Roy
S. Estimation of air quality index from
seasonal trends using deep neural network. In
proceedings of the International Conference
on Artificial Neural Networks. 2018; pp.
2. Leung DY. Outdoor-indoor air
pollution in urban environment: Challenges
and opportunity. Frontiers in Environmental
Science. 2015 Jan 15;2:69. [CrossRef]
3. Chen M, Yang J, Hu L, Hossain MS,
Muhammad G. Urban healthcare big data
system based on crowd sourced and cloudbased air quality indicators. IEEE Commun.
Mag. 2018; 56:14-20. {CrossRef]
4. Marques G. Ambient assisted living
and internet of things. In harnessing the
internet of everything (IoE) for accelerated
innovation opportunities, IGI Global:
Hershey, PA, USA. 2019; pp. 100-115.
5. United States Environmental
Protection Agency (USEPA). Indoor air
facts no. 4 (revised) sick building syndrome.
Air and Radiation (6609J), Research and
Development (MD-56), Tech. Rep. 1991.
[Online]. Available: http://www.epa.gov/iaq/
6. Gungor VC, Hancke, GP. Industrial
wireless sensor networks: Challenges, design
principles, and technical approach. IEEE
Trans. Ind. Electron. 2009; 56(10):4258–
7. Postolache OA, Pereira JMD, Girao
PMBS. Smart sensors network for air
quality monitoring applications. IEEE Trans.
Instrum. Meas. 2009; 58(9):3253–3262.
8. Kumar A, Singh IP, Sud SK. Energy
efficient and low cost indoor environment
monitoring system based on the IEEE 1451
standard. IEEE Sensors Journal. 2011;
9. Rodriguez-Sanchez MC, Borromeo
S, Hernandez-Tamames JA. Wireless sensor
networks for conservation and monitoring
cultural assets. IEEE Sensors Journal. 2011;
10. Kularatna N, Sudantha BH. An
environmental air pollution monitoring
system based on the IEEE 1451 Standard for
low cost requirements. IEEE Sensors Journal.
2008; 8(4):415-422.
11. Kumar A, Hancke GP. Energy efficient
environment monitoring system based on
the IEEE 802.15.4 Standard for low cost
requirements. IEEE Sensors Journal. 2014;
12. Oh SJ, Chung WY. Room environment
monitoring system from PDA terminal. In
Proc. International Symposium Intelligent
Signal Processing and Communication
Systems (IEEE-ISPACS 2004). 2004; pp.497-
13. Yan R, Sun H, Qian Y. ‘Energy-aware
sensor node design with its application in
wireless sensor networks’, IEEE Trans.
Instrum. Meas., Vol. 62, No. 5, pp.1183–
14. Yan R, Ball D, Deshmukh A, Gao RX.
A Bayesian network approach to energyaware distributed sensing. In Proc. IEEE
Sensors, Vienna, Austria. 2004; pp.44-47.
15. Nature. Big data: Science in the
petabyte era: community cleverness required.
International Journal of science. 2008;
16. Tsuchiya S, Sakamoto Y, Tsuchimoto
Y, Lee V. Big data processing in cloud
environments. Journal of FUJITSU Science
and Technology. 2012; 48(2):159-168.
17. Aich A, Krishna A, Akhilesh V, Hegde
C. Encoding web-based data for efficient
storage in machine learning applications.
In 2019 IEEE International conference on
Information processing, IEEE. 2019.
18. Ljungquist B, Petersson P, Johansson
AJ, Schouenborg J, Garwicz M. A bitencoding based new data structure for time
and memory efficient handling of spike
times in an electrophysiological setup.
Neuroinformatics. 2018; 16(2):217-229.
19. Jalil ME. Positioning and location
tracking using wireless sensor network.
Universiti Teknologi Malaysia. 2011.
20. PointSix. WiFi 2000 ppm CO2
Temperature Transmitter 3008-40-V6.
Point Six Wireless. Data Sheet. http://www.
21. Folea SC, Mois G. A low-power
wireless sensor for online ambient monitoring.
IEEE Sensors Journal. 2015;15:742-749.
22. Winsen. Zhengzhou Winsen
Electronics Technology Co., LTD. 2014.
[Online]. Available: https://www.winsensensor.com/d/files/MH-Z14.pdf
23. Yang H, Qin Y, Feng G, Ci H. Online
monitoring of geological CO2
storage and
leakage based on wireless sensor network.
IEEE Sensors Journal. 2013; 13(2):556–562.
24. Sgxsensortech. (2014). [Online].
Available: https://www.sgxsensortech.com/
25. Kim Y, Evans RG, Iversen WM. Remote
sensing and control of an irrigation system
using a distributed wireless sensor network.
IEEE Transactions on Instrumentation and
Measurement. 2008; 57(7):1379-1387.
26. Dhanalakshmi S, Poongothai M,
Sharma K. IoT based indoor air quality
and smart energy management for HVAC
system. In Proc. International Conference on
Computing and Network Communications
(CoCoNet’19). Procedia Computer Science,
Elsevier. 2020; 171:1800-1809.
27. Hilmani A, Maizate Abderrahin, Hassouni L. Designing and managing a
smart parking system using wireless sensor
networks. Journal of Sensor and Actuator
Networks. 2018; 7. https://doi.org/10.3390/
28. AC (Atmel Corporation). 8-Bit
AVR Microcontroller with 4/8/16/32K
bytes in-system programmable flash.
2017. https://www.atmel.com/pt/br/
devices/ATMEGA328P.aspx. Accessed
09 Feb 2017. https://doi.org/10.1109/
29. Jelicic V, Magno M, Brunelli D, Paci
G, Benini L. Context adaptive multimodal
wireless sensor network for energy-efficient
gas monitoring. IEEE Sensors Journal. 2013;
30. IEEE Standard for Information
Technology- telecommunications and
Information Exchange Between SystemsLocal and Metropolitan Area Networks.
IEEE Standard 802.15.4-2003. 2003.
31. Akyildiz IF, Vuran MC. Factors
influencing WSN design. Wireless Sensor
Networks, 1st ed. New York, NY, USA:
Wiley. 2010; pp. 37-51.
32. Digital International Inc., Minnetonka,
MN, USA. 2015. XBee-PRO RF Module
[Online]. Available: http://store.express-inc.
IssueVol 8 No 1 (2023): Winter 2023 QRcode
SectionOriginal Research
DOI https://doi.org/10.18502/japh.v8i1.12028
Air pollution; Sensor; Wireless network; Low power; Data encoding

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Datta Purkayastha K, Nath C, Pradhan SN. Power aware air quality sensing system with efficient data storage capability. JAPH. 2023;8(1):23-42.