Mobile Crowd Sensing Application for Noise Monitoring in Kuala Lumpur

  • Authors

    • Rashid Zafar
    • Megat F. Zuhairi
    • Eiad Yafi
    • Hassan Dao
    • Hilmi M. Salleh
    2018-11-26
    https://doi.org/10.14419/ijet.v7i4.29.21969
  • Mobile Crowd Sensing, Noise pollution, Kuala Lumpur, Sensors
  • Mobile Crowd Sensing (MCS) technology enables mobile devices, such as smart-phones or other android-based devices that are equipped with embedded sensors to gather relevant data for research work. Typically, the MCS application field ranges from online social-media monitoring, transportation system monitoring, atmosphere monitoring and etc. The inherent attributes of MCS applications is the ability of the system to monitor and collect data over a huge geographical area. Generally, the planners of MCS select participants based upon the scope of survey or the type of data to be acquired. Consequently, based on user’s movement behaviour and location, the MCS application running on the background is able to discreetly collects data from the proximate areas. In principle, this research work highlights the development of MCS application, using the noise parameter as input. The research work shows the feasibility of MCS for data gathering. However, it is essential that data obtained from smartphone’s sensor i.e. microphone is properly processed. Basically, the MCS application is designed to be able to interact with the sensor components within the smartphone. Data is collected and has to be periodically uploaded to the server, where analytical operation is undertaken to produce meaningful information. In principle, the MCS application is able to provide viable noise data in many different areas in Kuala Lumpur. The main benefit that the MCS application can offer is the ability to provide continuous data collection with minimal resource needed.

  • References

    1. [1] R. Ganti, F. Ye, and H. Lei, “Mobile crowdsensing: Current state and future challenges,†IEEE Communications Magazine, vol. 49, pp. 32–39, 2011.

      [2] Tilak, Sameer. "Real-world deployments of -participatory sensing applications: Current trends and future directions" International Scholarly Research Notices (2013).

      [3] M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard,R. West, and P. Boda, “Peir, the personal environmental impact report, as a platform for participatory sensing systems research†in Proceedings of the 7th international conference on Mobile systems, applications, and services. ACM, 2009, pp. 55–68.

      [4] S. Morishita, S. Maenaka, D. Nagata, M. Tamai, K. Yasumoto, T. Fukukura, and K. Sato, “Sakurasensor: quasi-realtime cherry-lined roads detection through participatory video sensing by cars,†in Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2015, pp. 695–705.

      [5] R. Ganti, F. Ye, and H. Lei, “Mobile crowdsensing: Current state and future challenges†IEEE Communications Magazine, vol. 49, pp. 32–39, 2011.

      [6] A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, R. A. Peterson, H. Lu,X. Zheng, M. Musolesi, K. Fodor, and G.-S. Ahn, “The rise of people-centric sensing,†Internet Computing, IEEE, vol. 12, no. 4, pp. 12–21, 2008.

      [7] W. Sherchan, P. P. Jayaraman, S. Krishnaswamy, A. Zaslavsky, S. Loke, and A. Sinha, “Using on-the-move mining for mobile crowdsensing†in MDM, 2012, pp. 115–124.

      [8] T. W. Malone, R. Laubacher, and C. Dellarocas, “The collective intelligence genome,†IEEE Engineering Management Review, vol. 38, no. 3, p. 38, 2010.

      [9] D. Hasenfratz, O. Saukh, S. Sturzenegger, and L. Thiele, “Participatory air pollution monitoring using smartphones,†in International Workshop on Mobile Sensing, IPSN, 2012.

      [10] Minister of Public Works and Government Services Canada (August 2011). Railway Noise Measurement and reporting Methodology, Canadian Transportation Agency. “Retrieve from https://www.otccta.gc.ca/eng/â€

      [11] Burke J, Estrin D, Hansen M, and et al, “Participatory sensingâ€, Workshop on World-Sensor-Web (WSW06): Mobile Device Centric Sensor Networks and Application ACM, pp. 117–134, 2006.

      [12] Lane N D, Eisenman S B, Musolesi M, and et al, “Urban sensing systems: opportunistic or participatory?â€, Proceedings of the 9th workshop on Mobile computing systems and applications, pp. 11–16, 2008.

      [13] Environment Quality Act 1974 “An Act relating to the prevention, abatement, control of pollution and enhancement of the environment, and for purposes connected therewith.†“Retrieve from https://www.env.go.jpâ€

      [14] Department of Occupational Safety and Health, “Factories and Machinery Act 1967 [ACT 139] P.U. (A) 1/1989 Factories and Machinery (Noise Exposure) Regulations 1989, “Retrieve from http://www.dosh.gov.myâ€.

      [15] Birgitta Berglund, Thomas Lindvall, Dietrich H Schwela, “Guidelines for Community Noise, WHO, Geneva, page no xviâ€.

      [16] Maximum permissible sound level (LAeq) by receiving land use for planning and new development (Air Division and Strategic Communications Division, Department of Environment Malaysia, October 2007)

      [17] Noise tube website and community memory, “Retrieved from http://www.noisetube.net, 2009.â€

  • Downloads

  • How to Cite

    Zafar, R., Zuhairi, M. F., Yafi, E., Dao, H., & Salleh, H. M. (2018). Mobile Crowd Sensing Application for Noise Monitoring in Kuala Lumpur. International Journal of Engineering & Technology, 7(4.29), 196-202. https://doi.org/10.14419/ijet.v7i4.29.21969