Pengembangan Mekanisme Change Detection Untuk Efisiensi Energi Pada Wifi-Based Indoor Positioning System

Authors

  • Ade Hendi Departemen Informatika, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Waskitho Wibisono Departemen Informatika, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Ary Mazaharudin Shiddiqi Departemen Informatika, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

DOI:

https://doi.org/10.31961/eltikom.v4i1.157

Keywords:

indoor positioning system, change detection, bluetooth, sampling secara adaptif

Abstract

Pengembangan mekanisme change detection mempunyai peranan penting terhadap Indoor Positioning System (IPS). Namun permasalahan yang masih umum dijumpai adalah konsumsi energi yang tinggi, karena proses WiFi scanning berjalan secara terus menerus. Proses WiFi scanning mengirimkan data dari klien ke server secara terus menerus, terkadang memberikan informasi yang sama dan berulang kepada user. Informasi yang dikirim secara redundansi bisa berdampak pada konsumsi energi yang tinggi. Paper ini mengusulkan mekanisme perbaikan dengan change detection untuk penghematan energi dalam melakukan sampling secara adaptif pada kekuatan sinyal WiFi dengan accelerometer sebagai trigger. Mekanisme change detection yang dilakukan adalah mengukur kekuatan sinyal pada accelerometer dengan menentukan silent zone. Silent Zone merupakan rentang nilai yang didapatkan ketika accelerometer dalam kondisi diam. Apabila diketahui nilai kekuatan sinyal pada accelerometer melebihi nilai silent zone, maka diidentifikasi user dalam kondisi bergerak dan secara otomatis proses WiFi scanning akan berjalan. Change detection dengan Bluetooth mempunyai proses yang sama dengan menggunakan accelerometer. Algoritma yang diusulkan dapat menghasilkan penghematan daya baterai sebesar  4,384% untuk scanning dengan change detection menggunakan accelerometer dan 2,666% untuk change detection menggunakan Bluetooth.

Downloads

Download data is not yet available.

References

J. Niu, B. Wang, L. Shu, T. Q. Duong, and Y. Chen, “ZIL: An Energy-Efficient Indoor Localization System Using ZigBee Radio to Detect WiFi Fingerprints,” IEEE J. Sel. Areas Commun., vol. 33, no. 7, pp. 1431–1442, 2015.
W. Waqar, Y. Chen, and A. Vardy, “Smartphone positioning in sparse Wi-Fi environments,” Comput. Commun., vol. 73, pp. 108–117, 2016.
X. Du, “Map-assisted Indoor Positioning Utilizing Ubiquitous WiFi Signals,” no. February, 2018.
X. Y. Liu, S. Aeron, V. Aggarwal, X. Wang, and M. Y. Wu, “Adaptive Sampling of RF Fingerprints for Fine-Grained Indoor Localization,” IEEE Trans. Mob. Comput., vol. 15, no. 10, pp. 2411–2423, 2016.
T. ; S. T. Sugino, Kyohei ; Niwa, Yusuke ; Shiramatsu, Shun ; Ozono, “Developing a Human Motion Detector using Bluetooth Beacons and its Applications,” Inf. Eng. Express, vol. 1, no. 4, p. PP.95-105, 2015.
A. Lourenço et al., “Activity Recognition from Accelerometer Data on a Mobile Phone,” Distrib. Comput. Artif. Intell. Bioinformatics, Soft Comput. Ambient Assist. Living, vol. 5518, no. June 2009, pp. 954–963, 2009.
D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring,” IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 1, pp. 156–167, 2006.
M. J. Mathie, A. C. F. Coster, N. H. Lovell, B. G. Celler, S. R. Lord, and A. Tiedemann, “A pilot study of long-term monitoring of human movements in the home using accelerometry,” J. Telemed. Telecare, vol. 10, no. 3, pp. 144–151, 2004.
M. A. Hoque, M. Siekkinen, and J. K. Nurminen, “Energy Efficient Multimedia Streaming to Mobile Devices – A Survey,” no. March, 2014.
H. Sajid and A. Al, “Indoor navigation to estimate energy consumption in android platform,” vol. 3, no. 1, pp. 32–36, 2018.
Y. U. Gu, F. Ren, and S. Member, “Energy-Efficient Indoor Localization of Smart Hand-Held Devices Using Bluetooth,” vol. 3, 2015.
J. Tuta and M. B. Juric, “A self-adaptive model-based Wi-Fi indoor localization method,” Sensors (Switzerland), vol. 16, no. 12, 2016.
N. Vallina-rodriguez, P. Hui, J. Crowcroft, and A. Rice, “Exhausting Battery Statistics,” no. February, pp. 9–14, 2010.
H. Sajid and A. Al, “Indoor navigation to estimate energy consumption in android platform,” vol. 3, no. 1, pp. 32–36, 2018.
A. Alvarez-Alvarez, J. M. Alonso, and G. Trivino, “Human activity recognition in indoor environments by means of fusing information extracted from intensity of WiFi signal and accelerations,” Inf. Sci. (Ny)., vol. 233, no. June, pp. 162–182, 2013.
D. Aiordachioaie, “On quick-change detection based on process adaptive modelling and identification,” 2014 Int. Conf. Dev. Appl. Syst. DAS 2014 - Conf. Proc., pp. 25–28, 2014.
T. Duc-tan, N. Dinh-chinh, T. Duc-nghia, and T. Duc-tuyen, “Development of a Rainfall-Triggered Landslide System using Wireless Accelerometer Network,” vol. 7, no. September, pp. 14–24, 2015.
S. S. Ho and H. Wechsler, “A Martingale framework for detecting changes in data streams by testing exchangeability,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 12, pp. 2113–2127, 2010.
D. Aiordachioaie, “On quick-change detection based on process adaptive modelling and identification,” 2014 Int. Conf. Dev. Appl. Syst. DAS 2014 - Conf. Proc., pp. 25–28, 2014.
A. Ahrabian, T. Elsaleh, Y. Fathy, and P. Barnaghi, “Detecting changes in the variance of multi-sensory accelerometer data using MCMC,” Proc. IEEE Sensors, vol. 2017-Decem, pp. 1–3, 2017.
D. Aiordachioaie, “On quick-change detection based on process adaptive modelling and identification,” 2014 Int. Conf. Dev. Appl. Syst. DAS 2014 - Conf. Proc., pp. 25–28, 2014.
L. I. Kuncheva, “Change detection in streaming multivariate data using likelihood detectors,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 5, pp. 1175–1180, 2013.
R. Sebastião and J. Gama, “Change detection in learning histograms from data streams,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4874 LNAI, no. December, pp. 112–123, 2007.
Y. Wang, X. Yang, Y. Zhao, Y. Liu, and L. Cuthbert, “Bluetooth positioning using RSSI and triangulation methods,” 2013 IEEE 10th Consum. Commun. Netw. Conf. CCNC 2013, pp. 837–842, 2013.
W. Sri Indrawanti, Annisaa ; Wibisono, “A CHANGE DETECTION AND RESOURCE - AWARE DATA SENSING APPROACHES FOR IMPROVING THE REPORTING PROTOCOL MECHANISM FOR MOBILE USER,” vol. 2, pp. 92–99, 2015.

Downloads

Published

17-06-2020

How to Cite

[1]
Hendi, A. et al. 2020. Pengembangan Mekanisme Change Detection Untuk Efisiensi Energi Pada Wifi-Based Indoor Positioning System. Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer. 4, 1 (Jun. 2020), 22–38. DOI:https://doi.org/10.31961/eltikom.v4i1.157.

Issue

Vol. 4 No. 1 (2020)

Section

Articles

License

All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).