CFP last date
15 April 2024
Call for Paper
May Edition
IJAIS solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 15 April 2024

Submit your paper
Know more
Reseach Article

An Approach to Self-Locate Patients in a Psychiatric Center based on Received Signal Strength Indicator and Sensor Information History

by Doris-Khöler Nyabeye Pangop, Elie Tagne Fute, Emmanuel Tonye
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 37
Year of Publication: 2021
Authors: Doris-Khöler Nyabeye Pangop, Elie Tagne Fute, Emmanuel Tonye
10.5120/ijais2021451910

Doris-Khöler Nyabeye Pangop, Elie Tagne Fute, Emmanuel Tonye . An Approach to Self-Locate Patients in a Psychiatric Center based on Received Signal Strength Indicator and Sensor Information History. International Journal of Applied Information Systems. 12, 37 ( June 2021), 29-35. DOI=10.5120/ijais2021451910

@article{ 10.5120/ijais2021451910,
author = { Doris-Khöler Nyabeye Pangop, Elie Tagne Fute, Emmanuel Tonye },
title = { An Approach to Self-Locate Patients in a Psychiatric Center based on Received Signal Strength Indicator and Sensor Information History },
journal = { International Journal of Applied Information Systems },
issue_date = { June 2021 },
volume = { 12 },
number = { 37 },
month = { June },
year = { 2021 },
issn = { 2249-0868 },
pages = { 29-35 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number37/1118-2021451910/ },
doi = { 10.5120/ijais2021451910 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:12.958192+05:30
%A Doris-Khöler Nyabeye Pangop
%A Elie Tagne Fute
%A Emmanuel Tonye
%T An Approach to Self-Locate Patients in a Psychiatric Center based on Received Signal Strength Indicator and Sensor Information History
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 37
%P 29-35
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, research around sensor networks has made significant progress. Increasingly, sensor networks are more present at almost every level of daily life. An interesting application of these, is their use for the localization of mobile entities such as animals, vehicles, humans, etc. In this work, the interest is focused on the localization of patients in a psychiatric center. Most of the work around the location of mobile entities is based on models for planning or predicting the trajectory of the mobile entity. However, for humans, even more psychiatric patients, it is difficult if not almost impossible to predict or plan their displacement successfully. It is in this context that the present workoffers this simple and effective indoor localization approach, which is based on the received signal strength indicator and the history of the mobile sensor's journey, to determine its position. In this technique, patients wear sensors without GPS on their arm. It is these sensors that will locate patients in the center in real time. The implementation and simulation of this approach made it possible to validate its effectiveness in terms of accuracy and localization time.

References
  1. Timoteo Cayetano-Antonio, M. Mauricio-Lara, and Aldo G. Orozco-Lugo. Self-localization of sensor node using monte carlo method considering shadowing. 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), November 2020.
  2. Subir Halder and Amrita Ghosal. A survey on mobile anchor assisted localization techniques in wireless sensor networks. Wireless Networks, 22(7):23172336, November 2016.
  3. Guangjie Han, Jinfang Jiang, Chenyu Zhang, Trung Q. Duong, Mohsen Guizani, and George K. Karagiannidis. A survey on mobile anchor node assisted localization in wireless sensor networks. IEEE Communications Surveys and Tutorials, 18(Mars), 2016.
  4. Minh Tuan Ho. Implementation of an indoor localization system based on an analysis of the movement of an embedded device. PhD thesis, Université de Rennes 1, December 2013.
  5. Cuiran Li, Jianli Xie, Wei Wu, Haoshan Tian, and Yingxin Liang. Monte carlo localization algorithm based on particle swarm optimization. Automatika, 60(4):451–461, July 2019.
  6. FatihaMekelleche and HafidHaffaf. Classification and comparison of range-based localization techniques in wireless sensor networks. Journal of Communications, 12(4):221–227, Avril 2017.
  7. Asma Mesmoudi, Mohammed Feham, and Nabila Labraoui. Wireless sensor networks localization algorithms: A comprehensive survey. International Journal of Computer Networks and Communications, 5(6), November 2013.
  8. Anup Kumar Paul and Takuro Sato. Localization in wireless sensor networks: A survey on algorithms, measurement techniques, applications and challenges. Journal of Sensor and Actuator Networks, 6:24, October 2017.
  9. ZhiyuQiu, Lihong Wu, and Peixin Zhang. An efficient localization method for mobile nodes in wireless sensor networks. International Journal of Online and Biomedical Engineering (iJOE), 13(3), 2017.
  10. PinkiRathee and Sanjeev Indora. Survey on various localization techniques in wireless sensor networks. International Journal of Scientific and Engineering Research, 7(12):273 – 279, December 2016.
  11. Wilson Sakpere, Michael Adeyeye-Oshin, and Nhlanhla B.W. Mlitwa. A state-of-the-art survey of indoor positioning and navigation systems and technologies. South African Computer Journal, 29:145–197, December 2017.
  12. Parulpreet Singh, Arun Khosla, and Anil Kumar. Computational intelligence-based localization of moving target nodes using single anchor node in wireless sensor networks. Telecommunication Systems, Springer, March 2018.
  13. Hua Wu, Ju Liu, Zheng Dong, and Yang Liu. A hybrid mobile node localization algorithm based on adaptive mcb-pso approach in wireless sensor networks. Wireless Communications and Mobile Computing, June 2020.
  14. Junhua Yang, Yong Li, and Wei Cheng. An improved geometric algorithm for indoor localization. International Journal of Distributed Sensor Networks, 14, February 2018.
  15. Ali Yassin, Youssef Nasser, Mariette Awad, Ahmed AlDubai, Ran Liu, Chau Yuen, Ronald Raulefs, and Elias Aboutanios. Recent advances in indoor localization: A survey on theoretical approaches and applications. IEEE Communications Surveys and Tutorials, 19(February), 2017.
  16. Jiyoung Yi, Jahyoung Koo, and Hojung Cha. A localization technique for mobile sensor networks using archived anchor information. IEEE SECON 2008 proceedings, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Indoor localization Mobile sensor networks Received signal strength indicator Information history Accuracy