Volume 5, Issue 2 (February 2018), Pages: 33-36

----------------------------------------------

 Original Research Paper

 Title: Modified weighted centroid algorithm for indoor and outdoor positioning using wireless sensors network

 Author(s): Abdulqudos Y. Alnahari, Noor Azurati Ahmad *, Yusnaidi Yusof

 Affiliation(s):

 Advanced Informatics School, University Technology Malaysia, Kuala Lumpur, Malaysia

 https://doi.org/10.21833/ijaas.2018.02.006

 Full Text - PDF          XML

 Abstract:

Positioning, either outdoor or indoor, has been one of the most attractive fields for researchers to study and they apply different approaches in order to allocate a moving object. Some positioning approaches use Received Signal Strength (RSSI) in order to determine the location such as Fingerprinting, and Weighted Centroid Localization (WCL). On the other hand, some approaches use signal travelling time such as Time of Arrival (TOA) and Time Difference of Arrival (TDOA). However, the accuracy is still low due to the effect of RSSI to moving objects. This paper shows how to allocate a blind node in a wireless sensor network (WSN) using the proposed WCL approach and shows how the enhancement of the algorithm made better accuracy with mean square error, MSE, of 64.5 cm for indoor positioning and 123.0cm for outdoor positioning. One of previous WCL approaches reached accuracy with an error of as low as 15.0 cm but in simulation, and others used different approaches with MSE of 80.0 cm and another as high as 2.6 m. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Indoor positioning, Outdoor positioning, WSN, Zigbee, RSSI, WCLA

 Article History: Received 30 August 2017, Received in revised form 23 November 2017, Accepted 5 December 2017

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.02.006

 Citation:

 Alnahari AY, Ahmad NA, and Yusof Y (2018). Modified weighted centroid algorithm for indoor and outdoor positioning using wireless sensors network. International Journal of Advanced and Applied Sciences, 5(2): 33-36

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I2/Alnahari.html

----------------------------------------------

 References (9)

1. Alshami IH, Ahmad NA, and Sahibuddin S (2014). Dynamic WLAN fingerprinting RadioMap for adapted indoor positioning model. In: Fujita H and Selamat A (Eds.), Intelligent software methodologies, tools and techniques, SoMeT 2014. Communications in Computer and Information Science: 513. Springer, Cham. https://doi.org/10.1007/978-3-319-17530-0_9 
2. Alshami IH, Ahmad NA, Sahibuddin S, and Firdaus F (2017). Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments. Sensors, 17(8): 1789-1818. https://doi.org/10.3390/s17081789  PMid:28783047 PMCid:PMC5579808 
3. Alshami IH, Ahmad NA, Sahibuddin S, and Yusof YM (2016). The effect of people presence on WLAN RSS is governed by influence distance. In the 3rd International Conference on Computer and Information Sciences, IEEE, Kuala Lumpur, Malaysia: 197-202. https://doi.org/10.1109/ICCOINS.2016.7783214     
4. Baccar N and Bouallegue R (2016). Interval type 2 fuzzy localization for wireless sensor networks. EURASIP Journal on Advances in Signal Processing, 2016(1): 42-55. https://doi.org/10.1186/s13634-016-0340-4 
5. Chen Q, Liu H, Yu M, and Guo H (2012). RSSI ranging model and 3D indoor positioning with ZigBee network. In the IEEE/ION Position Location and Navigation Symposium, IEEE, Myrtle Beach, SC, USA: 1233-1239. https://doi.org/10.1109/PLANS.2012.6236979     
6. Habaebi MH, Khamis RO, Zyoud A, and Islam MR (2014). RSS based localization techniques for ZigBee wireless sensor network. In the International Conference on Computer and Communication Engineering, IEEE, Kuala Lumpur, Malaysia: 72-75. https://doi.org/10.1109/ICCCE.2014.32 
7. Henniges R (2012). Current approaches of Wi-Fi positioning. In the SERVICE-CENTRIC NETWORKING - SEMINAR WS2011/2012, IEEE, TU-Berlin, Germany: 1-8. PMid:23151127     
8. Ou X, Wu X, He X, Chen Z, and Yu QA (2015). An improved node localization based on adaptive iterated unscented Kalman filter for WSN. In the 10th Conference on Industrial Electronics and Applications, IEEE, Auckland, New Zealand: 393-398. https://doi.org/10.1109/ICIEA.2015.7334145 
9. Wang ZM and Zheng Y (2014). The study of the weighted centroid localization algorithm based on RSSI. In the International Conference on Wireless Communication and Sensor Network, IEEE, Wuhan, China: 276-279. https://doi.org/10.1109/WCSN.2014.63