Research Article An Adaptive Linearized Method for Localizing Video Endoscopic Capsule Using Weighted Centroid Algorithm Umma Hany 1 and Khan A. Wahid 2 1 Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh 2 Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9 Correspondence should be addressed to Khan A. Wahid; khan.wahid@usask.ca Received 22 September 2014; Accepted 26 January 2015 Academic Editor: Chih-Yung Chang Copyright © 2015 U. Hany and K. A. Wahid. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Video Capsule Endoscope (VCE) sends images of abnormalities in the gastrointestinal (GI) tract. While the physicians receive these images, they have little idea of their exact location which is needed for proper treatment. Te proposed localization system consists of a 3D antenna array (with 8 receiver sensors) and one transmitter embedded inside the electronic capsule. We propose an adaptive linearized method of localization using Weighted Centroid Localization (WCL) where the position is calculated by averaging the weighted sum of the reference positions. In our proposed system, frst we identify the path loss attenuation exponents using linear least square regression of the collected data (RSSI versus distance). Ten the path loss model is linearized to minimize the path loss deviation which is mainly caused due to the nonhomogeneous environment of radio propagation. Ten the instantaneous path loss (PL) measured by the sensors is attenuated to the above linearized model and considered as the weight of the sensors to fnd the location of the capsule using WCL. Finally a calibration process is applied using linear least square regression. To assess the performance, we model the path loss and implement the algorithm in Matlab for 2,530 possible positions with a resolution of 1 mm. Te results show that the algorithm achieves high localization accuracy compared with other related methods when simulated using a 3D small intestine model. 1. Introduction Video capsule endoscopes (VCE) are used to diagnose lesions along digestive tracts. Tey send clear images of abnormalities in the gastrointestinal tract (GI tract). While the physicians receive the clear images of the abnormalities, they have little idea of their exact location [1]. Tus, it is necessary to know the exact location of the endoscopic capsule inside the GI tract for proper diagnosis of the intestinal abnormalities. Te current literature is very rich in algorithms designed for localization outside the human body. Very few localization methods [2–4] are available in the literature to localize endoscopic capsule which are based on electromagnetic feld and magnetic feld strength. As RSSI based techniques are cost-efective and have no adverse health efects, they have also been chosen for use with the Smart pill capsule [5] in USA and the M2A capsule [6] in Israel. RF localization schemes include both range-based [7–9] and range-free [10–17] algorithms. Within those, range-free positioning schemes, such as Centroid Localization schemes [10, 11], have attracted a lot of interests because of their simplicity and robustness to changes in wireless propagation properties such as path loss. Centroid Localization (CL) [10] localizes the transmitting source of a message to the coordi- nate obtained from averaging the coordinates of all receiving devices within range. Weighted Centroid Localization (WCL) [12] localizes the active tag as the weighted average of the sensors positions within its range. WCL proposed in [18] assigns a weight to each of the receiver coordinates, inversely proportional to either the known transmitter-receiver (T- R) distance or the link quality indicator available in the ZigBee/IEEE 802.15.4 sensor networks [19]. In [20, 21], the WCL mechanism is extended using normalized values of the link quality indicator and RSSI. Te authors in [22] conducted an indoor experiment to determine a set of fxed parameters Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2015, Article ID 342428, 18 pages http://dx.doi.org/10.1155/2015/342428