Hershey, Pennsylvania
New York, New YorkBeijing, China
Distance-Based Localization in Wireless Sensor Network Using Exponential Grey Prediction Model

Distance-Based Localization in Wireless Sensor Network Using Exponential Grey Prediction Model

Dipak W. Wajgi (Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, India), Jitendra V. Tembhurne (Department of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, India), and Rakhi D. Wajgi (Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India)
Copyright: © 2024 |Pages: 22
DOI: 10.4018/IJBDCN.353393
Article PDF Download
Open access articles are freely available for download

Abstract

Localization in wireless sensor network is the key for many applications in which location of occurrence of event is very important. Distance based localization techniques have proved to be more accurate and feasible for the location calculation of the sensor nodes. Moreover, Received Signal Strength Indicator (RSSI) based techniques are well adopted by the researcher to calculate the distance between the sensor nodes. The optimized value of RSSI may estimate the location more precisely. In this paper, Exponential Grey prediction model and weighted predicted RSSI values are utilized to provide estimated values of RSSI which are then optimized using an objective function. The decreasing term used in the differential equation of the Grey prediction model provides better prediction accuracy which if optimized provides efficient result. The conventional Grey prediction model is limited by the sample space and scope of applications. The Exponential Grey prediction model used in the proposed algorithm removes those drawbacks and can be applied in real time applications with larger sample space. The objective function used in the proposed algorithm uses weighting factor to be applied to Grey predicted RSSI and weighted predicted RSSI to estimate the RSSI value more accurately. The objective function calculates more near values of RSSI to localize the node more accurately. The simulation results obtained are compared with the latest techniques of RSSI calculation like weighted centroid method, RSSI quantization, TMA and FRBW algorithm. The results show that the proposed algorithm outperform the existing methods for RSSI estimation.
Article Preview
Top

The distance-based localization algorithms which uses received signal strength Indicator (RSSI) information to estimate the distance and the location information, are discussed in this section.

Complete Article List

Search this Journal:
Reset
Volume 20: 1 Issue (2025)
Volume 19: 1 Issue (2024)
Volume 18: 2 Issues (2022): 1 Released, 1 Forthcoming
Volume 17: 2 Issues (2021)
Volume 16: 2 Issues (2020)
Volume 15: 2 Issues (2019)
Volume 14: 2 Issues (2018)
Volume 13: 2 Issues (2017)
Volume 12: 2 Issues (2016)
Volume 11: 2 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing