A Distributed Range-Free Localization Scheme for Wireless Sensor Networks

碩士 === 國立中央大學 === 資訊工程研究所 === 94 === Location-related applications have become essential issues and have been widely proposed in wireless sensor networks (WSNs). For those applications, radio propagation irregularity and the cost of the additional devices on sensor nodes make range-based localizatio...

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Bibliographic Details
Main Authors: Pei-Chun Chen, 陳佩君
Other Authors: Jang-Ping Sheu
Format: Others
Language:en_US
Published: 2006
Online Access:http://ndltd.ncl.edu.tw/handle/z4t4sn
Description
Summary:碩士 === 國立中央大學 === 資訊工程研究所 === 94 === Location-related applications have become essential issues and have been widely proposed in wireless sensor networks (WSNs). For those applications, radio propagation irregularity and the cost of the additional devices on sensor nodes make range-based localization schemes in WSNs very challenging. Instead, range-free localization schemes don’t have to utilize received signal strength to estimate distances and only needs simple and costless hardware. Thus, range-free localization schemes are more suitable for WSNs if only coarse accuracy is required. However,existing range-free localization schemes are either too costly or not accurate enough. To improve previous works, we present DRLS, a fully distributed range-free localization scheme for WSNs. We assume that only a few sensor nodes, called anchors, can get their locations by Global Positioning System (GPS) and the remaining nodes need to estimate their own locations by gathering the nearby neighboring information. We first find the coarse estimative locations of the nodes. Furthermore, we derive a refinement method to correct the coarse estimative locations according to the relative position of the farther anchor and the coarse estimative locations. We show that our DRLS scheme performs well when random node placement is considered. Besides, our scheme is superior to previous range-free localization schemes via extensive simulations under a variety anchor densities. Simulation results show that the proposed algorithm achieves better accuracy of estimative locations.