Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks

Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks

To obtain accurate location information of individual sensor nodes is of vital importance in wireless sensor networks (WSNs), especially for objective tracking applications. However, it is challenging to acquire fine-grained localization accuracy because of resource constraints of sensor nodes, unre...

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Main Authors: Xiaoyan Yin, Qi Zhang, Xin Zheng, Liang Wang, Hui Zhao, Weike Nie
Format: Article
Language:English
Published: SAGE Publishing 2015-08-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2015/280674
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spelling doaj-301f6c9693104fbcaa198e19d14a28022020-11-25T03:08:35ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/280674280674Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor NetworksXiaoyan Yin0Qi Zhang1Xin Zheng2Liang Wang3Hui Zhao4Weike Nie5 School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada, M5S 3G4 School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, ChinaTo obtain accurate location information of individual sensor nodes is of vital importance in wireless sensor networks (WSNs), especially for objective tracking applications. However, it is challenging to acquire fine-grained localization accuracy because of resource constraints of sensor nodes, unreliable wireless communication, and cost. Moreover, heterogeneous characteristics of both sensor nodes and applications make this problem even harder to solve. In this paper, we propose NLMR , a novel on-demand node localization technology based on multiresolution model. NLMR comprises three phases: (1) subregion classification, which categorizes regions into subregions with either uniform node deployment or nonuniform node deployment; (2) multiresolution model construction, which creates a multiresolution model that caters for diverse localization granularity; (3) node localization, which allows the control center to estimate the locations of sensor nodes in a centralized manner. Our analysis and simulation results demonstrate the performance of NLMR and verify that our scheme can provide diverse localization granularity with high probability.https://doi.org/10.1155/2015/280674
collection DOAJ
language English
format Article
sources DOAJ
author Xiaoyan Yin
Qi Zhang
Xin Zheng
Liang Wang
Hui Zhao
Weike Nie
spellingShingle Xiaoyan Yin
Qi Zhang
Xin Zheng
Liang Wang
Hui Zhao
Weike Nie
Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks
International Journal of Distributed Sensor Networks
author_facet Xiaoyan Yin
Qi Zhang
Xin Zheng
Liang Wang
Hui Zhao
Weike Nie
author_sort Xiaoyan Yin
title Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks
title_short Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks
title_full Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks
title_fullStr Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks
title_full_unstemmed Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks
title_sort localizing wireless sensors with diverse granularities in wireless sensor networks
publisher SAGE Publishing
series International Journal of Distributed Sensor Networks
issn 1550-1477
publishDate 2015-08-01
description To obtain accurate location information of individual sensor nodes is of vital importance in wireless sensor networks (WSNs), especially for objective tracking applications. However, it is challenging to acquire fine-grained localization accuracy because of resource constraints of sensor nodes, unreliable wireless communication, and cost. Moreover, heterogeneous characteristics of both sensor nodes and applications make this problem even harder to solve. In this paper, we propose NLMR , a novel on-demand node localization technology based on multiresolution model. NLMR comprises three phases: (1) subregion classification, which categorizes regions into subregions with either uniform node deployment or nonuniform node deployment; (2) multiresolution model construction, which creates a multiresolution model that caters for diverse localization granularity; (3) node localization, which allows the control center to estimate the locations of sensor nodes in a centralized manner. Our analysis and simulation results demonstrate the performance of NLMR and verify that our scheme can provide diverse localization granularity with high probability.
url https://doi.org/10.1155/2015/280674
work_keys_str_mv AT xiaoyanyin localizingwirelesssensorswithdiversegranularitiesinwirelesssensornetworks
AT qizhang localizingwirelesssensorswithdiversegranularitiesinwirelesssensornetworks
AT xinzheng localizingwirelesssensorswithdiversegranularitiesinwirelesssensornetworks
AT liangwang localizingwirelesssensorswithdiversegranularitiesinwirelesssensornetworks
AT huizhao localizingwirelesssensorswithdiversegranularitiesinwirelesssensornetworks
AT weikenie localizingwirelesssensorswithdiversegranularitiesinwirelesssensornetworks
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