Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is...

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Main Authors: Marcin Bernas, Bartłomiej Płaczek
Format: Article
Language:English
Published: Wiley 2015-12-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2015/403242
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author Marcin Bernas
Bartłomiej Płaczek
author_facet Marcin Bernas
Bartłomiej Płaczek
author_sort Marcin Bernas
collection DOAJ
description The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speedup of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in real world indoor environment by using both stationary and mobile sensor nodes.
format Article
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institution Kabale University
issn 1550-1477
language English
publishDate 2015-12-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-9268e3211d584059af6768588c8b40cb2025-02-03T06:43:05ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-12-011110.1155/2015/403242403242Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor NetworksMarcin BernasBartłomiej PłaczekThe paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speedup of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in real world indoor environment by using both stationary and mobile sensor nodes.https://doi.org/10.1155/2015/403242
spellingShingle Marcin Bernas
Bartłomiej Płaczek
Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks
International Journal of Distributed Sensor Networks
title Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks
title_full Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks
title_fullStr Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks
title_full_unstemmed Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks
title_short Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks
title_sort fully connected neural networks ensemble with signal strength clustering for indoor localization in wireless sensor networks
url https://doi.org/10.1155/2015/403242
work_keys_str_mv AT marcinbernas fullyconnectedneuralnetworksensemblewithsignalstrengthclusteringforindoorlocalizationinwirelesssensornetworks
AT bartłomiejpłaczek fullyconnectedneuralnetworksensemblewithsignalstrengthclusteringforindoorlocalizationinwirelesssensornetworks