Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios
Local Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flig...
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doaj-0c45566d86134603bb7b031622489ee12021-04-02T23:01:44ZengMDPI AGSensors1424-82202021-04-01212458245810.3390/s21072458Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban ScenariosPaula Verde0Javier Díez-González1Rubén Ferrero-Guillén2Alberto Martínez-Gutiérrez3Hilde Perez4Department of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071 León, SpainDepartment of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071 León, SpainDepartment of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071 León, SpainDepartment of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071 León, SpainDepartment of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071 León, SpainLocal Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flights in restricted environments. LPS consists of ad-hoc deployments of sensors which meets the design requirements of each activity. Among LPS, those based on temporal measurements are attracting higher interest due to their trade-off among accuracy, robustness, availability, and costs. The Time Difference of Arrival (TDOA) is extended in the literature for LPS applications and consequently we perform, in this paper, an analysis of the optimal sensor deployment of this architecture for achieving practical results. This is known as the Node Location Problem (NLP) and has been categorized as NP-Hard. Therefore, heuristic solutions such as Genetic Algorithms (GA) or Memetic Algorithms (MA) have been applied in the literature for the NLP. In this paper, we introduce an adaptation of the so-called MA-Solis Wets-Chains (MA-SW-Chains) for its application in the large-scale discrete discontinuous optimization of the NLP in urban scenarios. Our proposed algorithm MA-Variable Neighborhood Descent-Chains (MA-VND-Chains) outperforms the GA and the MA of previous proposals for the NLP, improving the accuracy achieved by 17% and by 10% respectively for the TDOA architecture in the urban scenario introduced.https://www.mdpi.com/1424-8220/21/7/2458Cramèr-Rao Boundlocal positioning systemmemetic algorithm chainsnode location problemtime difference of arrivalvariable neighborhood descent |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Paula Verde Javier Díez-González Rubén Ferrero-Guillén Alberto Martínez-Gutiérrez Hilde Perez |
spellingShingle |
Paula Verde Javier Díez-González Rubén Ferrero-Guillén Alberto Martínez-Gutiérrez Hilde Perez Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios Sensors Cramèr-Rao Bound local positioning system memetic algorithm chains node location problem time difference of arrival variable neighborhood descent |
author_facet |
Paula Verde Javier Díez-González Rubén Ferrero-Guillén Alberto Martínez-Gutiérrez Hilde Perez |
author_sort |
Paula Verde |
title |
Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios |
title_short |
Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios |
title_full |
Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios |
title_fullStr |
Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios |
title_full_unstemmed |
Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios |
title_sort |
memetic chains for improving the local wireless sensor networks localization in urban scenarios |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-04-01 |
description |
Local Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flights in restricted environments. LPS consists of ad-hoc deployments of sensors which meets the design requirements of each activity. Among LPS, those based on temporal measurements are attracting higher interest due to their trade-off among accuracy, robustness, availability, and costs. The Time Difference of Arrival (TDOA) is extended in the literature for LPS applications and consequently we perform, in this paper, an analysis of the optimal sensor deployment of this architecture for achieving practical results. This is known as the Node Location Problem (NLP) and has been categorized as NP-Hard. Therefore, heuristic solutions such as Genetic Algorithms (GA) or Memetic Algorithms (MA) have been applied in the literature for the NLP. In this paper, we introduce an adaptation of the so-called MA-Solis Wets-Chains (MA-SW-Chains) for its application in the large-scale discrete discontinuous optimization of the NLP in urban scenarios. Our proposed algorithm MA-Variable Neighborhood Descent-Chains (MA-VND-Chains) outperforms the GA and the MA of previous proposals for the NLP, improving the accuracy achieved by 17% and by 10% respectively for the TDOA architecture in the urban scenario introduced. |
topic |
Cramèr-Rao Bound local positioning system memetic algorithm chains node location problem time difference of arrival variable neighborhood descent |
url |
https://www.mdpi.com/1424-8220/21/7/2458 |
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