A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks
Abstract The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes an...
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Online Access: | http://link.springer.com/article/10.1186/s13638-018-1037-1 |
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doaj-38ccd2c2b72845368dea12d5ff5949362020-11-24T23:56:45ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992018-02-01201811910.1186/s13638-018-1037-1A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networksMiao Qin0Rongbo Zhu1School of management, Wuhan University of TechnologyCollege of Computer Science, South-Central University for NationalitiesAbstract The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution function (PDF) in the previous time slot to estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples, which further affects the node’s localization accuracy. In this paper, differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally, the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithm provides a better position estimation with less localization error.http://link.springer.com/article/10.1186/s13638-018-1037-1Economic forecastingWireless sensor networksValid sampleLocalizationDifferential evolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Miao Qin Rongbo Zhu |
spellingShingle |
Miao Qin Rongbo Zhu A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks EURASIP Journal on Wireless Communications and Networking Economic forecasting Wireless sensor networks Valid sample Localization Differential evolution |
author_facet |
Miao Qin Rongbo Zhu |
author_sort |
Miao Qin |
title |
A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks |
title_short |
A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks |
title_full |
A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks |
title_fullStr |
A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks |
title_full_unstemmed |
A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks |
title_sort |
monte carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks |
publisher |
SpringerOpen |
series |
EURASIP Journal on Wireless Communications and Networking |
issn |
1687-1499 |
publishDate |
2018-02-01 |
description |
Abstract The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution function (PDF) in the previous time slot to estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples, which further affects the node’s localization accuracy. In this paper, differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally, the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithm provides a better position estimation with less localization error. |
topic |
Economic forecasting Wireless sensor networks Valid sample Localization Differential evolution |
url |
http://link.springer.com/article/10.1186/s13638-018-1037-1 |
work_keys_str_mv |
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1725456742301564928 |