Machine Learning Based Localization in Large-Scale Wireless Sensor Networks
The rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4179 |
| _version_ | 1798006110447206400 |
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| author | Ghulam Bhatti |
| author_facet | Ghulam Bhatti |
| author_sort | Ghulam Bhatti |
| collection | DOAJ |
| description | The rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications, the knowledge of the location of sensor nodes is crucial for meaningful interpretation of the sensor data. Localization refers to the process of estimating the location of sensor nodes in a WSN. Self-localization is required in large wireless sensor networks where these nodes cannot be manually positioned. Traditional methods iteratively localize these nodes by using triangulation. However, the inherent instability in wireless signals introduces an error, however minute it might be, in the estimated position of the target node. This results in the embedded error propagating and magnifying rapidly. Machine learning based localizing algorithms for large wireless sensor networks do not function in an iterative manner. In this paper, we investigate the suitability of some of these algorithms while exploring different trade-offs. Specifically, we first formulate a novel way of defining multiple feature vectors for mapping the localizing problem onto different machine learning models. As opposed to treating the localization as a classification problem, as done in the most of the reported work, we treat it as a regression problem. We have studied the impact of varying network parameters, such as network size, anchor population, transmitted signal power, and wireless channel quality, on the localizing accuracy of these models. We have also studied the impact of deploying the anchor nodes in a grid rather than placing these nodes randomly in the deployment area. Our results have revealed interesting insights while using the multivariate regression model and support vector machine (SVM) regression model with radial basis function (RBF) kernel. |
| first_indexed | 2024-04-11T12:49:56Z |
| format | Article |
| id | doaj.art-875f221df04d46899cc97027ed5d15cf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:49:56Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-875f221df04d46899cc97027ed5d15cf2022-12-22T04:23:14ZengMDPI AGSensors1424-82202018-11-011812417910.3390/s18124179s18124179Machine Learning Based Localization in Large-Scale Wireless Sensor NetworksGhulam Bhatti0Department of Computer Science, Taif University, Taif 21974, Saudi ArabiaThe rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications, the knowledge of the location of sensor nodes is crucial for meaningful interpretation of the sensor data. Localization refers to the process of estimating the location of sensor nodes in a WSN. Self-localization is required in large wireless sensor networks where these nodes cannot be manually positioned. Traditional methods iteratively localize these nodes by using triangulation. However, the inherent instability in wireless signals introduces an error, however minute it might be, in the estimated position of the target node. This results in the embedded error propagating and magnifying rapidly. Machine learning based localizing algorithms for large wireless sensor networks do not function in an iterative manner. In this paper, we investigate the suitability of some of these algorithms while exploring different trade-offs. Specifically, we first formulate a novel way of defining multiple feature vectors for mapping the localizing problem onto different machine learning models. As opposed to treating the localization as a classification problem, as done in the most of the reported work, we treat it as a regression problem. We have studied the impact of varying network parameters, such as network size, anchor population, transmitted signal power, and wireless channel quality, on the localizing accuracy of these models. We have also studied the impact of deploying the anchor nodes in a grid rather than placing these nodes randomly in the deployment area. Our results have revealed interesting insights while using the multivariate regression model and support vector machine (SVM) regression model with radial basis function (RBF) kernel.https://www.mdpi.com/1424-8220/18/12/4179wireless sensor networkslocalizationrandom vs. grid placementsimulatoinsInternet of Things (IoT)machine learning algorithmsmodel fittingsupport vector machinesregression |
| spellingShingle | Ghulam Bhatti Machine Learning Based Localization in Large-Scale Wireless Sensor Networks Sensors wireless sensor networks localization random vs. grid placement simulatoins Internet of Things (IoT) machine learning algorithms model fitting support vector machines regression |
| title | Machine Learning Based Localization in Large-Scale Wireless Sensor Networks |
| title_full | Machine Learning Based Localization in Large-Scale Wireless Sensor Networks |
| title_fullStr | Machine Learning Based Localization in Large-Scale Wireless Sensor Networks |
| title_full_unstemmed | Machine Learning Based Localization in Large-Scale Wireless Sensor Networks |
| title_short | Machine Learning Based Localization in Large-Scale Wireless Sensor Networks |
| title_sort | machine learning based localization in large scale wireless sensor networks |
| topic | wireless sensor networks localization random vs. grid placement simulatoins Internet of Things (IoT) machine learning algorithms model fitting support vector machines regression |
| url | https://www.mdpi.com/1424-8220/18/12/4179 |
| work_keys_str_mv | AT ghulambhatti machinelearningbasedlocalizationinlargescalewirelesssensornetworks |