Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm
For many of the applications in which wireless sensor networks are used, it is important to know from which nodes or what location useful information is acquired. The Global Positioning System (GPS) is conventionally used to determine location. However, GPS systems are not ideal for many application...
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| Format: | Article |
| Language: | English |
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Springer
2017-01-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://www.atlantis-press.com/article/25880339/view |
| _version_ | 1818233508960665600 |
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| author | Taner Tuncer |
| author_facet | Taner Tuncer |
| author_sort | Taner Tuncer |
| collection | DOAJ |
| description | For many of the applications in which wireless sensor networks are used, it is important to know from which nodes or what location useful information is acquired. The Global Positioning System (GPS) is conventionally used to determine location. However, GPS systems are not ideal for many applications due to their excessive power consumption and high cost. As an alternative to GPS, distance and location can be estimated through the usage of at least 3 nodes with known locations. Received Signal Strength Indication (RSSI) is the simplest and most inexpensive technique used to determine distance and location, and is a standard feature on every sensor. However, RSSI can be affected by noise and environmental obstacles. For this reason, it is difficult to set up a mathematical model for RSSI. This paper presents a conversion of the Centroid Localization (CL) method in determining the location of a sensor of unknown location to the Intelligent Centroid Localization (ICL) Method. Fuzzy logic and genetic algorithm are employed in the ICL method. RSSI values measured by anchor nodes are applied as inputs to the fuzzy system in the ICL developed. Anchor nodes have been assigned weight values to increase the effect of high-value RSSI nodes in positioning. Therefore the fuzzy system’s output is defined as weight (w). The base values of the fuzzy system’s output membership functions are adjusted using genetic algorithm to minimize location error. Toward observing the performance of the proposed ICL, comparisons with the both Centroid Localization method and APIT (Approximate Point In Triangle) algorithm have been provided. The localization error has been reduced to minimum levels. |
| first_indexed | 2024-12-12T11:23:18Z |
| format | Article |
| id | doaj.art-8cd9b97871464241b01a9ba2b1d41b60 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-12T11:23:18Z |
| publishDate | 2017-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-8cd9b97871464241b01a9ba2b1d41b602022-12-22T00:25:58ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832017-01-0110110.2991/ijcis.2017.10.1.70Intelligent Centroid Localization Based on Fuzzy Logic and Genetic AlgorithmTaner TuncerFor many of the applications in which wireless sensor networks are used, it is important to know from which nodes or what location useful information is acquired. The Global Positioning System (GPS) is conventionally used to determine location. However, GPS systems are not ideal for many applications due to their excessive power consumption and high cost. As an alternative to GPS, distance and location can be estimated through the usage of at least 3 nodes with known locations. Received Signal Strength Indication (RSSI) is the simplest and most inexpensive technique used to determine distance and location, and is a standard feature on every sensor. However, RSSI can be affected by noise and environmental obstacles. For this reason, it is difficult to set up a mathematical model for RSSI. This paper presents a conversion of the Centroid Localization (CL) method in determining the location of a sensor of unknown location to the Intelligent Centroid Localization (ICL) Method. Fuzzy logic and genetic algorithm are employed in the ICL method. RSSI values measured by anchor nodes are applied as inputs to the fuzzy system in the ICL developed. Anchor nodes have been assigned weight values to increase the effect of high-value RSSI nodes in positioning. Therefore the fuzzy system’s output is defined as weight (w). The base values of the fuzzy system’s output membership functions are adjusted using genetic algorithm to minimize location error. Toward observing the performance of the proposed ICL, comparisons with the both Centroid Localization method and APIT (Approximate Point In Triangle) algorithm have been provided. The localization error has been reduced to minimum levels.https://www.atlantis-press.com/article/25880339/viewIntelligent Centroid LocalizationRSSILocalization ErrorFuzzy LogicGenetic Algorithm |
| spellingShingle | Taner Tuncer Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm International Journal of Computational Intelligence Systems Intelligent Centroid Localization RSSI Localization Error Fuzzy Logic Genetic Algorithm |
| title | Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm |
| title_full | Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm |
| title_fullStr | Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm |
| title_full_unstemmed | Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm |
| title_short | Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm |
| title_sort | intelligent centroid localization based on fuzzy logic and genetic algorithm |
| topic | Intelligent Centroid Localization RSSI Localization Error Fuzzy Logic Genetic Algorithm |
| url | https://www.atlantis-press.com/article/25880339/view |
| work_keys_str_mv | AT tanertuncer intelligentcentroidlocalizationbasedonfuzzylogicandgeneticalgorithm |