Wireless fingerprinting indoor positioning using affinity propagation clustering methods
| Autor: | Pejman Abdollahzadeh Karegar |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Mahalanobis distance
Computer Networks and Communications Computer science business.industry Affinity propagation clustering 010401 analytical chemistry Probabilistic logic 020206 networking & telecommunications 02 engineering and technology computer.software_genre 01 natural sciences 0104 chemical sciences Norm (mathematics) 0202 electrical engineering electronic engineering information engineering Affinity propagation Wireless Data mining Electrical and Electronic Engineering Cluster analysis business Wireless sensor network computer Information Systems |
| Zdroj: | Wireless Networks. 24:2825-2833 |
| ISSN: | 1572-8196 1022-0038 |
| DOI: | 10.1007/s11276-017-1507-0 |
| Popis: | According to rapid extension of wireless sensor network localization, indoor localization using fingerprint has turned out to be more considerable lately. It contains of a database called Receive Strength Signal Indicator vectors, which is a primitive amount in wireless sensor network fingerprinting positioning. The equivalence of a few strategies is brought up from the literary works, and some new variants are presented in this study. A combination of a clustering strategy named affinity propagation and statistical and probabilistic positioning procedures is considered in this review and at the same time, the impact of some components in our methodology onto positioning precision will be investigated. Affinity propagation clustering method set up a common baseline for assessing the relative accuracy of various indoor location methods effectively. Eventually two coarse localization methods as Mahalanobis norm method and similarity to exemplar receive strength signal vector are compared based on positioning accuracy and performance. Experimental outcomes prove that the intended algorithm will advance the accuracy and localization error compared with the method without clustering. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink