Localization of Sensors in Indoor Wireless Networks: An Observation Model Using WiFi RSS
Résumé
Indoor localization has become an important issue for wireless sensor networks. This paper presents a zoning-based localization technique that works efficiently in indoor environments. The targeted area is composed of several zones, the objective being to determine the zone of the sensor using an observation model. The observation model is constructed based on fingerprints collected as WiFi signals strengths received from surrounding Access Points. The method creates a belief functions framework that uses all available information to assign evidence to each zone. A hierarchical clustering technique is then applied to create a two-level hierarchy composed of clusters and of original zones in each cluster. At each level of the hierarchy, an Access Point selection approach is proposed to choose the best subset of Access Points in terms of discriminative capacity and redundancy. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.
Mots clés
- WiFi signals
- localization
- hierarchicalclustering
- belief functions
- Access point selection
- Statistical distributions
- Databases
- Wireless communication
- Access Points
- indoor radio
- pattern clustering
- radionavigation
- RSSI
- wireless LAN
- indoor localization
- indoor environments
- observation model
- WiFi signals strengths
- Redundancy
- hierarchical clustering technique
- Access Point selection approach
- indoor wireless networks
- WiFi RSS
- zoning-based localization technique
- belief functions framework
- two-level hierarchy
- Wireless fidelity
- Wireless sensor networks
- Sensors
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|---|
| Licence |