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
indoor radio
pattern clustering
radionavigation
RSSI
wireless LAN
indoor localization
indoor environments
observation model
WiFi signals strengths
Access Points
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
Redundancy
Wireless communication
Databases
Statistical distributions
Access point selection
belief functions
hierarchicalclustering
localization
WiFi signals
| Origine | Fichiers produits par l'(les) auteur(s) |
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