Tracking of Mobile Sensors Using Belief Functions in Indoor Wireless Networks
Résumé
Localization of mobile sensors is an important research issue in wireless sensor networks. Most indoor localization schemes focus on determining the exact position of these sensors. This paper presents a zoning-based tracking technique that works efficiently in indoor environments. The targeted area is composed of several zones, the objective being to determine the zone of the mobile sensor in a real time tracking process. The proposed method creates a belief functions framework that combines evidence using the sensors mobility and observations. To do this, a mobility model is proposed by using the previous state of the sensor and its assumed maximum speed. Also, an observation model is constructed based on fingerprints collected as Wi-Fi signals strengths received from surrounding access points. This model can be extended via hierarchical clustering and access point selection. Real experiments demonstrate the effectiveness of this approach and its competence compared with state-of-the-art methods.
Mots clés
machine learning
belief networks
indoor radio
mobile radio
sensor placement
mobile sensor tracking
mobile sensor localization
indoor wireless sensor network
indoor localization scheme
zoning-based tracking technique
indoor environment
belief function framework
Wi-Fi signal strength
hierarchical clustering
Sensors
Mobile communication
Wireless fidelity
Indoor environments
Databases
Wireless sensor networks
Target tracking
Access point selection
belief functions
mobility
tracking
WiFi signals