@INPROCEEDINGS{18.ntms.mobility,
   author =  "Daniel AlShamaa and Farah Chehade and Paul Honeine",
   title =  "Mobility-based Tracking Using {WiFi} {RSS} in Indoor Wireless Sensor Networks",
   booktitle =  "Proc. 9th IFIP International Conference on New Technologies, Mobility and Security",
   address =  "Paris, France",
   year  =  "2018",
   month =    "26 - 28~" # feb,
   acronym =  "NTMS",
   url_paper  =  "http://honeine.fr/paul/publi/18.ntms.mobility.pdf",
   abstract={Tracking of mobile sensors is an important research issue in wireless sensor networks. 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 WiFi signals strengths received from surrounding Access Points. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.}, 
   keywords={indoor radio, mobility management (mobile radio), RSSI, target tracking, wireless LAN, wireless sensor networks, indoor environments, mobile sensor, real-time tracking process, mobility model, WiFi signals strengths, WiFi RSS, indoor wireless sensor networks, zoning-based tracking technique, access points, mobility-based tracking, Sensors, Wireless fidelity, Indoor environments, Data models, Wireless sensor networks, Real-time systems, Computational modeling, Belief functions, evidence fusion, mobility, tracking, WiFi signals}, 
   doi={10.1109/NTMS.2018.8328704}, 
   ISSN={2157-4960}, 
}%- Mobility & Wireless Networks Track paper

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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 WiFi signals strengths received from surrounding Access Points. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.","keywords":"indoor radio, mobility management (mobile radio), RSSI, target tracking, wireless LAN, wireless sensor networks, indoor environments, mobile sensor, real-time tracking process, mobility model, WiFi signals strengths, WiFi RSS, indoor wireless sensor networks, zoning-based tracking technique, access points, mobility-based tracking, Sensors, Wireless fidelity, Indoor environments, Data models, Wireless sensor networks, Real-time systems, Computational modeling, Belief functions, evidence fusion, mobility, tracking, WiFi signals","doi":"10.1109/NTMS.2018.8328704","issn":"2157-4960","bibtex":"@INPROCEEDINGS{18.ntms.mobility,\n author = \"Daniel AlShamaa and Farah Chehade and Paul Honeine\",\n title = \"Mobility-based Tracking Using {WiFi} {RSS} in Indoor Wireless Sensor Networks\",\n booktitle = \"Proc. 9th IFIP International Conference on New Technologies, Mobility and Security\",\n address = \"Paris, France\",\n year = \"2018\",\n month = \"26 - 28~\" # feb,\n acronym = \"NTMS\",\n url_paper = \"http://honeine.fr/paul/publi/18.ntms.mobility.pdf\",\n abstract={Tracking of mobile sensors is an important research issue in wireless sensor networks. 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 WiFi signals strengths received from surrounding Access Points. 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