@INPROCEEDINGS{18.ntms.localization,
   author =  "Daniel AlShamaa and Farah Chehade and Paul Honeine",
   title =  "Localization of Sensors in Indoor Wireless Networks: An Observation Model Using {WiFi} {RSS}",
   booktitle =  "Proc. 9th IFIP International Conference on New Technologies, Mobility and Security - Workshop on Wireless Sensor Networks: Architectures, Deployments, and Trends",
   address =  "Paris, France",
   year  =  "2018",
   month = "26 - 28~" # feb,
   acronym =  "NTMS",
   url_paper  =  "http://honeine.fr/paul/publi/18.ntms.localization.pdf",
   abstract={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.}, 
   keywords={indoor radio, pattern clustering, radionavigation, RSSI, wireless LAN, wireless sensor networks, indoor localization, wireless sensor networks, 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, observation model, WiFi signals}, 
   doi={10.1109/NTMS.2018.8328699}, 
   ISSN={2157-4960}, 
}

{"_id":"vXg9okgvcitgJb2MC","bibbaseid":"alshamaa-chehade-honeine-localizationofsensorsinindoorwirelessnetworksanobservationmodelusingwifirss-2018","author_short":["AlShamaa, D.","Chehade, F.","Honeine, P."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Daniel"],"propositions":[],"lastnames":["AlShamaa"],"suffixes":[]},{"firstnames":["Farah"],"propositions":[],"lastnames":["Chehade"],"suffixes":[]},{"firstnames":["Paul"],"propositions":[],"lastnames":["Honeine"],"suffixes":[]}],"title":"Localization of Sensors in Indoor Wireless Networks: An Observation Model Using WiFi RSS","booktitle":"Proc. 9th IFIP International Conference on New Technologies, Mobility and Security - Workshop on Wireless Sensor Networks: Architectures, Deployments, and Trends","address":"Paris, France","year":"2018","month":"26 - 28 February","acronym":"NTMS","url_paper":"http://honeine.fr/paul/publi/18.ntms.localization.pdf","abstract":"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.","keywords":"indoor radio, pattern clustering, radionavigation, RSSI, wireless LAN, wireless sensor networks, indoor localization, wireless sensor networks, 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, observation model, WiFi signals","doi":"10.1109/NTMS.2018.8328699","issn":"2157-4960","bibtex":"@INPROCEEDINGS{18.ntms.localization,\n author = \"Daniel AlShamaa and Farah Chehade and Paul Honeine\",\n title = \"Localization of Sensors in Indoor Wireless Networks: An Observation Model Using {WiFi} {RSS}\",\n booktitle = \"Proc. 9th IFIP International Conference on New Technologies, Mobility and Security - Workshop on Wireless Sensor Networks: Architectures, Deployments, and Trends\",\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.localization.pdf\",\n abstract={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. 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