Reference number : GC-2017-Q1-02909, amendment number : , agreement type : G, recipient legal name : Ye, Qiang (University of Prince Edward Island), recipient country : CA, recipient province : nan, recipient city : Prince Edward Island|Île-du-Prince-Édouard, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Localization is one of the key operations in mobile wireless networks. The Global Position System (GPS) has been widely used to locate mobile network nodes. However, GPS requires the line of sight to the satellites and stops working when the line of sight is not available (e.g. indoors or in a downtown canyon). In addition, GPS tends to consume much power, which is highly problematic for power-constrained devices such as smartphones and smartwatches. The problems associated with GPS have motivated many researchers to devise a variety of different localization methods for the scenarios in which GPS does not work well. Despite the fact that the state-of-the art non-GPS schemes can completely or partially avoid the use of GPS, they still suffer from several serious problems such as low localization precision and high computation complexity. In this project, we plan to explore varied non-GPS techniques to achieve high-precision mobile localization in both indoor and outdoor environments. 
 
Specifically, we plan to work on three types of localization mechanisms for the scenarios in which GPS does not work well: Refined Connectivity/Distance-based Localization, Sign-based Online Localization, and Localization Based on Crowdsourced Fingerprints. Refined Connectivity/Distance-based Localization is a generic method that can be applied to both indoor and outdoor environments. Sign-based Online Localization works well in a downtown canyon, where there are many signs and the line of sight to the satellites is often blocked by skyscrapers. Localization Based on Crowdsourced Fingerprints is tailored for pedestrians carrying power-constrained devices in a non-rural environment. 
 
One of our short-term objectives is to come up with the appropriate localization mechanisms for mobile wireless networks. The experimental results are expected to result in a series of high-impact publications in top-notch journals and conference proceedings. When possible, the proposed methods will be commercialized through the collaboration with industry partners. The other short-term objective is to train both undergraduate and graduate students through the proposed program, preparing them for their future academic or industry positions. Our long-term goal is to develop a first-class research lab that focuses on the research area of communication networks. We hope that, with the support from NSERC and other funding agencies, our lab will ultimately grow into a network research center that is competitive at the national/international level., program purpose french : Localization is one of the key operations in mobile wireless networks. The Global Position System (GPS) has been widely used to locate mobile network nodes. However, GPS requires the line of sight to the satellites and stops working when the line of sight is not available (e.g. indoors or in a downtown canyon). In addition, GPS tends to consume much power, which is highly problematic for power-constrained devices such as smartphones and smartwatches. The problems associated with GPS have motivated many researchers to devise a variety of different localization methods for the scenarios in which GPS does not work well. Despite the fact that the state-of-the art non-GPS schemes can completely or partially avoid the use of GPS, they still suffer from several serious problems such as low localization precision and high computation complexity. In this project, we plan to explore varied non-GPS techniques to achieve high-precision mobile localization in both indoor and outdoor environments. 
 
Specifically, we plan to work on three types of localization mechanisms for the scenarios in which GPS does not work well: Refined Connectivity/Distance-based Localization, Sign-based Online Localization, and Localization Based on Crowdsourced Fingerprints. Refined Connectivity/Distance-based Localization is a generic method that can be applied to both indoor and outdoor environments. Sign-based Online Localization works well in a downtown canyon, where there are many signs and the line of sight to the satellites is often blocked by skyscrapers. Localization Based on Crowdsourced Fingerprints is tailored for pedestrians carrying power-constrained devices in a non-rural environment. 
 
One of our short-term objectives is to come up with the appropriate localization mechanisms for mobile wireless networks. The experimental results are expected to result in a series of high-impact publications in top-notch journals and conference proceedings. When possible, the proposed methods will be commercialized through the collaboration with industry partners. The other short-term objective is to train both undergraduate and graduate students through the proposed program, preparing them for their future academic or industry positions. Our long-term goal is to develop a first-class research lab that focuses on the research area of communication networks. We hope that, with the support from NSERC and other funding agencies, our lab will ultimately grow into a network research center that is competitive at the national/international level., agreement title english : High-Precision Localization in Mobile Wireless Networks, agreement title french : High-Precision Localization in Mobile Wireless Networks, agreement number : RGPIN, agreement value : 130000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada