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The Internet of Things is finally blooming through diverse applications, from home automation and monitoring to health tracking and quantified-self movement. Consumers deploy more and more low-rate and low-power connected devices that provide complex services. However, indoor positioning of devices using only their radio interface is still very imprecise. In this scenario, positioning these intelligent objects in their environment is necessary to provide geo-localized services, as well as to optimize the network operation. Such geo-localized services should be offered with inexpensive smart…mehr

Produktbeschreibung
The Internet of Things is finally blooming through diverse applications, from home automation and monitoring to health tracking and quantified-self movement. Consumers deploy more and more low-rate and low-power connected devices that provide complex services. However, indoor positioning of devices using only their radio interface is still very imprecise. In this scenario, positioning these intelligent objects in their environment is necessary to provide geo-localized services, as well as to optimize the network operation. Such geo-localized services should be offered with inexpensive smart objects without any central computation point, tedious calibration or intervention from a human. Toward this book, localization algorithms including distributed one are proposed and improved RSSI-based indoor localization accuracy results. Such refined positions are estimated by a wireless/sensor node. Simulation as well as experimental results obtained on real testbeds show a centimeter-levelaccuracy.
Autorenporträt
Dieng, N'deye Amy
N.Amy Dieng is graduated from the University of Technology of Troyes : master degree (engineer diploma) in computer science. She received its Ph.D in computer science with honors in 2014, funded by Orange Labs between Telecom Bretagne and Telecom ParisTech. She is currently working as an IT security and audit management expert.