Localization using neural networks in wireless sensor networks
Proceedings of the 1st international conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications
Exploiting RF-Scatter: Human Localization with Bistatic Passive UHF RFID-Systems
WIMOB '09 Proceedings of the 2009 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications
Characterizing the Influence of Human Presence on Bistatic Passive RFID-System
WIMOB '09 Proceedings of the 2009 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications
Radio Tomographic Imaging with Wireless Networks
IEEE Transactions on Mobile Computing
A Highly Integrable FPGA-Based Runtime-Configurable Multilayer Perceptron
AINA '13 Proceedings of the 2013 IEEE 27th International Conference on Advanced Information Networking and Applications
CoSDEO 2013: device-free radio-based recognition
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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Context sensing is an important part of building ubiquitous smart and assistive environments. It is the major data source for intention recognition and strategy generation systems. Device-free localization systems (DFL) join the efforts of non-instrumentation of users maintaining their privacy. In recent publications we propose an innovative approach utilizing a cluster of passive Radio Frequency Identification Transponders (pRFID) for device-free radio-based positioning. Due to the point that the RFID technology is typically not designed for that purpose we have to deal with certain drawbacks. A high number of transponders typically conclude in lower measurement frame rates while generating substantially more information for accurate positioning. To fix this tradeoff this work presents a transponder clustering approach based on inherent EPC protocol based bit masking, which allows us to calculate fast coarse grained localization results and increase the precision by time, so that the user is able to adjust between localization speed and accuracy. We made simulations and conducted experiments in an indoor room DFL scenario for validation.