@inproceedings{Acer2015,

title = {Sensing WiFi Network for Personal IoT Analytics},

author = {Utku Gunay Acer and Aidan Boran and Claudio Forlivesi and Werner Liekens and Fernando Perez-cruz and Fahim Kawsar},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7356554},

doi = {10.1109/IOT.2015.7356554},

isbn = {978-1-4673-8056-0},

year  = {2015},

date = {2015-10-01},

booktitle = {2015 5th International Conference on the Internet of Things (IOT)},

pages = {104--111},

publisher = {IEEE},

address = {Seoul},

abstract = {We present the design, implementation and evaluation of an enabling platform for locating and querying physical objects using existing WiFi network. We propose the use of WiFi management probes as a data transport mechanism for physical objects that are tagged with WiFi-enabled accelerometers and are capable of determining their state-of-use based on motion signatures. A local WiFi gateway captures these probes emitted from the connected objects and stores them locally after annotating them with a coarse grained location estimate using a proximity ranging algorithm. External applications can query the aggregated views of state-of-use and location traces of connected objects through a cloud-based query server. We present the technical architecture and algorithms of the proposed platform together with a prototype personal object analytics application and assess the feasibility of our different design decisions. This work makes important contributions by demonstrating that it is possible to build a pure network-based IoT analytics platform with only location and motion signatures of connected objects, and that the WiFi network is the key enabler for the future IoT applications.},

keywords = {Accelerometers, cloud based query server, cloud computing, data transport mechanism, digital signatures, Distance measurement, Internet of Things, internetworking, IoT analytic, Logic gates, Mobile communication, motion signatures, network servers, Probes, proximity ranging algorithm, Search problems, telecommunication network management, WiFi gateway captures, WiFi management probes, WiFi network, wireless LAN},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Florentino-Liano2012a,

title = {Human Activity Recognition Using Inertial Sensors with Invariance to Sensor Orientation},

author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6232914},

isbn = {978-1-4673-1878-5},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},

pages = {1--6},

publisher = {IEEE},

address = {Baiona},

abstract = {This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a “virtual” sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.},

keywords = {Acceleration, Accelerometers, biomechanics, classification algorithm, Gyroscopes, Hidden Markov models, human daily activity recognition, inertial measurement unit, Legged locomotion, miniature inertial sensors, raw sensor signal classification, sensor orientation invariance, sensor orientation sensitivity, sensor placement, sensor position sensitivity, sensors, signal classification, signal transformation, Training, triaxial accelerometer, triaxial gyroscope, virtual sensor orientation},

pubstate = {published},

tppubtype = {inproceedings}

}