Publications

@article{HUI2017358,

title = {Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies},

author = {Terence K-L-Hui and Simon R-Sherratt and Daniel Díaz-Sánchez},

url = {http://www.sciencedirect.com/science/article/pii/S0167739X16304721

/download/Major_requirements_for_building_Smart_Homes_in_Smart_Cities_based.pdf},

doi = {https://doi.org/10.1016/j.future.2016.10.026},

issn = {0167-739X},

year  = {2016},

date = {2016-11-01},

urldate = {2016-11-01},

journal = {Future Generation Computer Systems},

volume = {76},

pages = {358 - 369},

abstract = {The recent boom in the Internet of Things (IoT) will turn Smart Cities and Smart Homes (SH) from hype to reality. SH is the major building block for Smart Cities and have long been a dream for decades, hobbyists in the late 1970s made Home Automation (HA) possible when personal computers started invading home spaces. While SH can share most of the IoT technologies, there are unique characteristics that make SH special. From the result of a recent research survey on SH and IoT technologies, this paper defines the major requirements for building SH. Seven unique requirement recommendations are defined and classified according to the specific quality of the SH building blocks.},

keywords = {Ambient intelligence, Home Automation, Internet of Things, Smart Cities, Smart Home requirements},

pubstate = {published},

tppubtype = {article}

}

@article{campo009b,

title = {Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems},

author = {Alicia Rodriguez-Carrion and Carlos García-Rubio and Celeste Campo-Vázquez and Alberto Cortés and Estrella García-Lozano },

url = {http://hdl.handle.net/10016/27902},

doi = {https://doi.org/10.3390/s120607496},

issn = {1424-8220},

year  = {2012},

date = {2012-06-04},

urldate = {2012-06-04},

journal = {Sensors },

volume = {12},

issue = {6},

pages = {7496-7517},

abstract = {Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.},

keywords = {active lezi, Ambient intelligence, españavirtual, gsm-based location, lezi update, lz, prediction, recommender system, Ubiquitous computing},

pubstate = {published},

tppubtype = {article}

}