@article{atzeni2022,

title = {A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques},

author = {Daniele Atzeni and Davide Bacciu and Daniele Mazzei and Giuseppe Prencipe},

url = {https://www.mdpi.com/1424-8220/22/13/4925},

doi = {10.3390/s22134925},

issn = {1424-8220},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Sensors},

volume = {22},

number = {13},

abstract = {Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nca2014,

title = {An experimental characterization of reservoir computing in ambient assisted living applications},

author = {Bacciu Davide and Barsocchi Paolo and Chessa Stefano and Gallicchio Claudio and Micheli Alessio},

url = {http://dx.doi.org/10.1007/s00521-013-1364-4, Publisher version

https://archive.ics.uci.edu/ml/datasets/Indoor+User+Movement+Prediction+from+RSS+data, Dataset @ UCI},

doi = {10.1007/s00521-013-1364-4},

issn = {0941-0643},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Neural Computing and Applications},

volume = {24},

number = {6},

pages = {1451-1464},

publisher = {Springer London},

abstract = {In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}