@article{
 title = {Datasets for Mobile, Wearable and IOT Research},
 type = {article},
 year = {2017},
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 keywords = {dartmouth-cs,iot,measurement,mobile,wireless},
 pages = {5-7},
 volume = {20},
 websites = {http://www.cs.dartmouth.edu/~dfk/papers/rawassizadeh-datasets.pdf},
 month = {4},
 id = {7d33f238-769e-375e-8564-d26f709dd265},
 created = {2018-07-12T21:32:11.008Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:32:11.008Z},
 read = {false},
 starred = {false},
 authored = {false},
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 citation_key = {rawassizadeh:datasets},
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 abstract = {The advent of affordable devices with sensors and communication capabilities has led to the proliferation of computing paradigms, such as the Internet of Things (IoT), mobile devices, and wearable technologies. For the sake of simplicity, we use the umbrella term "small devices" for these technologies. At the same time, in the past decade, the increasing availability of large datasets has shifted scientists' attention toward data science, and defined new trends in computation. Even some scientists call it an evolutionary shift that has changed the pace of scientific progress, i.e., the "fourth paradigm".},
 bibtype = {article},
 author = {Rawassizadeh, Reza and Kotz, David},
 journal = {GetMobile: Mobile Computing and Communications},
 number = {4}
}

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