@article{
 title = {Natural Timestamps in Powerline Electromagnetic Radiation},
 type = {article},
 keywords = {clock,electric,iot,radiation,synchronization,time},
 volume = {14},
 websites = {https://doi.org/10.1145/3199676},
 month = {5},
 publisher = {ACM},
 id = {a905bbb9-5595-3525-9539-0cda0d458250},
 created = {2018-07-12T21:32:12.059Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:32:12.059Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {li:enf-time},
 source_type = {article},
 private_publication = {false},
 abstract = {The continuous fluctuation of electric network frequency (ENF) presents a fingerprint indicative of time, which we call natural timestamp. This article studies the time accuracy of these natural timestamps obtained from powerline electromagnetic radiation (EMR), which is mainly excited by powerline voltage oscillations at the rate of the ENF. However, since the EMR signal is often weak and noisy, extracting the ENF is challenging, especially on resource-limited sensor platforms. We design an efficient EMR conditioning algorithm and evaluate the time accuracy of EMR natural timestamps on two representative classes of IoT platforms—a high-end single-board computer with a customized EMR antenna and a low-end mote with a normal conductor wire acting as EMR antenna. Extensive measurements at six sites in a city, which are away from each other for up to 24km, show that the high-end and low-end nodes achieve median time errors of about 50ms and 150ms, respectively. To demonstrate the use of the EMR natural timestamps, we discuss three applications: time recovery, runtime clock verification, and secure clock synchronization.},
 bibtype = {article},
 author = {Li, Yang and Tan, Rui and Yau, David K Y},
 journal = {Transactions on Sensor Networks (TOSN)},
 number = {2}
}

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