Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/72862
Title: Electric network frequency forensics
Authors: Fong, Zi Hao
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2017
Abstract: Hardware clocks are imperfect due to the variations in oscillators. The variations in oscillators may result in the phenomenon called clock drift. The variations in oscillators may cause the durations of time intervals of events to be different between nodes even though the expectation of the durations of time events of events between nodes is supposed to be the same. Therefore, time synchronisation is needed to overcome this imperfection of hardware clocks. Power grids have a transmission frequency of either 50 Hz or 60 Hz. The transmission frequency is normally referred as Electric Network Frequency (ENF) and the nominal value of ENF in Singapore is 50Hz. Powerline radiation and non–perfect AC to DC conversion allow ENF to be captured by electronic devices that have recording audio capabilities. Therefore, the purpose in this project is to verify if time synchronisation between two smart phones can be achieved with the help of power grid. The two smartphones are first placed in the same location and start the audio recording application. Tap sounds are introduced at the start and end of the recording to signify the start and end of audio recording. The audio recording process takes place for about 46 seconds. The two audio files are processed separately to remove the audio data before the first tap sound and the audio data after the second tap sound. The resulting audio files will then be run through the python code to produce the graph of filtered ENF in time domain. The two smartphones are then placed in the same location and start the audio recording application. The command to start audio recording for the two smartphones will then send from the two laptops. When the smartphones received the command, the smartphones will then execute the command to record audio for one minute. The lag time between the command received to start audio recording and executing the command to record audio will serve as the synchronisation to start audio recording. The audio data before the lag time will then be removed and the resulting audio data will then be passed through the Python code to produce the graph of filtered ENF in time domain. There is minimum difference between the two graphs of filtered ENF in time domain and thus, time synchronisation between two smart phones can be achieved with the help of power grid. The proposed recommendation would be to place the two smartphones in separate locations to verify if time synchronisation can still be achieved. The next proposed recommendation would be to determine if the second harmonics of Ac mains frequency can be used to verify the presence of electric network frequency instead.
URI: http://hdl.handle.net/10356/72862
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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