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A Platform for Adaptive Audio Sensing in IoT

Mariani, Marco
Master thesis
Åpne
18115_FULLTEXT.pdf (Låst)
18115_COVER.pdf (Låst)
Permanent lenke
http://hdl.handle.net/11250/2616171
Utgivelsesdato
2017
Metadata
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Samlinger
  • Institutt for informasjonssikkerhet og kommunikasjonsteknologi [1644]
Sammendrag
The Internet of Things (IoT) is the technology which connects Things

that surround us to the Internet. The interest in this technology has been

increasing along with its fields of interest in recent years. This project

focuses on checking the feasibility of a sensing audio system in the IoT

environment. We think that an audio sensing system composed of lots of

small and low-cost devices has potential in several scenarios such as dBA

noise measurement and room occupation. We implement a prototype of a

sensor node able to detect the human audible spectrum, which we use as

testbed. We selected hardware and software technologies in the way that

the sensor node can work in an IoT environment: low-energy consumption,

small footprint and wireless connection are the characteristics that the

sensor node must have.

The key feature of our sensing system is the use of the audio spectrum

analysis. In fact it allows us to work with audio chunks of tens up to

hundreds of milliseconds to be transmitted instead of streaming the audio

continuously, reducing the information to send and the active time of the

node. The resulting sensing pipeline consists of Detection, Sampling, Fast

Fourier Transform (FFT) and Transmission stages, continuously iterating

after an idle state period. The firmware for the Detection, Sampling

and FFT stages was developed. Random Access Memory (RAM) and

Read Only Memory (ROM) occupation, time occupation and power

consumption measurement were performed on different test cases and

sample lengths. The FFT stage can also be performed in a more powerful

node and the pros and cons were evaluated.

The measurement tests provided evidence on efficient RAM usage on

the testbed while simultaneously working with very small audio chunks.

However, this deteriorated when we performed the FFT on board because

it requested extra space on the RAM. From the gathered data from the

time occupation and power measurement tests, we developed an energy

model of our sensor node that helps to configure the energy profile of the

sensing system. Performing the FFT is a hallmark of our sensing system,

its performance on board is justified only if the data are needed locally or

for privacy-related reasons. We also suggest some proposals to improve

the FFT stage.
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