dc.contributor.advisor | Krämer, Frank Alexander | |
dc.contributor.author | Mariani, Marco | |
dc.date.accessioned | 2019-09-11T11:49:35Z | |
dc.date.created | 2017-10-27 | |
dc.date.issued | 2017 | |
dc.identifier | ntnudaim:18115 | |
dc.identifier.uri | http://hdl.handle.net/11250/2616171 | |
dc.description.abstract | 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. | en |
dc.language | eng | |
dc.publisher | NTNU | |
dc.subject | Telematics - Communication Networks and Networked Services (2 year), Tjenester og systemutvikling | en |
dc.title | A Platform for Adaptive Audio Sensing in IoT | en |
dc.type | Master thesis | en |
dc.source.pagenumber | 84 | |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi og elektroteknikk,Institutt for informasjonssikkerhet og kommunikasjonsteknologi | nb_NO |
dc.date.embargoenddate | 10000-01-01 | |