Bandwidth reductions gains through Edge Computing in connected cars
(2018) EITM01 20182Department of Electrical and Information Technology
- Abstract
- Gathering and analyzing data that is generated in IoT and mobile devices is increasing
due to the huge potential value it brings to consumers and car manufacturers.
The increase in production of data introduces new problems in terms of
bandwidth requirements. Performing computations, filtering, and analyzing data
could be introduced to devices to reduce the bandwidth usage, this concept is
called Edge computing.
This Masters thesis has tackled the problem of bringing sensor based edge
computing to vehicles. The problem motivation raised two questions; How arrival
intensity of sensor data affects the system, and what bandwidth reductions gains
can be made. Firstly a pipeline was defined, and technologies and frameworks
were... (More) - Gathering and analyzing data that is generated in IoT and mobile devices is increasing
due to the huge potential value it brings to consumers and car manufacturers.
The increase in production of data introduces new problems in terms of
bandwidth requirements. Performing computations, filtering, and analyzing data
could be introduced to devices to reduce the bandwidth usage, this concept is
called Edge computing.
This Masters thesis has tackled the problem of bringing sensor based edge
computing to vehicles. The problem motivation raised two questions; How arrival
intensity of sensor data affects the system, and what bandwidth reductions gains
can be made. Firstly a pipeline was defined, and technologies and frameworks
were evaluated to be used in said pipeline in the method chapter.
The PoC was then tested in a real car, in order to prove that it works. It was
also used as a baseline for testing the two research questions posed. The PoC was
then tested in two rounds, in order to evaluate the different research questions.
The First research question was evaluated through having a static system and set
of data and varying the sample rate of data to simulate the arrival intensity. The
results show that the system had a linear relationship in terms of memory, cpu
usage to the arrival rate. For the specific hardware that the system was tested
showed that the system was stable up to a sample rate of 10000 Hz.
The main research question was tested with the results from the secondary
research question in mind. The sample rate was set to 100Hz and instead, the
agent scenarios were varied in order to evaluate what bandwidth reductions gains
can be made with three different edge computing levels. The results showed that
the bandwidth can be reduced to 0.01% of the original amount when sampling data
over 2 hours at 100 Hz. The scenarios had similar CPU usage despite increasing
the amount of edge computing done in the agents, which further showed that edge
computing is feasible in that car. However, it was also shown that the use case
which the agent is based upon dictates what bandwidth reductions gains can be
made. (Less) - Popular Abstract
- The car industry is rapidly evolving and including more technology
in its cars each generation. The technology in the cars is also becoming
smarter through the use of more sensors such as parking sensors
and cameras to aid in driving. In order to be competitive in the market
of tomorrow, one of the aspects that can set a car apart from the
competition, is a car that becomes smarter with age.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8963424
- author
- Jarfors, Mikael LU and Rosén, Axel LU
- supervisor
-
- Lars Larsson LU
- Maria Kihl LU
- organization
- course
- EITM01 20182
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Edge Computing, Connected cars, Message Queues, Distributed systems, Profiling
- report number
- LU/LTH-EIT 2018-674
- language
- English
- id
- 8963424
- date added to LUP
- 2018-11-27 08:09:23
- date last changed
- 2018-11-27 08:09:23
@misc{8963424, abstract = {{Gathering and analyzing data that is generated in IoT and mobile devices is increasing due to the huge potential value it brings to consumers and car manufacturers. The increase in production of data introduces new problems in terms of bandwidth requirements. Performing computations, filtering, and analyzing data could be introduced to devices to reduce the bandwidth usage, this concept is called Edge computing. This Masters thesis has tackled the problem of bringing sensor based edge computing to vehicles. The problem motivation raised two questions; How arrival intensity of sensor data affects the system, and what bandwidth reductions gains can be made. Firstly a pipeline was defined, and technologies and frameworks were evaluated to be used in said pipeline in the method chapter. The PoC was then tested in a real car, in order to prove that it works. It was also used as a baseline for testing the two research questions posed. The PoC was then tested in two rounds, in order to evaluate the different research questions. The First research question was evaluated through having a static system and set of data and varying the sample rate of data to simulate the arrival intensity. The results show that the system had a linear relationship in terms of memory, cpu usage to the arrival rate. For the specific hardware that the system was tested showed that the system was stable up to a sample rate of 10000 Hz. The main research question was tested with the results from the secondary research question in mind. The sample rate was set to 100Hz and instead, the agent scenarios were varied in order to evaluate what bandwidth reductions gains can be made with three different edge computing levels. The results showed that the bandwidth can be reduced to 0.01% of the original amount when sampling data over 2 hours at 100 Hz. The scenarios had similar CPU usage despite increasing the amount of edge computing done in the agents, which further showed that edge computing is feasible in that car. However, it was also shown that the use case which the agent is based upon dictates what bandwidth reductions gains can be made.}}, author = {{Jarfors, Mikael and Rosén, Axel}}, language = {{eng}}, note = {{Student Paper}}, title = {{Bandwidth reductions gains through Edge Computing in connected cars}}, year = {{2018}}, }