MARC details
| 000 -LEADER |
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| fixed length control field |
03889nam a2200265 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250513t2019 |||ad||g m||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
Universiti Teknologi Brunei |
| Language of cataloging |
eng |
| Transcribing agency |
UTB |
| 084 ## - BOOK Call Number |
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| Classification number |
UTB 120 REPORT THESIS & DISSERTATION |
| -- |
RTDS 265 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Dk. Siti Nur Amalina Binti Pg. Hj. Damit |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Machine Learning Based Sleep Interval Deciding Algorithm In Time Division Multiplexing Passive Optical Network (TDM-PON) / |
| Statement of responsibility, etc. |
Dk. Siti Nur Amalina Binti Pg. Hj. Damit |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
Brunei Darussalam : |
| Name of publisher, distributor, etc. |
Universiti Teknologi Brunei , |
| Date of publication, distribution, etc. |
© 2019. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 89 pages : |
| Other physical details |
coloured illustrations, charts, tables ; |
| Dimensions |
30 cm. |
| 500 ## - GENERAL NOTE |
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| General note |
The project report is submitted in fulfillment of the requirements for Master by Coursework in Information Security. |
| 500 ## - GENERAL NOTE |
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| General note |
Abstract<br/><br/>The study of Passive Optical Network (PON) standard as a next generation broadband optical access network in providing large capacity of bandwidth and saving energy consumption capability compared to other access technologies (such as Point-to-point), has always been a favorite subject to look onto. Since Information Communication Technology (ICT) is widely arise, Time-Division Multiplexing Passive Optical Network (TDM-PON) appears to be a promising technology that can meet users demand requirement. However, to keep the offer of ability to cope with the demand in TDM-PON, making further effort towards necessary approach is obligatory, and based on research found in academia and industry as well; there are still possible ways to make an enhancement towards energy efficiency.<br/><br/>Popular saving energy approach wherein already made by TDM-PON, is to tum Optical Network Unit (ONU) into sleep state when nothing to transmit or receive. However, the approach could arise risk in the case of letting ONU to switch into sleep state in a not at a right moment. Optical Line Transmission (OLT) will not transmit the data when ONU is inactive or sleep. Therefore, this can made OLT to suffer and unfortunately causing delay, which then deteriorate the Quality of System (QoS) that could contradict PON's benefit that PON originally bring in. Hence, the sleep intervals can give impact towards delay in traffic. Delay experienced by downlink's traffic and energy it consumes can cause TDM-PON with sleep-mode method to be born with a compromise issue due to tradeoff problem.<br/><br/>Concerning matter mentioned above, our goal is then established which is to save energy while also meeting network operator's access delay requirement. There are several studies related to this and we realize that most of existing work's solution utilize average value in order to forecast traffic arrival. Whereas here, our approach is to use machine learning and real traffic traces. We determine a suitable machine learning of Auto Regression Integrated Moving Average (ARIMA) model. We do also have evaluated our solution in real traffic case based on prediction, delay and energy saving performance in this thesis. It is to the best of our knowledge that our contribution by taking the concept of machine learning and digital twin based traffic arrival behavior as the first proposed solution towards trying to tackle the issue arise in achieving appropriate feasible sleep time in TDM-PON sleep state approach within this research domain. |
| 500 ## - GENERAL NOTE |
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| General note |
Thesis is also available in CD and is not for loan or reference use. |
| 610 #4 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Form subdivision |
Project Report |
| Corporate name or jurisdiction name as entry element |
Universiti Teknologi Brunei |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Machine learning |
| General subdivision |
Algorithms |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Passive optical networks |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Time division multiplexing |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Computer network protocols |
| General subdivision |
Energy conservation |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Titles and other words associated with a name |
Dr. |
| Relator term |
supervisor. |
| Personal name |
S. H. Shah Newaz |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Local Classification |
| Koha item type |
Reports, Thesis & Dissertation Students |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
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| Internal field |
Reports, Thesis & Dissertation |
| CC (RLIN) |
850433 : 002043 c. 1_UTB |
| Internal field |
Universiti Teknologi Brunei |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
|---|
| Internal field |
CD-ROM |
| CC (RLIN) |
850434 : CD No. RTDS CD 35 UTB |
