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dc.contributor.author
Charisi, Vasiliki
en
dc.date.accessioned
2016-05-10T07:36:38Z
dc.date.available
2016-05-11T00:00:24Z
dc.date.issued
2016-05-10
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/14521
dc.rights
Default License
dc.subject
networks
en
dc.subject
opportunistic networks
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dc.subject
mobile nodes
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dc.title
Towards a marine opportunistic network across the Mediterranean Sea
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heal.type
masterThesis
el
heal.creatorID.dhareID
3301140010
heal.generalDescription
The dissertation’s motivation is to leverage the mobility of marine nodes across the sea to set
up an opportunistic network. The network could serve traffic with no strict delay
requirements and it would be helpful in the ever – expanding mobile networks, because there
are several regions in the sea, without cellular coverage and satellite communication is much
costlier.
en
heal.classification
Networks
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heal.keywordURI.LCSH
Wireless communication systems
heal.keywordURI.LCSH
Computer networks
heal.keywordURI.LCSH
Mobile communication systems
heal.language
en
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heal.access
free
el
heal.license
http://creativecommons.org/licenses/by-nc/4.0
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heal.references
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Boldrini, C., Conti, M. & Passarella, A., 2011. From pareto inter-contact times to residuals. IEEE Communications Letters, 15(11), pp.1256–1258.
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Buchsbaum, A.L. et al., 2006. Rectangular Layouts and Contact Graphs. , V(October), p.28. Available at: http://arxiv.org/abs/cs/0611107.
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Calcev, G. & Bonta, J., 2009. OFDMA Cellular Networks with Opportunistic Two-Hop Relays. EURASIP Journal on Wireless Communications and Networking, 2009(1), p.702659. Available at: http://jwcn.eurasipjournals.com/content/2009/1/702659 [Accessed August 25, 2015].
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Chaintreau, a et al., 2006. Impact of Human Mobility on the Performance of Opportunistic Forwarding Algorithms. Infocom, 6(6), pp.606–620.
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Groenevelt, R., Nain, P. & Koole, G., 2005. The message delay in mobile ad hoc networks. Performance Evaluation, 62(1-4), pp.210–228.
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Hay, D. & Giaccone, P., 2009. Optimal routing and scheduling for deterministic delay tolerant networks. 2009 Sixth International Conference on Wireless On-Demand Network Systems and Services.
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Huang, C.M., Lan, K.C. & Tsai, C.Z., 2008. A survey of opportunistic networks. Proceedings - International Conference on Advanced Information Networking and Applications, AINA, pp.1672–1677.
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Jain, S., Fall, K. & Patra, R. 2004. Routing in a delay tolerant network. SIGCOMM Comput. Commun. Rev. 34, 4.pp.145-158
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Karagiannis, T., Le Boudec, J.-Y. & Vojnović, M., 2007. Power law and exponential decay of inter contact times between mobile devices. Proceedings of the 13th annual ACM international conference on Mobile computing and networking MobiCom 07, (March), pp.183–194. Available at: http://dl.acm.org/citation.cfm?id=1287853.1287875
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Karaliopoulos, M. & Rohner, C. 2012. Trace-based performance analysis of opportunistic forwarding under imperfect node cooperation. Proceedings of the 31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012).
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Kostadinova, R. & Adam, C., Performance Analysis of the Epidemic Algorithms. , pp.1–5.
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Li, N., Hou, J.C. & Sha, L., 2005. Design and analysis of an MST-based topology control algorithm. IEEE Transactions on Wireless Communications, 4(3), pp.1195–1206. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1427709.
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Lindgren, A., Doria, A. & Schelén, O., 2003. Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review, 7(3), p.19.
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Passarella, A. & Conti, M., 2011. Characterising aggregate inter-contact times in heterogeneous opportunistic networks. Networking 2011, pp.301–313.
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Passarella, A. & Conti, M., 2013. Analysis of individual pair and aggregate intercontact times in heterogeneous opportunistic networks. IEEE Transactions on Mobile Computing, 12(12), pp.2483–2495.
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heal.fileFormat
pdf
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heal.recordProvider
School of Science and Technology, MSc in Information & Communication Technology Systems
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heal.publicationDate
2015-12-15
heal.bibliographicCitation
Charisi Vasiliki, Towards a marine opportunistic network across the Mediterranean Sea, School of Science and Technology, MSc in Information and Communication Technology Systems, International Hellenic University, 2015
en
heal.abstract
The broader Mediterranean Region is an area where maritime transportation is one of the
most important ways of communication. Daily movement of a large number of vessels is the
Mediterranean Sea creates the most densely populated marine routed in the world. The goal
of this dissertation is to study the feasibility of an opportunistic (aka Delay Tolerant) network
in the Mediterranean area that will be leveraging the (semi - deterministic) trajectories of
ferries, ships and boats across the sea. AIS data transmitted from vessels for maritime
purposes will be used for the network’s creation.
The dissertation’s motivation is to leverage the mobility of marine nodes across the sea to set
up an opportunistic network. The network could serve traffic with no strict delay
requirements and it would be helpful in the ever – expanding mobile networks, because there
are several regions in the sea, without cellular coverage and satellite communication is much
costlier.
The first chapter of the dissertation is an introduction to the basic idea proposed in it.
Background information about marine traffic and maritime signal transmission is given. It
also includes a brief introduction to the opportunistic networks, the dissertation’s objectives
and the methodology followed.
The second chapter is dedicated to the network characteristics and metrics that were used in
the simulation of the opportunistic network. Inter – contact times, contact graph and contact
duration are studied.
Chapter three includes the study of the AIS existing in the data set used. The definitions of
the used data, as well as the pre – processing followed before the development of the
application. There is also the algorithmic approach of the Matlab application, as well as the
analysis of some of the critical points of it.
Chapter four contains the network characterization results, as obtained from the application.
The results are taken from different scenarios for the time threshold and the distance. Graphs
visualize the results are also presented. Finally, chapter five contains the conclusions drown
and the further development toward a full network application.
en
heal.tableOfContents
Table of Contents
1.Introduction ........................................................................................................................... 1
1.1 Motivation – Background .................................................................................. 1
1.2 Opportunistic Networks: a Brief Introduction ................................................... 1
1.3 Objectives .......................................................................................................... 5
1.4 Methodology ...................................................................................................... 6
2. Opportunistic Networks ...................................................................................................... 7
2.1 Characterizing an Opportunistic Network ......................................................... 7
2.2 Inter – Contact Times......................................................................................... 7
2.2.1 Pair – Wise Inter – Contact Time ...................................................................... 8
2.2.2 Aggregate Inter – Contact Time ........................................................................ 8
2.3 Contact Graph .................................................................................................... 8
2.4 Contact Duration ................................................................................................ 9
2.5 Other Characteristics .......................................................................................... 9
3. Datasets and Μethodology ................................................................................................ 10
3.1 AIS Data........................................................................................................... 10
3.1.1 MMSI – Maritime Mobile Service Identity ..................................................... 11
3.1.2 Latitude ............................................................................................................ 11
3.1.3 Longitude ......................................................................................................... 12
3.1.4 SOG – Speed Over Ground.............................................................................. 12
3.1.5 COG – Course Over Ground............................................................................ 13
3.1.6 Timestamp........................................................................................................ 14
3.2 Pre – Processing of AIS Data Trace ............................................................... 15
3.2.1 Pre – Processing of Data .................................................................................. 15
3.2.2 Main Processing of Data .................................................................................. 16
3.2.2.1 Why Matlab? .................................................................................................... 16
3.2.3 Algorithmic Steps from AIS Data to Contact Traces ...................................... 16
3.3 Code Analysis .................................................................................................. 17
3.4 Baseline Scenario Definition ........................................................................... 20
4. Characterization Results ................................................................................................... 21
4.1 Scenarios Results ............................................................................................. 23
4.1.1 Baseline Scenario – dist=1 and thresh_t=1 ..................................................... 23
4.1.1.1 Day1 ................................................................................................................. 23
4.1.1.2 Day2, Day3, Day4 – Baseline Scenario ........................................................... 29
4.1.2 Scenario1 – dist=0.5 and thresh_t=1 .............................................................. 40
4.1.3 Scenario2 – dist=2 and thresh_t=1 ................................................................. 46
4.1.4 Scenario3 – dist=1 and thresh_t=2 ................................................................. 50
4.1.5 Scenario4 – dist=0.5 and thresh_t=2 .............................................................. 55
4.1.6 Scenario5 – dist=2 and thresh_t=2 ................................................................. 58
4.2 Distributions Comparison ................................................................................ 62
5. Epilogue .............................................................................................................................. 64
5.1 Conclusions ...................................................................................................... 64
5.2 Further Work .................................................................................................... 65
Appendix ................................................................................................................................. 66
References ............................................................................................................................... 72
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heal.advisorName
Karaliopoulos, Merkouris
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heal.committeeMemberName
Karaliopoulos, Merkouris
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heal.committeeMemberName
Gatzianas, Marios
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heal.committeeMemberName
Vassiliades, Nikolaos
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heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
el
heal.numberOfPages
84
el
heal.spatialCoverage
Mediterranean Sea
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heal.license.source-code
http://www.gnu.org/licenses/gpl-3.0.html
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