Resource-efficient Vehicle-to-Cloud communications leveraging machine learning:
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Düren
Shaker Verlag
2021
|
| Schriftenreihe: | Dortmunder Beiträge zu Kommunikationsnetzen und -systemen
Band 21 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 267 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 428 g |
| ISBN: | 9783844083569 3844083561 |
Internformat
MARC
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| 020 | |a 3844083561 |9 3-8440-8356-1 | ||
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| 100 | 1 | |a Sliwa, Benjamin |d 1988- |e Verfasser |0 (DE-588)1248947185 |4 aut | |
| 245 | 1 | 0 | |a Resource-efficient Vehicle-to-Cloud communications leveraging machine learning |c Benjamin Sliwa |
| 264 | 1 | |a Düren |b Shaker Verlag |c 2021 | |
| 300 | |a XVIII, 267 Seiten |b Illustrationen, Diagramme |c 21 cm x 14.8 cm, 428 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Dortmunder Beiträge zu Kommunikationsnetzen und -systemen |v Band 21 | |
| 502 | |b Dissertation |c Technische Universität Dortmund |d 2021 | ||
| 650 | 0 | 7 | |a Mobilfunk |0 (DE-588)4170280-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kommunikationssystem |0 (DE-588)4125542-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Car-to-Car-Kommunikation |0 (DE-588)1024622754 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenübertragung |0 (DE-588)4011150-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 653 | |a Vehicle-to-Cloud | ||
| 653 | |a Machine Learning | ||
| 653 | |a V2X | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
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| 689 | 1 | 2 | |a Datenübertragung |0 (DE-588)4011150-7 |D s |
| 689 | 1 | 3 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
| 689 | 1 | 4 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
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| 883 | 1 | |8 1\p |a vlb |d 20211125 |q DE-101 |u https://d-nb.info/provenance/plan#vlb | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033089893 | |
Datensatz im Suchindex
| _version_ | 1821795574282715136 |
|---|---|
| adam_text |
CONTENTS
ABSTRACT
111
KURZFASSUNG
V
LIST
OF
ACRONYMS
XI
1
INTRODUCTION
1
1.1
APPLICATIONS
AND
CHALLENGES
OF
MOBILE
AND
VEHICULAR
CROWDSENS
ING
IN
FUTURE
INTELLIGENT
TRANSPORTATION
SYSTEMS
.
1
1.1.1
MOBILE
AND
VEHICULAR
CROWDSENSING
APPLICATIONS
.
3
1.1.2
CONNECTED
VEHICLES
AND
VEHICLE-TO-CLOUD
COMMUNICATION
6
1.2
CHALLENGES
FOR
RESOURCE-EFFICIENT
VEHICLE-TO-CLOUD
COMMUNICATION
7
1.3
PROBLEM
STATEMENT
AND
REQUIREMENT
ANALYSIS
.
9
1.4
SCIENTIFIC
CONTRIBUTIONS
AND
RESEARCH
METHODOLOGY
.
11
1.5
STRUCTURE
OF
THE
THESIS
.
14
2
SELECTED
TECHNICAL
BASICS
19
2.1
CELLULAR
NETWORKING
WITH
LONG
TERM
EVOLUTION
.
19
2.1.1
RESOURCE
ALLOCATION
AND
ADAPTIVE
MODULATION
AND
CODING
20
2.1.2
CLIENT-BASED
DETERMINATION
OF
NETWORK
QUALITY
INDICATORS
22
2.1.3
COMMUNICATION-RELATED
POWER
CONSUMPTION
OF
MOBILE
DE
VICES
.
24
2.2
PROPERTIES
OF
RADIO
CHANNELS
AND
COMMUNICATION
PROTOCOLS
.
24
2.3
MACHINE
LEARNING
FOR
MOBILE
COMMUNICATION
SYSTEMS
.
26
2.3.1
SUPERVISED
LEARNING
FOR
REGRESSION
AND
CLASSIFICATION
.
.
26
2.3.2
UNSUPERVISED
LEARNING
FOR
DATA
CLUSTERING
.
33
2.3.3
REINFORCEMENT
LEARNING
FOR
AUTONOMOUS
DECISION
MAKING
33
2.3.4
OVERVIEW
OF
DATA
ANALYSIS
TOOLKITS
.
34
3
RELATED
WORK
37
3.1
ANTICIPATORY
MOBILE
NETWORKING
.
37
3.1.1
MACHINE
LEARNING-ENABLED
WIRELESS
NETWORKS
.
38
3.1.2
OPPORTUNISTIC
NETWORKING
AND
CONTEXT-AWARE
OPTIMIZATION
39
3.1.3
END-TO-END
DATA
RATE
PREDICTION
IN
CELLULAR
NETWORKS
.
41
3.1.4
END-EDGE
INTELLIGENCE
AND
RESOURCE-CONSTRAINED
EMBED
DED
LOT
SYSTEMS
.
44
VII
CONTENTS
3.2
METHODOLOGICAL
APPROACHES
FOR
MODELING
AND
PERFORMANCE
ANAL
YSIS
OF
VEHICULAR
COMMUNICATION
NETWORKS
.
46
4
MACHINE
LEARNING-BASED
DATA
RATE
PREDICTION
FOR
VEHICLE-TO-CLOUD
COMMUNICATION
51
4.1
INTRODUCTION
.
51
4.2
DATA
ACQUISITION
METHODOLOGY
OF
THE
REAL
WORLD
MEASUREMENT
CAMPAIGN
.
53
4.3
STATISTICAL
ANALYSIS:
DISTRIBUTION
OF
CONTEXT
INDICATORS
WITHIN
THE
EVALUATION
SCENARIOS
.
57
4.4
CORRELATION
ANALYSIS:
IMPACT
OF
THE
CONTEXT
INDICATORS
ON
THE
END-TO-END
DATA
RATE
.
62
4.5
MACHINE
LEARNING-BASED
DATA
RATE
PREDICTION
.
67
4.6
TOWARDS
PREDICTIVE
QOS:
FORECASTING
THE
END-TO-END
DATA
RATE
AT
FUTURE
LOCATIONS
ALONG
THE
VEHICULAR
TRAJECTORY
.
87
4.6.1
PREDICTION
OF
FUTURE
VEHICLE
LOCATIONS
.
88
4.6.2
MAINTAINING
NETWORK
CONTEXT
INFORMATION
WITH
RADIO
EN
VIRONMENTAL
MAPS
.
89
5
MACHINE
LEARNING-ENABLED
OPPORTUNISTIC
VEHICLE-TO-CLOUD
COMMUNI
CATION
101
5.1
INTRODUCTION
.
101
5.2
CONTEXT-AWARE
DATA
TRANSFER
METHODS
.
105
5.2.1
MACHINE
LEARNING
CAT
(ML-CAT)
.
107
5.2.2
REINFORCEMENT
LEARNING
CAT
(RL-CAT)
.
ILL
5.2.3
BLACK
SPOT-AWARE
CONTEXTUAL
BANDIT
(BS-CB)
.
117
5.3
METHODOLOGY
OF
THE
REAL
WORLD
PERFORMANCE
EVALUATION
.
125
5.4
REAL
WORLD
PERFORMANCE
OF
MACHINE
LEARNING-BASED
OPPORTUNIS
TIC
VEHICLE-TO-CLOUD
COMMUNICATION
.
128
5.5
TOWARDS
CONTEXT-PREDICTIVE
OPPORTUNISTIC
DATA
TRANSFER
.
134
5.5.1
CONTEXT-PREDICTIVE
VARIANTS
OF
THE
PROBABILISTIC
TRANSMIS
SION
SCHEMES
.
136
5.5.2
CONTEXT-PREDICTIVE
VARIANTS
OF
THE
REINFORCEMENT
LEARNING
BASED
TRANSMISSION
SCHEMES
.
138
5.5.3
REAL
WORLD
PERFORMANCE
EVALUATION
OF
CONTEXT-PREDICTIVE
DATA
TRANSFER
METHODS
.
138
6
DATA-DRIVEN
SIMULATION
AND
OPTIMIZATION
OF
ANTICIPATORY
VEHICULAR
COMMUNICATION
NETWORKS
143
6.1
INTRODUCTION
.
143
6.2
DATA-DRIVEN
SIMULATION
OF
END-TO-END
NETWORK
PERFORMANCE
IN
DICATORS
.
147
6.3
LIGHTWEIGHT
SIMULATION
OF
VEHICULAR
MOBILITY
WITH
LIMOSIM
.
.
.
151
VIII
CONTENTS
6.4
HYBRID
DATA-DRIVEN
AND
MODEL-BASED
VEHICULAR
NETWORK
SIMULA
TION
.
157
6.5
VALIDATION
METHODOLOGY
.
158
6.6
VALIDATION
AND
PERFORMANCE
EVALUATION
.
160
6.7
MODELING
ACCURACY
OF
DES
AND
DDNS-BASED
METHODS
.
160
6.7.1
COMPUTATIONAL
EFFICIENCY
OF
THE
SIMULATION
METHODS
.
.
.
164
6.7.2
LIMITATIONS
OF
DATA-DRIVEN
NETWORK
SIMULATION
.
165
7
LIGHTWEIGHT
MACHINE
LEARNING
FOR
END-EDGE
INTELLIGENCE-ENABLED
COM
MUNICATION
SYSTEMS
169
7.1
INTRODUCTION
.
169
7.2
CORE
CONCEPTS
AND
FUNCTIONS
OF
THE
LIMITS
FRAMEWORK
.
172
7.2.1
INTUITIVE
INTERFACES
FOR
HIGH-LEVEL
DATA
ANALYSIS
.
173
7.2.2
SUPPORTED
MACHINE
LEARNING
MODELS
.
174
7.2.3
OVERVIEW
ABOUT
AUTOMATION
FEATURES
.
175
7.2.4
IMPLEMENTATION
OF
ANN-BASED
ONLINE
LEARNING
.
179
7.3
CASE
STUDY:
PLATFORM-IN-THE-LOOP
MODEL
SELECTION
FOR
DATA
RATE
PREDICTION
WITH
END-EDGE
DEVICES
.
180
7.3.1
METHODOLOGY:
LOT
PLATFORMS,
DATA
SETS,
AND
POWER
CON
SUMPTION
ASSESSMENT
PROCEDURE
.
181
7.3.2
SWEET
SPOT
ANALYSIS:
MODEL
PERFORMANCE
AND
MEMORY
OC
CUPATION
.
183
7.3.3
TEMPORAL
EFFICIENCY
AND
POWER
CONSUMPTION
BEHAVIOR
OF
MACHINE
LEARNING
MODELS
.
188
7.4
VALIDATION
OF
THE
C/C++
CODE
GENERATOR
.
192
8
EVOLUTION
PERSPECTIVES
AND
TRANSFER
OPPORTUNITIES
197
8.1
TOWARDS
COOPERATIVE
DATA
RATE
PREDICTION
IN
FUTURE
NETWORKS
.
197
8.2
ONLINE
LEARNING
FOR
SELF
ADAPTATION
TO
CONCEPT
DRIFT
.
201
8.3
MINING
SIGNAL
STRENGTH
INFORMATION
FROM
GEOGRAPHICAL
DATA
.
.
203
8.4
PARROT:
PREDICTIVE
AD-HOC
ROUTING
FUELED
BY
REINFORCEMENT
LEARNING
AND
TRAJECTORY
KNOWLEDGE
.
205
8.5
MIGRATION
OF
THE
CONTRIBUTIONS
TO
FUTURE
NETWORK
GENERATIONS
.
208
8.6
ADDITIONAL
IMPULSES
FOR
FURTHER
RESEARCH
ACTIVITIES
.
210
9
CONCLUSION
215
BIBLIOGRAPHY
221
A
SCIENTIFIC
ACTIVITY
REPORT
257
B
ACKNOWLEDGMENTS
267
IX |
| adam_txt |
CONTENTS
ABSTRACT
111
KURZFASSUNG
V
LIST
OF
ACRONYMS
XI
1
INTRODUCTION
1
1.1
APPLICATIONS
AND
CHALLENGES
OF
MOBILE
AND
VEHICULAR
CROWDSENS
ING
IN
FUTURE
INTELLIGENT
TRANSPORTATION
SYSTEMS
.
1
1.1.1
MOBILE
AND
VEHICULAR
CROWDSENSING
APPLICATIONS
.
3
1.1.2
CONNECTED
VEHICLES
AND
VEHICLE-TO-CLOUD
COMMUNICATION
6
1.2
CHALLENGES
FOR
RESOURCE-EFFICIENT
VEHICLE-TO-CLOUD
COMMUNICATION
7
1.3
PROBLEM
STATEMENT
AND
REQUIREMENT
ANALYSIS
.
9
1.4
SCIENTIFIC
CONTRIBUTIONS
AND
RESEARCH
METHODOLOGY
.
11
1.5
STRUCTURE
OF
THE
THESIS
.
14
2
SELECTED
TECHNICAL
BASICS
19
2.1
CELLULAR
NETWORKING
WITH
LONG
TERM
EVOLUTION
.
19
2.1.1
RESOURCE
ALLOCATION
AND
ADAPTIVE
MODULATION
AND
CODING
20
2.1.2
CLIENT-BASED
DETERMINATION
OF
NETWORK
QUALITY
INDICATORS
22
2.1.3
COMMUNICATION-RELATED
POWER
CONSUMPTION
OF
MOBILE
DE
VICES
.
24
2.2
PROPERTIES
OF
RADIO
CHANNELS
AND
COMMUNICATION
PROTOCOLS
.
24
2.3
MACHINE
LEARNING
FOR
MOBILE
COMMUNICATION
SYSTEMS
.
26
2.3.1
SUPERVISED
LEARNING
FOR
REGRESSION
AND
CLASSIFICATION
.
.
26
2.3.2
UNSUPERVISED
LEARNING
FOR
DATA
CLUSTERING
.
33
2.3.3
REINFORCEMENT
LEARNING
FOR
AUTONOMOUS
DECISION
MAKING
33
2.3.4
OVERVIEW
OF
DATA
ANALYSIS
TOOLKITS
.
34
3
RELATED
WORK
37
3.1
ANTICIPATORY
MOBILE
NETWORKING
.
37
3.1.1
MACHINE
LEARNING-ENABLED
WIRELESS
NETWORKS
.
38
3.1.2
OPPORTUNISTIC
NETWORKING
AND
CONTEXT-AWARE
OPTIMIZATION
39
3.1.3
END-TO-END
DATA
RATE
PREDICTION
IN
CELLULAR
NETWORKS
.
41
3.1.4
END-EDGE
INTELLIGENCE
AND
RESOURCE-CONSTRAINED
EMBED
DED
LOT
SYSTEMS
.
44
VII
CONTENTS
3.2
METHODOLOGICAL
APPROACHES
FOR
MODELING
AND
PERFORMANCE
ANAL
YSIS
OF
VEHICULAR
COMMUNICATION
NETWORKS
.
46
4
MACHINE
LEARNING-BASED
DATA
RATE
PREDICTION
FOR
VEHICLE-TO-CLOUD
COMMUNICATION
51
4.1
INTRODUCTION
.
51
4.2
DATA
ACQUISITION
METHODOLOGY
OF
THE
REAL
WORLD
MEASUREMENT
CAMPAIGN
.
53
4.3
STATISTICAL
ANALYSIS:
DISTRIBUTION
OF
CONTEXT
INDICATORS
WITHIN
THE
EVALUATION
SCENARIOS
.
57
4.4
CORRELATION
ANALYSIS:
IMPACT
OF
THE
CONTEXT
INDICATORS
ON
THE
END-TO-END
DATA
RATE
.
62
4.5
MACHINE
LEARNING-BASED
DATA
RATE
PREDICTION
.
67
4.6
TOWARDS
PREDICTIVE
QOS:
FORECASTING
THE
END-TO-END
DATA
RATE
AT
FUTURE
LOCATIONS
ALONG
THE
VEHICULAR
TRAJECTORY
.
87
4.6.1
PREDICTION
OF
FUTURE
VEHICLE
LOCATIONS
.
88
4.6.2
MAINTAINING
NETWORK
CONTEXT
INFORMATION
WITH
RADIO
EN
VIRONMENTAL
MAPS
.
89
5
MACHINE
LEARNING-ENABLED
OPPORTUNISTIC
VEHICLE-TO-CLOUD
COMMUNI
CATION
101
5.1
INTRODUCTION
.
101
5.2
CONTEXT-AWARE
DATA
TRANSFER
METHODS
.
105
5.2.1
MACHINE
LEARNING
CAT
(ML-CAT)
.
107
5.2.2
REINFORCEMENT
LEARNING
CAT
(RL-CAT)
.
ILL
5.2.3
BLACK
SPOT-AWARE
CONTEXTUAL
BANDIT
(BS-CB)
.
117
5.3
METHODOLOGY
OF
THE
REAL
WORLD
PERFORMANCE
EVALUATION
.
125
5.4
REAL
WORLD
PERFORMANCE
OF
MACHINE
LEARNING-BASED
OPPORTUNIS
TIC
VEHICLE-TO-CLOUD
COMMUNICATION
.
128
5.5
TOWARDS
CONTEXT-PREDICTIVE
OPPORTUNISTIC
DATA
TRANSFER
.
134
5.5.1
CONTEXT-PREDICTIVE
VARIANTS
OF
THE
PROBABILISTIC
TRANSMIS
SION
SCHEMES
.
136
5.5.2
CONTEXT-PREDICTIVE
VARIANTS
OF
THE
REINFORCEMENT
LEARNING
BASED
TRANSMISSION
SCHEMES
.
138
5.5.3
REAL
WORLD
PERFORMANCE
EVALUATION
OF
CONTEXT-PREDICTIVE
DATA
TRANSFER
METHODS
.
138
6
DATA-DRIVEN
SIMULATION
AND
OPTIMIZATION
OF
ANTICIPATORY
VEHICULAR
COMMUNICATION
NETWORKS
143
6.1
INTRODUCTION
.
143
6.2
DATA-DRIVEN
SIMULATION
OF
END-TO-END
NETWORK
PERFORMANCE
IN
DICATORS
.
147
6.3
LIGHTWEIGHT
SIMULATION
OF
VEHICULAR
MOBILITY
WITH
LIMOSIM
.
.
.
151
VIII
CONTENTS
6.4
HYBRID
DATA-DRIVEN
AND
MODEL-BASED
VEHICULAR
NETWORK
SIMULA
TION
.
157
6.5
VALIDATION
METHODOLOGY
.
158
6.6
VALIDATION
AND
PERFORMANCE
EVALUATION
.
160
6.7
MODELING
ACCURACY
OF
DES
AND
DDNS-BASED
METHODS
.
160
6.7.1
COMPUTATIONAL
EFFICIENCY
OF
THE
SIMULATION
METHODS
.
.
.
164
6.7.2
LIMITATIONS
OF
DATA-DRIVEN
NETWORK
SIMULATION
.
165
7
LIGHTWEIGHT
MACHINE
LEARNING
FOR
END-EDGE
INTELLIGENCE-ENABLED
COM
MUNICATION
SYSTEMS
169
7.1
INTRODUCTION
.
169
7.2
CORE
CONCEPTS
AND
FUNCTIONS
OF
THE
LIMITS
FRAMEWORK
.
172
7.2.1
INTUITIVE
INTERFACES
FOR
HIGH-LEVEL
DATA
ANALYSIS
.
173
7.2.2
SUPPORTED
MACHINE
LEARNING
MODELS
.
174
7.2.3
OVERVIEW
ABOUT
AUTOMATION
FEATURES
.
175
7.2.4
IMPLEMENTATION
OF
ANN-BASED
ONLINE
LEARNING
.
179
7.3
CASE
STUDY:
PLATFORM-IN-THE-LOOP
MODEL
SELECTION
FOR
DATA
RATE
PREDICTION
WITH
END-EDGE
DEVICES
.
180
7.3.1
METHODOLOGY:
LOT
PLATFORMS,
DATA
SETS,
AND
POWER
CON
SUMPTION
ASSESSMENT
PROCEDURE
.
181
7.3.2
SWEET
SPOT
ANALYSIS:
MODEL
PERFORMANCE
AND
MEMORY
OC
CUPATION
.
183
7.3.3
TEMPORAL
EFFICIENCY
AND
POWER
CONSUMPTION
BEHAVIOR
OF
MACHINE
LEARNING
MODELS
.
188
7.4
VALIDATION
OF
THE
C/C++
CODE
GENERATOR
.
192
8
EVOLUTION
PERSPECTIVES
AND
TRANSFER
OPPORTUNITIES
197
8.1
TOWARDS
COOPERATIVE
DATA
RATE
PREDICTION
IN
FUTURE
NETWORKS
.
197
8.2
ONLINE
LEARNING
FOR
SELF
ADAPTATION
TO
CONCEPT
DRIFT
.
201
8.3
MINING
SIGNAL
STRENGTH
INFORMATION
FROM
GEOGRAPHICAL
DATA
.
.
203
8.4
PARROT:
PREDICTIVE
AD-HOC
ROUTING
FUELED
BY
REINFORCEMENT
LEARNING
AND
TRAJECTORY
KNOWLEDGE
.
205
8.5
MIGRATION
OF
THE
CONTRIBUTIONS
TO
FUTURE
NETWORK
GENERATIONS
.
208
8.6
ADDITIONAL
IMPULSES
FOR
FURTHER
RESEARCH
ACTIVITIES
.
210
9
CONCLUSION
215
BIBLIOGRAPHY
221
A
SCIENTIFIC
ACTIVITY
REPORT
257
B
ACKNOWLEDGMENTS
267
IX |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Sliwa, Benjamin 1988- |
| author_GND | (DE-588)1248947185 |
| author_facet | Sliwa, Benjamin 1988- |
| author_role | aut |
| author_sort | Sliwa, Benjamin 1988- |
| author_variant | b s bs |
| building | Verbundindex |
| bvnumber | BV047705998 |
| ctrlnum | (OCoLC)1298745936 (DE-599)DNB1246463814 |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV047705998 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:59:09Z |
| indexdate | 2025-01-20T19:00:48Z |
| institution | BVB |
| isbn | 9783844083569 3844083561 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033089893 |
| oclc_num | 1298745936 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | XVIII, 267 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 428 g |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Shaker Verlag |
| record_format | marc |
| series | Dortmunder Beiträge zu Kommunikationsnetzen und -systemen |
| series2 | Dortmunder Beiträge zu Kommunikationsnetzen und -systemen |
| spelling | Sliwa, Benjamin 1988- Verfasser (DE-588)1248947185 aut Resource-efficient Vehicle-to-Cloud communications leveraging machine learning Benjamin Sliwa Düren Shaker Verlag 2021 XVIII, 267 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 428 g txt rdacontent n rdamedia nc rdacarrier Dortmunder Beiträge zu Kommunikationsnetzen und -systemen Band 21 Dissertation Technische Universität Dortmund 2021 Mobilfunk (DE-588)4170280-3 gnd rswk-swf Kommunikationssystem (DE-588)4125542-2 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Car-to-Car-Kommunikation (DE-588)1024622754 gnd rswk-swf Datenübertragung (DE-588)4011150-7 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Vehicle-to-Cloud Machine Learning V2X (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Kommunikationssystem (DE-588)4125542-2 s DE-604 Car-to-Car-Kommunikation (DE-588)1024622754 s Mobilfunk (DE-588)4170280-3 s Datenübertragung (DE-588)4011150-7 s Datenanalyse (DE-588)4123037-1 s Dortmunder Beiträge zu Kommunikationsnetzen und -systemen Band 21 (DE-604)BV041418989 21 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033089893&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20211125 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Sliwa, Benjamin 1988- Resource-efficient Vehicle-to-Cloud communications leveraging machine learning Dortmunder Beiträge zu Kommunikationsnetzen und -systemen Mobilfunk (DE-588)4170280-3 gnd Kommunikationssystem (DE-588)4125542-2 gnd Datenanalyse (DE-588)4123037-1 gnd Car-to-Car-Kommunikation (DE-588)1024622754 gnd Datenübertragung (DE-588)4011150-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4170280-3 (DE-588)4125542-2 (DE-588)4123037-1 (DE-588)1024622754 (DE-588)4011150-7 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Resource-efficient Vehicle-to-Cloud communications leveraging machine learning |
| title_auth | Resource-efficient Vehicle-to-Cloud communications leveraging machine learning |
| title_exact_search | Resource-efficient Vehicle-to-Cloud communications leveraging machine learning |
| title_exact_search_txtP | Resource-efficient Vehicle-to-Cloud communications leveraging machine learning |
| title_full | Resource-efficient Vehicle-to-Cloud communications leveraging machine learning Benjamin Sliwa |
| title_fullStr | Resource-efficient Vehicle-to-Cloud communications leveraging machine learning Benjamin Sliwa |
| title_full_unstemmed | Resource-efficient Vehicle-to-Cloud communications leveraging machine learning Benjamin Sliwa |
| title_short | Resource-efficient Vehicle-to-Cloud communications leveraging machine learning |
| title_sort | resource efficient vehicle to cloud communications leveraging machine learning |
| topic | Mobilfunk (DE-588)4170280-3 gnd Kommunikationssystem (DE-588)4125542-2 gnd Datenanalyse (DE-588)4123037-1 gnd Car-to-Car-Kommunikation (DE-588)1024622754 gnd Datenübertragung (DE-588)4011150-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Mobilfunk Kommunikationssystem Datenanalyse Car-to-Car-Kommunikation Datenübertragung Maschinelles Lernen Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033089893&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV041418989 |
| work_keys_str_mv | AT sliwabenjamin resourceefficientvehicletocloudcommunicationsleveragingmachinelearning |