TY  - BOOK
AU  - Ahmed Maher Mohamed Hamed
TI  - Smart Connectivity for Teleoperated Vehicles Using a Predictive LTE Multi-Operator Approach
U1  - 610 
PY  - 2024///
KW  - informatics
KW  - NULIB
KW  - Dissertation, Academic
N1  - Thesis (M.A.)—Nile University, Egypt, 2024; "Includes bibliographical references"; Contents:
DEDICATION................................................................................................................i
ACKNOWLEDGEMENTS...........................................................................................ii
TABLE OF CONTENTS..............................................................................................iv
LIST OF FIGURES ......................................................................................................vi
LIST OF TABLES.......................................................................................................vii
ACRONYMS..............................................................................................................viii
PUBLICATIONS..........................................................................................................xi
ABSTRACT................................................................................................................xiii
Chapter 1 Introduction ...................................................................................................1
1.1 Connectivity-Based Services in Business............................................................1
1.2 Teleoperated Services..........................................................................................2
1.3 Automotive Industry Assistive Driving ...............................................................4
1.4 Connectivity Service............................................................................................5
1.5 Connectivity Demand Growth .............................................................................6
1.6 The need behind Multi-Operator Connection ....................................................10
1.7 Thesis Objectives...............................................................................................11
1.8 Thesis Outline ....................................................................................................12
Chapter 2 Related Teleoperating Idea & Prediction Model Data ................................13
2.1 Teleoperative Supporting Devices.....................................................................13
2.2 Teleoperating with Cellular ...............................................................................18
2.3 Prediction Model and Correlation of the Environment Parameters...................21
Chapter 3 Accurate Data Collection for Modeling ......................................................25
3.1 Collecting Equipment ........................................................................................25
3.2 Selected Test Area Criteria ................................................................................25
3.3 Preforming Data Collection ...............................................................................27
Chapter 4 Feature Engineering for Improved Predection ............................................36
4.1 Data Cleansing & Labelling...............................................................................37
4.1.1 Labelling Level 1: Basic Suitability .......................................................................... 37
4.1.2 Labelling Level 2: Prioritization with Weighted Scores .......................................... 37
4.1.3 Tie-Breaking Mechanism ........................................................................................ 38
4.1.4 Addressing Unsuitable Data for Model Training .................................................... 39
4.1.5 Data Features: Building the Model's Foundation................................................... 40
4.2 Predictive Modelling..........................................................................................43
4.2.1 Dividing the Data: Training and Testing Sets.......................................................... 45
4.2.2 Addressing Data Imbalance: Ensuring a Fair Playing Field for Operators .............. 46
4.2.3 Evaluating the Impact of Balanced Data: Confusion Matrix and Optimal Choice.. 48
4.2.4 Best Model Results ................................................................................................. 51
Chapter 5 Conclusion and Future Work ......................................................................55
5.1 Conclusion .........................................................................................................55
5.1.1 Building Upon Existing Work: Comparing Approaches to Throughput Prediction 56
5.1.2 Model Selection and Verification ........................................................................... 56
5.1.3 Success Without Private Data................................................................................. 57
5.1.4 Moving Forward: Beyond Throughput ................................................................... 57
5.1.5 Road Sample Distribution and Localized Impact.................................................... 58
5.1.6 Impact on Model Performance............................................................................... 58
5.2 Future Work .......................................................................................................59
5.2.1 Enhancing Data Streams and Sensor Fusion .......................................................... 59
5.2.2 Investigating More Complex Models: Beyond Simple ANNs.................................. 60
5.2.3 Data Intensity Through Crowd-Sourced Collection................................................ 61
N2  - Abstract:
The demand for reliable connectivity-driven services is on the rise, leading to 
increased sensitivity in technologies like Advanced Driver-Assistance Systems 
(ADAS). ADAS represents a prevalent technological advancement in modern 
vehicles, with the aspiration of achieving trustworthy autonomous driving as the 
ultimate goal. From both end-user and manufacturer perspectives, we are assessing 
Teleoperated Driving as a promising feature to address emerging needs for traffic 
management and health and safety precautions. Human-to-human interaction and 
perception have been found to be superior to human-to-machine interaction in 
handling tasks, such as human driving compared to machine driving.
As this entire service relies on sensors which it is already implemented by various 
car manufacturers, and connectivity. It is obvious that the quality of the connectivity 
is expected to change in terms of coverage and capacity. We studied the feasibility 
of predicting the most suitable market operator within a specific area. This is based 
on previously established criteria, to serve as the primary data connection before 
engaging in a new measurement delay. To this end, an extensive measurement 
period was conducted. Establishing error margins in data collection and refining the 
data filtering process were meant to accurately capture the specifically targeted road 
conditions. These steps were completed prior to the commencement of our analysis 
of different models.
Smart Connectivity for Teleoperated 
Vehicles Using a Predictive LTE 
Multi-Operator Approach
Ahmed Maher Mohamed Hamed
xiv
These efforts have strengthened our study's focus on confirming the feasibility of 
seamless transitions between different operators, ensuring the fulfilment of the 
necessary teleoperation conditions. 
Keywords: Cellular networks, teleoperated vehicle, teleoperated driving, latency, 
throughput, 4G, LTE, predication models
ER  -