000			08375nam a22002657a 4500
008			201210s2024 a|||f bm|| 00| 0 eng d
024	7		_a0000-0003-3128-9559 _2ORCID
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng _bara
082			_a610
100	0		_aAhmed Maher Mohamed Hamed _93570
245	1		_aSmart Connectivity for Teleoperated Vehicles Using a Predictive LTE Multi-Operator Approach _c/Ahmed Maher Mohamed Hamed
260			_c2024
300			_a64p. _bill. _c21 cm.
500			_3Supervisor: Nashwa AbdelBaki
502			_aThesis (M.A.)—Nile University, Egypt, 2024 .
504			_a"Includes bibliographical references"
505	0		_aContents: 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
520	3		_aAbstract: 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
546			_aText in English, abstracts in English and Arabic
650		4	_ainformatics
655		7	_2NULIB _aDissertation, Academic _9187
690			_ainformatics
942			_2ddc _cTH
999			_c10898 _d10898