Enhanced OSM Connectivity Scenario Simulator

Project Structure

The original monolithic version3.py file has been refactored into multiple modules based on functionality:

Core Modules

1. models.py - Data Models

• OSMNode: Represents geographic nodes from OSM data
• OSMWay: Represents roads/paths in OSM data
• VehiclePath: Handles vehicle movement along paths with progress tracking

Key Functions:

• get_position_at_progress(): Calculate vehicle position along path
• get_direction_at_progress(): Calculate vehicle heading/direction

---

2. osm_parser.py - OSM File Parsing

• OSMParser: Static class for parsing OSM XML files

Key Functions:

• parse_osm_file(): Parse OSM XML and extract nodes/ways
• get_bounds(): Calculate geographic bounds
• convert_coordinates_to_canvas(): Convert lat/lon to canvas coordinates
• create_vehicle_paths(): Generate vehicle paths from road data
• create_sample_data(): Generate sample data for testing

---

3. simulation_logic.py - Simulation Engine

• SimulationEngine: Core simulation logic and calculations

Key Features:

• Application type assignment (File Transfer, IoT Sensing, Voice Call, etc.)
• Bandwidth requirement calculations (traditional and SemCom)
• Vehicle/carriage creation with requirements
• Connection assignment algorithm (priority-based)
• System metrics calculation (throughput, goodput, success ratio)

Key Methods:

• calculate_bandwidth_requirement(): Standard BW calculation
• calculate_bandwidth_requirement_semcom(): SemCom-aware BW calculation
• compute_semantic_comm_delays(): Model compression/compute trade-offs
• assign_connections(): Priority-based user-to-AP assignment
• calculate_metrics(): Compute performance metrics

---

4. visualization.py - Canvas Rendering

• CanvasRenderer: Handles all drawing operations

Key Features:

• OSM road network visualization with color-coded road types
• Vehicle rendering with directional arrows
• Train carriage rendering
• Base station and satellite visualization
• Connection line drawing
• Legend creation
• Utilization-based color coding

Key Methods:

• draw_osm_roads(): Render road network
• draw_road_vehicle(): Draw vehicle with direction
• draw_base_station(): Draw BS tower with utilization
• draw_satellite(): Draw satellite with solar panels
• draw_legend(): Create visualization legend

---

5. metrics_logger.py - Metrics and CSV Logging

• MetricsLogger: Handles metrics tracking and CSV output

Key Features:

• CSV file management with configurable test numbers
• Metric formatting for display
• Structured logging with headers

Key Methods:

• log_metrics(): Append metrics to CSV file
• clear_csv(): Reset CSV file with header
• format_metrics_summary(): Format metrics for status display

---

6. ui_components.py - UI Builder

• UIBuilder: Constructs all UI elements

Key Features:

• Tabbed control interface (OSM, Simulation, Logging, SemCom, Map)
• Slider controls for parameters
• Requirement display tables
• Canvas setup with scrollbars

Key Methods:

• create_all_widgets(): Main UI construction
• _create_control_tabs(): Tabbed interface
• _create_requirements_tree(): User requirement displays
• _create_canvas_panel(): Simulation visualization area

---

7. main.py - Application Entry Point

• EnhancedScenarioSimulator: Main application class (extends tk.Tk)

Key Responsibilities:

• Application initialization and coordination
• State management (vehicles, base stations, satellites)
• Simulation loop control
• Integration of all modules

Key Methods:

• simulate_scenario(): Start simulation
• update_simulation(): Main simulation loop
• initialize_simulation_objects(): Create simulation entities
• redraw_canvas(): Update visualization
• update_requirement_displays(): Refresh UI tables

---

File Dependencies

main.py
├── models.py
├── osm_parser.py
│   └── models.py
├── simulation_logic.py
│   └── models.py
├── visualization.py
│   └── models.py
├── metrics_logger.py
└── ui_components.py
    └── visualization.py

Configuration Parameters

Semantic Communication (SemCom)

• bandwidth_mb_s: max 12.5 MB/s (≈100 Mbps)
• base_delay_s: 0.001 s (1 ms)
• compute_capacity_mb_s: 62,500 MB/s
• alpha: 0-1.0 (compression exponent)

Digital Twin (DT)

• Traditional DT: +(0-50ms) cloud delay
• Multilayer DT: No additional delay

Application Types

1. File Transfer: 50 MB, 10s latency, Priority 1
2. IoT Sensing: 0.01 MB, 500ms latency, Priority 1
3. Charging Scheduling: 0.5 MB, 1s latency, Priority 1
4. Voice Call: 0.1 MB, 150ms latency, Priority 2
5. Traffic Planning: 0.5 MB, 150ms latency, Priority 2
6. Autonomous Driving: 1.0 MB, 50ms latency, Priority 3

Running the Application

python main.py

Key Features

1. OSM Integration: Load real OpenStreetMap data or use sample data
2. Dynamic Simulation: Vehicles move along realistic road networks
3. Semantic Communication: Model compression vs. compute trade-offs
4. Priority-Based Scheduling: Higher priority users get preference
5. Real-Time Metrics: Track throughput, goodput, and success ratios
6. CSV Logging: Export metrics for analysis
7. Interactive Visualization: Pan, zoom, and observe simulation in real-time

Benefits of Modular Structure

1. Maintainability: Each module has a clear, focused purpose
2. Testability: Individual components can be tested in isolation
3. Reusability: Modules can be reused in other projects
4. Scalability: Easy to add new features without affecting existing code
5. Readability: Smaller files are easier to understand and navigate
6. Collaboration: Multiple developers can work on different modules simultaneously