Simulation of Smart Traffic Management System Using OMNeT++ and Veins for Real-World Traffic Challenges -- 4
Budget: £250 – £750 GBP
Main Goal of the Project:
The project focuses on simulating a smart traffic management system using OMNeT++ and Veins. The primary objective is to develop and implement multiple simulation scenarios that model how a smart city’s traffic system can manage real-world challenges, such as vehicle accidents, malfunctioning equipment, emergency routing, and optimized resource allocation. The system will also efficiently handle vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, and will use real-world traffic datasets to enhance the accuracy and realism of the simulations.
We aim to evaluate the system’s performance in terms of latency, resource utilization, and energy consumption, using algorithms for resource management and data processing, such as ALTAIC (or alternative algorithms) and K-Nearest Neighbors (KNN) for outlier detection. This project is intended for personal development and exploration of simulation technologies.
What the Freelancer Needs to Do:
The freelancer will be responsible for the implementation of these simulations, broken down into five key scenarios, ranging from simple to complex, while incorporating real-world traffic datasets where applicable:
Scenario 1: Basic Traffic Management
A simple vehicle-to-infrastructure (V2I) communication model, where vehicles send data to roadside units (RSUs) and receive traffic updates. The freelancer needs to implement this basic setup and ensure proper data flow between the vehicles and infrastructure.
Scenario 2: Malfunctioning Equipment (e.g., Broken Traffic Camera)
The system should be able to detect and adapt to a malfunction, such as a broken traffic camera. In this case, RSUs and edge servers should rely on alternative sources (e.g., data from nearby vehicles or infrastructure) to maintain traffic management.
Scenario 3: Vehicle Accident and Emergency Response
Simulate a traffic accident, where the system reroutes traffic around the accident site and prioritizes emergency vehicles (ambulance/fire trucks) by controlling traffic lights and rerouting nearby vehicles.
Scenario 4: Resource Allocation and Task Scheduling
The freelancer should simulate the ALTAIC algorithm (or a similar algorithm) to optimize resource allocation between vehicles and edge servers, ensuring tasks are handled efficiently.
Scenario 5: Outlier Detection (e.g., Abnormal Vehicle Data)
Using the K-Nearest Neighbors (KNN) algorithm (or a similar method), the system should detect vehicles that are transmitting abnormal data, isolating faulty or malicious data from the system.
The scenarios can get modified later according to the needs of this project.
Key Deliverables:
Step-by-step implementation of each scenario, with detailed documentation and explanation of the process and results.
Incorporation of real-world datasets to improve the realism and accuracy of the simulations.
Suggestions for alternative algorithms, where appropriate, for resource allocation and outlier detection.
Performance evaluation for each scenario, including metrics such as latency, resource utilization, and energy consumption.
The freelancer must deliver the project one scenario at a time, ensuring that each scenario is completed, reviewed, and confirmed before moving to the next.
The project focuses on simulating a smart traffic management system using OMNeT++ and Veins. The primary objective is to develop and implement multiple simulation scenarios that model how a smart city’s traffic system can manage real-world challenges, such as vehicle accidents, malfunctioning equipment, emergency routing, and optimized resource allocation. The system will also efficiently handle vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, and will use real-world traffic datasets to enhance the accuracy and realism of the simulations.
We aim to evaluate the system’s performance in terms of latency, resource utilization, and energy consumption, using algorithms for resource management and data processing, such as ALTAIC (or alternative algorithms) and K-Nearest Neighbors (KNN) for outlier detection. This project is intended for personal development and exploration of simulation technologies.
What the Freelancer Needs to Do:
The freelancer will be responsible for the implementation of these simulations, broken down into five key scenarios, ranging from simple to complex, while incorporating real-world traffic datasets where applicable:
Scenario 1: Basic Traffic Management
A simple vehicle-to-infrastructure (V2I) communication model, where vehicles send data to roadside units (RSUs) and receive traffic updates. The freelancer needs to implement this basic setup and ensure proper data flow between the vehicles and infrastructure.
Scenario 2: Malfunctioning Equipment (e.g., Broken Traffic Camera)
The system should be able to detect and adapt to a malfunction, such as a broken traffic camera. In this case, RSUs and edge servers should rely on alternative sources (e.g., data from nearby vehicles or infrastructure) to maintain traffic management.
Scenario 3: Vehicle Accident and Emergency Response
Simulate a traffic accident, where the system reroutes traffic around the accident site and prioritizes emergency vehicles (ambulance/fire trucks) by controlling traffic lights and rerouting nearby vehicles.
Scenario 4: Resource Allocation and Task Scheduling
The freelancer should simulate the ALTAIC algorithm (or a similar algorithm) to optimize resource allocation between vehicles and edge servers, ensuring tasks are handled efficiently.
Scenario 5: Outlier Detection (e.g., Abnormal Vehicle Data)
Using the K-Nearest Neighbors (KNN) algorithm (or a similar method), the system should detect vehicles that are transmitting abnormal data, isolating faulty or malicious data from the system.
The scenarios can get modified later according to the needs of this project.
Key Deliverables:
Step-by-step implementation of each scenario, with detailed documentation and explanation of the process and results.
Incorporation of real-world datasets to improve the realism and accuracy of the simulations.
Suggestions for alternative algorithms, where appropriate, for resource allocation and outlier detection.
Performance evaluation for each scenario, including metrics such as latency, resource utilization, and energy consumption.
The freelancer must deliver the project one scenario at a time, ensuring that each scenario is completed, reviewed, and confirmed before moving to the next.