A Lightweight Encryption Framework for V2I using AES and ECC

A Lightweight Encryption Framework for V2I using AES and ECC

Objective:

The primary aim of this research project is to design and implement a secure and resilient Vehicular Edge Computing (VEC) framework that ensures the confidentiality, integrity, and authenticity of data exchanged between vehicles and edge servers. The focus is on integrating lightweight encryption mechanisms that are suitable for resource-constrained environments, such as connected and autonomous vehicles (CAVs), to protect sensitive vehicular data from cyber threats. The framework will include adaptive task classification and offloading mechanisms that enable real-time responsiveness while safeguarding privacy, upholding ethical norms, and guaranteeing secure communication.

This project moves beyond traditional objectives like latency minimization and task optimization by placing security at the core of system design, aiming to establish a trustworthy data transmission and offloading environment within dynamic vehicular networks.
To address these challenges, the core objective of this project is to design and implement a secure, encryption-based framework for VEC environments. The proposed framework prioritizes data security and protection against cyber threats through the integration of lightweight encryption algorithms suited for latency-sensitive and resource-constrained vehicular environments. Techniques such as Elliptic Curve Cryptography (ECC) and Advanced Encryption Standard with Galois/Counter Mode (AES-GCM) will be explored for their ability to provide robust security with minimal computational overhead. These encryption methods ensure that only authorized entities can access and interpret vehicular data, while also maintaining system responsiveness.

Key Requirements

1.Task Classification Module

Classify tasks into:

• Critical Tasks (CT) – e.g., collision alerts, emergency braking
• High Priority Tasks (HPT) – e.g., navigation, traffic flow
• Low Priority Tasks (LPT) – e.g., infotainment
• This classification drives encryption strength and processing priority.

2.Lightweight Encryption System

Implement:

• AES-GCM for fast, authenticated encryption of data payloads
• Elliptic Curve Cryptography (ECC) for secure key exchange and authentication
• Optimize for low latency and minimal computational load (must be CAV-friendly).

3.Secure Offloading Architecture (TTORA-based)

Enhance the TTORA framework by integrating:

• Task classification rules
• Encryption modules
• Real-time decision-making (offload locally, to RSU, or to mobile edge)

4.Simulation & Testing Environment

Use MATLAB or Python to simulate:

• Different vehicle densities
• Encryption overhead
• Offloading success rate
• Security breach resistance

Performance Metrics To Track

• Encryption/Decryption Delay
• Latency in Task Execution
• Communication Overhead
• Task Success Rate
• System Resistance to Cyber Threats

Expected Deliverables

• Task classification model
• Working encryption module (ECC + AES-GCM)
• Secure TTORA-based simulation
• Performance analysis (vs. non-secure models)
• Ethical and legal risk assessment
• Final project report

Basically the prof wants :

1. How we will be evaulating stress test on it, parameters:

• Quickness
• Time Conversion (Time taken to complete the task)
• How secure it is
• What sorts of attacks it could prevent and why?

2. Evauate AES with factors like:

• How can we implement it?
• What is the effect on it?
• If we use AES this is where we reach 
• Confirming the constraints, even if the test result is nehgative thats fine, he wants to know that weve tested it.

For example:

If we say its a fast encryption method then thee should be proof to it.

3. ECC (same as to AES above)

4. Consider 3-4 scenarios or tasks and test it on them using MATLAB.