This repository contains a Python implementation of the research paper titled "A Relay-Assisted Parallel Offloading Strategy for Multi-Source Tasks in Internet of Vehicles". This project was given as a course project in a team of 3 members. The paper proposes a novel approach to offloading computational tasks from vehicles (TaVs) to nearby Mobile Edge Computing (MEC) nodes, addressing challenges related to network bandwidth, task latency, and resource allocation.
- Network Bandwidth Pressure: Offloading tasks to MEC nodes alleviates pressure on network bandwidth.
- Task Latency: Reducing latency by processing tasks closer to the source.
- Resource Allocation: Ensuring fair allocation of resources among both stationary edge nodes (RSUs) and mobile edge nodes (vehicles).
- Relay Assistance: Using vehicles as relay nodes to assist in communication when edge nodes are out of range.
- Develop a 3-D road vehicle mobility model to predict vehicle movement and aid in designing the offloading strategy.
- Formulate an optimization problem to manage computing and communication resources effectively.
- Propose the RAPO (Relay-Assisted Parallel Offloading) strategy to minimize latency and efficiently handle multi-source tasks.
- System Models: A 3-D scenario involving RSUs, TaVs generating tasks, and relay-assisted nodes.
- Network Model: Detailed representation of RSUs, vehicles, and their roles as service nodes and relay-assisted nodes.
- Communication Model: Utilizes a flat Rayleigh fading channel for upload links.
- Computing Model: Includes local and edge computing resources.
You can access the research paper here