IoV Task Offloading via D2D Edge Computing
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Solution Overview
Problem
Existing multi-access edge computing task offloading methods in IoV environments fail to minimize time delay and energy consumption while ensuring reliability, particularly in vehicle-to-vehicle (V2V) communications, due to high computational complexity and neglect of V2V time delay reliability.
Innovation Solution
A method for multi-access edge computing task offloading based on Device-to-Device (D2D) communication, which models an optimization problem to maximize the benefit of time delay and energy consumption, decomposes the problem into sub-optimization tasks for task offloading strategy, transmission power, and channel resource allocation, using linear programming and dynamic programming to determine optimal powers and resource allocation modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If task offloading strategy is optimized to minimize time delay and energy consumption, then system efficiency is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the optimization problem into two independent parts: task offloading strategy optimization and transmission power/channel resource allocation. By decomposing the mixed integer nonlinear programming problem into a linear programming problem for power allocation and a separate combinatorial optimization for task offloading, the computational complexity is significantly reduced while maintaining system efficiency improvements.
Solution Approach 2:
The patent changes the optimization approach by transforming the original mixed integer nonlinear programming problem into a linear programming problem through parameter substitutions and problem decomposition. This allows the use of efficient linear programming algorithms instead of complex iterative optimization methods, reducing computational complexity while achieving optimal or near-optimal solutions.
2Productivity
If V2I link optimization is focused on time delay and energy consumption minimization, then resource utilization is improved, but V2V time delay reliability is compromised
Solution Approach 1:
The patent creates a unified optimization framework that simultaneously handles both V2I and V2V communication requirements. The task offloading strategy considers both cellular users (CUE) and device-to-device users (DUE), with the optimization problem incorporating constraints for both types of communications. This multi-functional approach ensures that V2V time delay reliability is maintained while optimizing resource utilization across the entire system.
Solution Approach 2:
The patent incorporates reliability constraints as feedback mechanisms in the optimization problem. The time delay reliability requirement for D2D communications is explicitly included as a constraint in the linear programming problem, ensuring that the optimization solution maintains reliable V2V communication while achieving efficient resource allocation.
3Productivity
If DUE multiplexes CUE channels to improve spectrum efficiency, then bandwidth utilization is improved, but communication interference increases
Solution Approach 1:
The patent dynamically allocates channels and transmission powers based on real-time system conditions. The linear programming optimization determines optimal transmission powers for both CUE and DUE, and the channel allocation is dynamically adjusted to minimize interference while maximizing spectrum efficiency. This dynamic approach allows the system to adapt to changing conditions and maintain optimal performance.
Solution Approach 2:
The patent uses parameter optimization to balance bandwidth utilization and interference management. By optimizing transmission power levels and channel allocation parameters through linear programming, the system achieves high spectrum efficiency while controlling interference to acceptable levels. The optimization explicitly considers interference constraints in the power allocation problem.
Data Source
AI summary
The present disclosure discloses a method of multi-access edge computing task offloading based on D2D in IoV environment, which models a task offloading strategy, a transmission power and a channel resource allocation mode as a mixed integer nonlinear programming problem, wherein an optimization problem is to maximize the sum of time delay and energy consumption benefits of all CUE in cellular communication with a base station in the IoV system. This method has low time complexity, may effectively utilize channel resources of the IoV system, and ensures the delay reliability of the DUE for which the local V2V data exchange is performed in the form of D2D communication. In the meantime, the time delay and energy consumption of CUE are both close to the minimum, thus meeting the IoV requirements of low time delay and high reliability.