Table 1 A state of art.
References | Network | Problem | Technique used | Resource Allocation | Task Splitting | Parameter Analysed | |||
|---|---|---|---|---|---|---|---|---|---|
Energy consumption | Latency | Energy Consumption | Latency | CPU Utilization | |||||
MECD2D |  | √ | Stochastic learning |  | √ |  | √ |  | |
MECD2D | √ | √ | Lyapunov optimization | √ |  | √ | √ |  | |
MECD2D |  | √ | Nash equilibrium |  | √ |  | √ |  | |
MECD2D | √ |  | Nash equilibrium |  | √ | √ |  |  | |
MECD2D | √ |  | Multi-objective bat algorithm |  | √ | √ |  |  | |
MECD2D |  | √ | Urgency-Value Based Transmission Scheduling | √ | √ |  | √ |  | |
MECD2D | √ | √ | Price elasticity log-log model |  | √ | √ | √ |  | |
MECD2D |  | √ | NOVA Algorithm | √ |  |  | √ |  | |
MECD2D |  | √ | Multiseller-Multibuyer double auction mechanism | √ | √ |  | √ |  | |
D2D | √ |  | Meta-heuristic algorithm | √ |  | √ | √ |  | |
D2D | √ |  | Particle swarm optimization | √ |  | √ |  |  | |
MECD2D | √ |  | Lyapunov optimization | √ |  | √ |  |  | |
MECD2D | √ | √ | Deep reinforcement learning | √ | √ |  | √ |  | |
Proposed | MECD2D | √ | √ | Q-Learning | √ | √ | √ | √ | √ |