Future enhancements & topics

### 1. **Future Enhancements to Leave Out for Now**

These additions will make the paper stronger in later versions but are too complex to tackle right now. We’ll save them for future iterations once we’ve established the baseline.

#### 1. **Hold Off on Stochastic Latency Models (G/G/1)**
- **Why**: G/G/1 models allow for handling complex, unpredictable arrival rates, but we don’t need that level of detail yet.
- **Goal**: Mention this as a future enhancement for cases where system load is less predictable.

#### 2. **Skip Dynamic Reconfiguration for Now**
- **Why**: Adjusting settings based on the environment (like traffic conditions) adds a lot of complexity. Static configurations work fine for our current latency focus.
- **Goal**: Note this for future research when we expand into more adaptive, real-time adjustments.

#### 3. **Defer Reinforcement Learning (RL) for Failure Prevention**
- **Why**: RL is a great next step but requires complex training and real-time learning. LSTM is more manageable for now.
- **Goal**: Leave RL as a suggestion for future iterations when we’re ready for a self-learning system.

#### 4. **Don’t Include Network Latency or Offloading Yet**
- **Why**: Right now, we’re focusing on **on-vehicle** latency. Cloud or edge offloading complicates things with network delays, which aren’t central to our current goal.
- **Goal**: Indicate that network latency will become important as we expand to cloud processing.

#### 5. **Hold Off on Graceful Degradation Policies**
- **Why**: Graceful degradation keeps the system running under heavy load, but for now, we’re focused on **preventing** failures rather than managing them after they happen.
- **Goal**: Suggest this as a future area to explore once failure prediction is stabilized.

#### 6. **Defer Power/Resource Efficiency Trade-Offs**
- **Why**: While balancing power use and latency is important, it complicates the latency focus. Adding power concerns would divert from our main point.
- **Goal**: Leave this for future research, especially as we expand to power-constrained systems like electric vehicles.

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### Future Direction Summary

**Deferred Enhancements** more advanced models (stochastic latency, dynamic reconfiguration, RL), network latency, and resource optimization. These can wait until after we’ve fully established the basics.