## 6 Discussion
### Potential Computational and Battery Impact of Proposed Framework  
Although this study focuses on the detection accuracy and robustness of the proposed LSTM-GA and Optimized LSTM-GA models, the real-world deployment of these architectures in UAVs may introduce unique challenges. By integrating GAs to explore an expanded hyperparameter space, including atypical layer structures, activation functions, or additional recurrent connections, there is potential for producing models with increased parameter counts and computational complexity. While these hyperparameters demonstrably enhance detection capabilities, they may also lead to greater inference overhead if deployed directly in embedded flight controllers or low-power edge devices.  
In a UAV context, such overhead has immediate implications for both _real-time responsiveness_ and _buttery life_. Higher-dimensional LSTM layers or more frequent recurrent connections can require additional floating-point operations. Depending on the onboard processor's capabilities, this extra load may risk exceeding tight latency budgets required for the near-instantaneous detection of spoofed signals, particularly critical in dynamic flight maneuvers. Moreover, increased processing translates into elevated power consumption, reducing overall flight duration or necessitating a larger power reserve, which can be impractical for lightweight UAVs where strict weight and size constraints apply.  
To mitigate these concerns, a number of techniques can be adapted to the final LSTM-GA models obtained from the genetic search:  
* **Model Pruning and Quantization:** After genetic optimization has identified an effective model architecture, pruning can systematically remove parameters with limited contribution to classification outcomes. Similarly, quantizing weights from floating-point to lower-precision representations (e.g., 16-bit or 8-bit integers) can substantially improve inference speed and reduce memory usage, with minimal degradation in detection accuracy [47].
* **Lightweight or Hybrid Architectures:** If the GA suggests more complex LSTM configurations, designers might adopt _hybrid_ solutions, such as using a smaller GRU or TCN variant onboard the UAV for initial filtering and offloading only ambiguous cases to a more powerful ground station or edge node [48]. This tiered approach reduces onboard computation while retaining the enhanced performance of the optimized model when needed.
* **Onboard Accelerators and AI Edge Platforms:** Modern embedded hardware--such as NVIDIA Jetson modules or specialized DSPs--can execute deep neural networks at a lower power than general-purpose CPUs [49]. Deploying the final LSTM-GA model on such accelerators could ensure the speed required for real-time anti-spoofing.  
Power-aware scheduling algorithms could further conserve battery by throttling computations when the UAV is in stable flight or free of anomalous events.
* **Partial or Batch-Based Inference:** In scenarios where continuous classification is not strictly required, engineers might opt for periodic sampling. This approach processes data in batched intervals, reducing the processor's active duty cycle and thus lowering total energy consumption.