Editing Applications of Edge Computing (section)

==8.4 State-of-the-Art Edge Computing Solutions==

===8.4.1 AI and Edge Processing===
As discussed in the previous sections, AUVs require specialized processing hardware that balances computational power, energy efficiency, and ruggedization for deep-sea environments. This section examines state-of-the-art approaches to implementing AI at the edge in underwater systems, covering hardware platforms, optimized algorithms, and emerging trends in the field.

* '''Hardware Solutions'''
# '''GPU-Powered Systems'''
:: Embedded GPU systems balance computational performance with power efficiency.
:: * NVIDIA Jetson AGX Orin (50-200 TOPS) for real-time sonar processing. It performs around 32-200 trillion operations per second (TOPS). In efficiency mode (15W), it can process sonar images at 5-10 frames per second (FPS).
:: * Qualcomm QCS6490 has integrated 5G capabilities. This enables AUVs operating near the surface to maintain communication while performing edge processing tasks.

# '''Field-Programmable Gate Arrays (FPGAs)'''
:: FPGAs can be reconfigured and can be easily adapted to different mission requirements. They combine AI engines with programmable logic, consuming only 8-15W while performing real-time object detection. 
:: * Use Case: The BlueROV2 platform uses Xilinx Versal ACAP for underwater infrastructure inspection with 95% defect detection accuracy. The Xilinx Versal ACAP runs low-power (8-15W) convolutional neural networks.
:: * Intel Cyclone V SoC is another such FPGA that consumes even less power (<5W). It is usually used for swarm AUV applications which require only basic edge processing. 
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* '''Software solutions: Optimized AI Models'''
# '''Model Optimization'''
:: There are several techniques to optimize factors such as memory usage, power consumption, and accuracy of AI models to fit the constraints of AUVs. Here are a few:

:: * Quantization, a technique that reduces numerical precision from 32-bit floating point to 8-bit integers. this leads to a 4x reduction in model size with minimal accuracy loss (<1%).
:: * Pruning is the process of removing redundant neural network connections. This eliminates unnecessary parameters while maintaining 95% performance again leading to a smaller model size.
:: * Knowledge Distillation is another powerful model size reduction technique where a large "teacher" model trains a smaller "student" model. The smaller model is then deployed reducing model complexity while preserving accuracy.

# ''' Model Architecture'''
:: Recent advances in neural network design have produced several architectures that can be particularly suited for underwater edge applications.

: Apart from model optimization and choosing the right architecture for specific use-cases, certain implementation techniques such as data pre-processing and selective transmission of processed results can be used improve the performance of these AI models. 

'''Case Study: MBARI's Benthic Observer'''

The Monterey Bay Aquarium Research Institute (MBARI) achieved a 70% reduction in AI power consumption by using some of the strategies discussed above. The Observer uses TensorRT-optimized models with dynamic voltage and frequency scaling and adaptive sampling strategies.

===8.4.2 Federated Learning===
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Federated Learning is an ML technique that allows collaborative AI model training across distributed AUV swarms without centralized data collection—a huge advantage for underwater environments where connectivity is limited and data privacy is essential. 

Federated Learning systems have a three-tier learning architecture.
* '''Device Level''': This is where local model training and energy-constrained optimization occurs. This level also implements some differential (adding noise to model updates to prevent data leakage) privacy mechanisms to protect data.
* '''Edge Aggregation Level''': This level combines model updates from multiple AUVs using federated averaging (FedAvg). It also flags suspicious model updates (eg, from malfunctioning AUVs).
* '''Global Updates''': This level retrains the global model with edge-aggregated data and marine scientists validate models before redeployment. Additionally, it implements Long-Term Learning where monthly retraining cycles incorporate new oceanographic data.

Federated Learning techniques are known to save energy to upto 42% when compared to centralized training, with 15-20% faster convergence speed and a lower accuracy loss.

'''Advantages'''
* Privacy Preservation: Raw data never leaves AUVs (crucial for military/ecological missions).
* Bandwidth Efficiency: Only model updates (not datasets) are transmitted.
* Adaptability: Models improve as more AUVs contribute.

===8.4.3 Other Emerging Technologies===

As edge computing evolves further, its incorporation into underwater systems creates opportunities for crazy potential. Several state-of-the-art technologies are starting to use edge processing to improve autonomy, efficiency, and flexibility in underwater environments, going beyond traditional AUV operations and data dumping. Here are a few of such advancements.

*'''Underwater Swarm Intelligence'''
: Swarm intelligence is basically multiple AUVs working collaboratively to complete complex tasks. Particularly considering underwater environments, edge computing helps by enabling:
:* Real-time data sharing among AUVs
:* Distributed decision-making
:* Adaptive mission planning

It helps with efficiency and robustness of underwater operations, particularly in dynamic environments.

*'''Underwater Augmented Reality (AR)'''
: Integrating AR with edge computing is one of the most interesting tech that is currently being built. It offers better visualization of underwater structures leading to improved navigation for AUVs and real-time overlay of sensor data. This kind of tech would have heavy applications in tasks such as underwater construction, maintenance, and archaeological exploration.

*'''Edge-Enabled Bio-Inspired Robotics'''
: Inspired by marine life such as fish, octopuses, and jellyfish, bio-inspired underwater robots are being developed to operate more efficiently in complex terrains. When combined with edge computing, these robots would be able to adapt motion patterns in real-time to navigate turbulent currents, perform distributed sensing using soft, flexible sensor arrays and even execute silent, low-energy operations to avoid disturbing marine life. This would promise the advantage of stealth, mimicking natural organisms and their movements avoids detection and the disruption of the natural ecosystem. These robots could be used for delicate ecological monitoring, covert surveillance, and search-and-rescue missions in fragile environments where traditional AUVs may be too disruptive.