Editing Applications of Edge Computing (section)

==8.2 Edge Computing for AUVs: Benefits and Necessity== 

===8.2.1 Cloud Computing vs Edge Computing for Underwater Systems===
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Traditional underwater systems use cloud-based architectures where AUVs collect data and transmit it to surface stations or shore-based servers for processing. But just like everything else that we have discussed in our course, this approach faces serious limitations, particularly in marine environments:
* '''High Latency:''' Acoustic signals (the primary underwater communication method as discussed in the previous section) face propagation delays of almost 1.5 seconds per kilometer, making real-time cloud processing impractical for time-sensitive operations like obstacle avoidance.
* '''Energy Inefficiency:''' Transmitting raw sensor data needs significantly more power (often 100-1000x more) than local processing, rapidly depleting AUV batteries.
*'''Intermittent Connectivity:''' Underwater channels suffer from frequent disruptions due to environmental factors like turbulence, marine life interference, and surface conditions.

Edge computing addresses these challenges by moving computation closer to the data source through:
# Dedicated edge processors (e.g., NVIDIA Jetson, Intel Movidius) installed within AUVs acting as onboard processing units.
# Distributed Edge Nodes (underwater sensor hubs) that pre-process data before selective transmission.
# Systems arranged in a hierarchical architecture that uses device-edge-surface computing

All of these strategies improve real-time decision-making capabilities, energy optimization and operational reliability.

{| class="wikitable" style="margin:auto"
|+ Comparative Analysis
|-
! Parameter !! Cloud Computing !! Edge Computing
|-
| Latency || 10s-100s of seconds || <100 milliseconds
|-
| Energy Consumption || High (continuous transmission) || Optimized (local processing)
|-
| Bandwidth Usage || Maximum (raw data) || Minimal (processed data)
|-
| Operational Continuity || Dependent on connectivity || Autonomous capability
|-
| Hardware Requirements || Simple AUV design || Advanced onboard compute
|-
| Security || Vulnerable in transit || Localized data processing
|}

===8.2.2 Emerging Edge Computing Paradigms for AUVs===
* Federated Edge Learning where multiple AUVs collaboratively train models without sharing raw data. This protects privacy while also improving collective intelligence.
* Edge-Cloud Hybrid Architectures which provide real-time processing at edge for critical tasks and long-term analytics and model refinement is done in the cloud. This method allows flexibility by distributing the workload based on connectivity.
* Neuromorphic Edge Processors: Chips that are inspired by the human brain that offer ultra-low-power AI at depth. This allows event-based processing for sensor inputs that aren’t as regular (for example: fault detecting sensors that are triggered only when there is a breach in a system).