Editing Applications of Edge Computing (section)

==8.3 Challenges of Using Edge Computing with AUVs==

Section 8.1.3 of this chapter outlines the general challenges of implementing IoT in underwater environments—communication barriers, energy constraints, harsh environments, and computational limits. This section provides further analysis of these issues in the context of Edge Computing. It quantifies these challenges and explores solutions for these pproblems.

===8.3.1 Unreliable Communication===
Building on the communication limitations introduced in Section 8.1.3, acoustic signals face not only high latency but also severe bandwidth restrictions. Here, we compare alternative technologies (optical/RF) and their tradeoffs.

# '''Limited Bandwidth & High Latency'''
:: Different communication strategies face different problems underwater.
:: Acoustic Communication has extremely low bandwidth (1–50 kbps) and high propagation delay (~1.5 sec/km) because of the low speed of sound underwater. And so, transmitting a 10MB sonar scan could take approx. 30+ minutes. Comparatively, optical communication has high bandwidth (in Gbps), low range (<100m) for transmission and requires clear water. Radio frequencies have an even limited range (<10m) and can only work in shallow waters.           
# '''Intermittent & Unreliable Links'''
:: Apart from bandwidth and latency issues, there are also problems such as surface wave disruptions due to which communication buoys lose connectivity in rough seas and dynamic topology (caused by constantly moving AUVs) leading to frequent network reconfigurations.

===8.3.2 Energy Constraints===

AUVs operate on limited battery capacity (typically 1–10 kWh), making energy management critical. This subsection details optimization techniques like adaptive sampling and model compression for energy efficiency.

Transmitting 1MB of data acoustically consumes approx. 10–100 Joules, 100x more energy than local edge processing (0.1–1 Joule). 

'''Energy Optimization Strategies'''
* Adaptive Sampling: Edge AI triggers high-resolution sensors only when anomalies detected
* Model Compression: Quantized neural networks (e.g., TensorFlow Lite) reduce CPU usage by 4–10x
* Dynamic Voltage Scaling: Processors throttle performance based on mission phase

===8.3.3 Harsh Environments===
This section further builds upon the environmental challenges discussed in section 8.1.3. Edge Computing particularly amplifies these challenges due to thermal, mechanical, and operational constraints. 

* '''Thermal Management in AUVs'''
: Edge processors generate 5–50W of heat in sealed AUV hulls, creating thermal gradients that directly affect the lifespan of components.

{| class="wikitable" style="margin:auto"
|+ Comparative Analysis
|-
! Parameter !! Impact 
|-
| Heat Density || 10–100x higher than passive systems
|-
| Heat Dissipation Rate || 80% slower than air-cooled designs
|-
| Temperature Fluctuations || ±20°C during compute bursts
|}


:This can be handled using Phase Change Materials (PCM) such as paraffin wax to absorb the heat during heavy computation. One other common method of handling excess heat is through liquid cooling loop where a dielectric fluid circulates through titanium channels. There's also the method of Thermoelectric Harvesting which converts waste heat and powers low-energy sensors with it.
::> Case Study: WHOI’s Orpheus AUV limits hull temperatures to 55°C at 6,000m depth.

*'''Pressure-Induced Component Degradation'''
: High-density edge electronics face accelerated failure under extreme hydrostatic pressure (capacitors face capacitance loss, solder joints get microfractures, etc.). This can be prevented using oil-filled pressure cavities and using solid-state tantalum designs.

* '''Corrosion & Biofouling'''
: Unprotected copper circuits get corroded 50% faster in deep-sea as compared to the surface. To reduce this effect graphene coatings are used. And to counter biofouling, UV-LED antifouling with 5W arrays are used to inhibit microbial growth on sensors.

: Edge-enabled AUVs suffer severely from a phenomenon known as Galvanic Corrosion. Most AUVs contain unprotected multi-metal (copper, iron, nickel) circuit boards that get corroded 50% faster in deep-sea as compared to the surface. Another major problem is the formation of natural Microbial Fuel Cells (MFC) on electrical components caused by microorganisms. This can lead to electron transfers and short circuits. Seawater also gets trapped in dense component arrays and causes crevice corrosion. To tackle all of these problems, there are some advanced protection methods. 
:: 1. Graphene Nanocoatings reduce corrosion current by almost 89% (MIT Sea Grant trials) and add protective layers (<1μm in thickness) to PCBs.
:: 2. UV-LED Antifouling is a process where 5W 365nm arrays are used to inhibit microbial growth while consuming only 0.3% of the system’s energy budget. 
:: 3. Self-Healing Epoxy resins are used to extend component lifespans threefold in saline environments by releasing corrosion inhibitors when damage occurs.

Edge computing introduces critical thermal/mechanical challenges that require co-design of hardware and marine platforms. All of these techniques aim to enhance the durability of edge systems in harsh underwater conditions while maintaining operational efficiency. The optimal balance occurs at 2,000–4,000m depths using hybrid cooling strategies.

===8.3.4 Computational Constraints===
Beyond basic AUV capabilities, Edge computing requires unique processing limitations.
* '''Real-Time Latency Bottlenecks'''
: {| class="wikitable" style="margin:auto"
|-
! Task !! Max Allowable Latency !! Typical Edge Performance
|-
| Obstacle avoidance || 100ms || 50–80ms (Jetson AGX Orin)
|-
| Fault detection || 500ms || 200–300ms (FPGA solutions)
|}

* '''Memory Limitations'''
: Most AUV edge systems have 4–16GB RAM vs. cloud servers' 100+GB. This is handled by a technique known as Tensor Slicing where large models are processed in chunks or by using MRAM caches which needs almost 10x lower power than SRAM for frequent data access.

These constraints drive innovation in edge solutions, which we explore in Section 8.4 through state-of-the-art architectures like federated learning and hybrid systems.