Applications of Edge Computing

8.1 Introduction to UIoT and AUVs

The Underwater Internet of Things (UIoT) is an extension of the Internet of Things (IoT) to the aquatic environments, enabling seamless data collection, communication, and automation beneath the ocean’s surface. It represents a transformative shift in marine technology. UIoT systems basically consist of interconnected smart devices—such as sensors, autonomous vehicles, and underwater drones, that monitor and interact with underwater ecosystems in real time. And unlike terrestrial IoT, which uses stable radio-frequency (RF) communication, UIoT systems operate in an environment where traditional wireless signals do not function, and energy efficiency is critical due to limited power sources.

8.1.1 Key components of UIoT

• Underwater Sensors to measure parameters like temperature, salinity, pressure, and pollution levels depending on the purpose of that particular UIoT device.
• Underwater Communication Networks: Use acoustic, optical, or hybrid signals to relay data.
• Edge Computing Nodes: Enable localized data processing to reduce reliance on distant cloud servers.
• Autonomous Underwater Vehicles (AUVs): Systems designed for marine exploration, mapping, environmental monitoring, and industrial inspections. AUVs are the most important components since they carry all of the other components.

8.1.2 Automated Underwater Vehicles (AUVs)

AUVs are unmanned, self-guided, programmable submersibles that can function without tethers or real-time human control. Considering the unpredictable nature of marine environments, AUVs are used for deep-sea missions where human presence is either impossible or impractical. Here are a few such scenarios:

• Scientific Research: Mapping seabeds, studying marine biodiversity, and tracking climate change effects.
• Industrial Inspections: Maintaining offshore oil rigs, pipelines, and fault detection in underwater cables.
• Defense & Security: Detecting and neutralizing underwater explosives, surveillance, and submarine reconnaissance.
• Disaster Response: Assessing tsunami or oil-spill damage and aiding search-and-rescue operations (particularly for crashed aircrafts).

Unlike remotely operated vehicles (ROVs), AUVs operate independently, leveraging onboard edge computing to process data without constant human intervention.

8.1.3 Unique Challenges in Underwater Environments

Surely, UIoT offers great potential. But it is equally problematic while deploying UIoT and AUVs. Here are the four fundamental challenges:

1. Limited & Unreliable Communication

Unlike terrestrial IoT which uses radio waves for communication, UIoT cannot use radio waves because the strength of radio waves decreases rapidly underwater. This makes RF signals nearly unusable beyond shallow depths and long-range communication is out of the question. Most UIoT systems tend to rely on acoustic or optical signals with limited bandwidth and high latency.

• Acoustic Signals are the most common method for underwater communication. However, acoustic signals have high latency (seconds to minutes) and low bandwidth (kbps range).
• Optical Signals offer relatively higher speeds but require clear water and precise alignment.

1. Severe Energy Constraints

Usually, AUVs run on batteries and majorly rely on limited onboard batteries. Moreover, battery replacement is impractical in deep-sea missions, making energy-efficiency a key constraint. 

1. Harsh Environmental Conditions

AUVs are constructed out of metals and other sensitive substances. Extreme pressure, corrosion and biofouling (effect of saltwater and marine life on machinery) degrade hardware reliability over time. This means AUV would require specialized hardware. 

1. Computational Limitations

Unlike cloud servers, AUVs have limited processing power. AUVs would need lightweight AI models for real-time decision-making tasks like obstacle avoidance, etc.

All of these challenges highlight the need for edge computing, which shifts data processing from distant clouds to local AUV systems, reducing latency and energy consumption while enabling real-time responsiveness.

8.2 Edge Computing for AUVs: Benefits and Necessity

8.2.1 Cloud Computing vs Edge Computing for Underwater Systems

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:

1. Dedicated edge processors (e.g., NVIDIA Jetson, Intel Movidius) installed within AUVs acting as onboard processing units.
2. Distributed Edge Nodes (underwater sensor hubs) that pre-process data before selective transmission.
3. 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.

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).

8.2.3 Applications of Edge Computing (Work In Progress)

Case Study 1: Real-Time Coral Reef Monitoring AUV equipped with edge AI processes 4K video feeds locally to: Identify coral bleaching patterns Count fish populations Detect invasive species Only statistical summaries transmitted weekly, extending operational duration from 8 to 72 hours

Case Study 2: Offshore Pipeline Inspection Edge-based acoustic signal processing: Immediate detection of leaks/pitting corrosion 3D modeling of structural integrity Prioritization of defects for maintenance crews

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.

1. 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.

1. 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

As discussed in section