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

Caption text

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