Editing Applications of Edge Computing (section)

===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:
# '''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.
#'''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. 
#'''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. 
#'''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.