# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 4 Applications and Challenges of Blockchains in MIoT and IoUT
### Solutions for Blockchain-Enabled MIoT and IoUT Systems
The optimization techniques discussed below could complement these solutions and further enhance their scalability and efficiency, making them more suitable for practical deployment. With that said, recent advancements in optimization techniques for underwater acoustic communication networks (UACNs) present opportunities that, when integrated effectively with blockchain frameworks, could provide complementary solutions to further address these challenges and enhance the practicality of blockchain-enabled MIoT/IoUT systems. For example, [PERSON] et al. [52] proposed a distributed power allocation algorithm for UACNs based on Reinforcement Learning (RL), leveraging a Markov Decision Process (MDP) framework and a Q-learning-based algorithm. This approach allows transmitters to autonomously adjust their power levels, leading to improved energy efficiency as well as an enhanced channel capacity. These capabilities could complement blockchain-enabled systems by reducing the computational and energy overhead of consensus protocols. \begin{table}
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**Author(s)** & **Proposed Solution** & **Problem Addressed** & **Blockchain Platform** \\ \hline [PERSON] et al. [51] & Blockchain-based MMS for large-scale maritime monitoring & Improves data security, integrity, and scalability in large-scale maritime systems & Hyperledger Fabric with dockerized client and Raspberry Pi AS receiver \\ \hline [PERSON] et al. [23] & UAV-based wireless communication system for maritime IoT & Provides secure authentication and protects against intrusions such as DDoS attacks & Custom private blockchain with PoA\({}_{1}\) consensus \\ \hline [PERSON] et al. [25] & Blockchain framework integrating cloud and fog layers for IoUT systems & Improves scalability and privacy with a multi-layer architecture & Custom modelchain with a modified PoS in a cloud–fog architecture \\ \hline [PERSON] [26] & Blockchain combined with SDN and fog computing & Improves scalability, energy efficiency, and data security in IoUT & Custom modified PoW blockchain with SDN and fog nodes \\ \hline [PERSON] et al. [21] & BCTrust, a decentralized authentication framework to reduce energy consumption & Lowers energy consumption and improves transmission efficiency & Private Ethereum blockchain with permissioned nodes and smart contracts \\ \hline [PERSON] et al. [22] & Energy-efficient authentication for cohesive device migration & Reduces energy consumption by 74.63\%, enhances efficiency during device migration & Permission-less blockchain with consensus for secure operations \\ \hline [PERSON] et al. [24] & Blockchain integrated with federated learning for edge computing in MIoT nodes & Increases energy efficiency and secure transmission in data transmission in low-energy edge nodes & Custom blockchain with PoQ consensus \\ \hline [PERSON] [14] & Ethereum smart contracts with IPFS for secure and decentralized IoT data management & Ensures data integrity and transmission via IPFS & Ethereum blockchain encrypted storage in IPFS \\ \hline [PERSON] et al. [28] & Framework integrating blockchain, IPFS, AI, and onion routing for secure IoUT communication & Mitigates security threats in underwater vehicle communication & IPFS-based blockchain (unspecified platform) with verifying tokens and TTL \\ \hline [PERSON] et al. [50] & Blockchain-Based Privacy-Preserving Authentication and Malicious Node Detection Model & Secures IoUT networks by restricting unauthorized access and removing malicious nodes & Custom blockchain for anonymous hashed storage of node credentials \\ \hline \end{tabular}
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Table 5: Blockchain-based solutions in MIoT/IoUT. Similarly, [PERSON] et al. [53] introduced the AMUSE (Adaptive Modulation in Underwater Acoustic Networks) algorithm, an MAB-based (Multi-Armed Bandit) method designed to optimize communication in underwater networks by automatically adjusting how data are encoded (modulation technique) based on the quality of the connection at any given moment.