# 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
This provides both a secure and efficient method for handling IoUT data, ensuring the integrity of data transmission while maintaining the overall performance. The system also records the hash value of the encrypted data on the blockchain, ensuring tamper resistance. Thus, this solution effectively decentralizes both data storage and access control, with customizable smart contracts managing access roles and permissions. Expanding on security solutions, [PERSON] et al. [28] propose a framework that integrates multiple technologies--blockchains, the IPFS, AI, and onion routing--to tackle security threats in communication between underwater vehicles and control stations, such as insecure authentication and data integrity attacks. The IPFS-based blockchain ensures decentralized, tamper-proof data storage with verifying tokens and a time-to-live (TTL) feature for data validation in transit. AI classifiers identify malicious data, while onion routing securely transmits only legitimate information. Similarly, [PERSON] al. [50] present a Blockchain-Based Privacy-Preserving Authentication and Malicious Node Detection Model (BAM), which leverages blockchains to anonymously store the hashed credentials of sensor nodes during registration, enabling the system to quickly identify and remove any malicious nodes detected. In another approach, [PERSON] et al. [23] have demonstrated that integrating Unmanned Aerial Vehicles (UAVs) with blockchain technology can further secure wireless communications in maritime IoT systems. This approach prevents network interference and enhances the overall security by validating received packets through a fusion center (FC) (a centralized hub that collects and analyzes data from various sources) based on stored IDs on the blockchain. More specifically, it utilizes a private blockchain network and a Proof-of-Authority (PoA\({}_{1}\)) consensus algorithm, which also minimizes the computational overhead, making it suitable for environments with limited processing capabilities. While primarily focused on the MioT, this method could be adapted for IoUT applications, offering similar benefits in underwater settings. Similarly, [PERSON] et al. [51] developed a blockchain-based Maritime Monitoring System (MMS) using Hyperledger Fabric to ensure AIS data integrity, authenticity, and availability. Their prototype, which included a low-cost Automatic Identification System (AIS) receiver from the Brazilian Navy, processed 1,500 real entries. Utilizing technologies such as a dockerized client for scalable deployment, their results indicate that, while the blockchain implementation increased CPU and memory demands, the system remains a viable solution for secure, large-scale maritime monitoring. Finally, [PERSON] [26] proposed a modified PoW blockchain solution that combines software-defined networking (SDN), fog computing, and blockchain technology. In this model, fog nodes, which maintain a full copy of the blockchain and manage data storage, are located on the water's surface and communicate with underwater devices via acoustic signals, while radio communications connect them with onshore stations. The SDN controller optimizes routing and selects the most energy-efficient fog node for block mining, improving the overall sustainability while also strengthening data security. Table 5 provides an overview of these solutions, including the platforms and technologies they utilize. While these examples highlight significant approaches, they are not exhaustive but are instead indicative of the diverse methodologies and approaches currently being explored in blockchain applications for the IoUT and MIoT. From the analysis of Table 5, it is evident that while many blockchain-based solutions effectively address security and trust in MIoT and IoUT systems, the scalability and cost remain areas with room for ongoing refinement. These limitations can hinder the real-world adoption of such systems.