# 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  
While blockchain technology offers significant potential for tackling the complexities of MIoT and IoUT systems, it is evident that its integration is not straightforward. In order to fully leverage the potential of BIoT systems, it is essential to develop solutions that address existing challenges rather than amplifying them. Various innovative approaches have been proposed to specifically tackle these issues, paving the way for more effective and reliable BIoT implementations. Figure 7: Key challenges in MIoT and IoUT systems. To address scalability issues, [PERSON] et al. [25] proposed a blockchain framework designed for managing IoUT systems. This framework leverages a multi-layered sensor monitoring architecture that integrates both cloud and fog computing resources, enabling the secure and efficient management of IoUT data. The proposed solution operates on a cloud-based blockchain platform, employing a modified PoS consensus mechanism to handle transactions and data storage. This customized PoS method selects healthy validators using the TOPSIS method to ensure operation efficiency in resource-constrained environments. By employing a hierarchical structure and secure routing strategies, the framework effectively tackles scalability issues while enhancing the privacy and authenticity of data sources within IoUT networks. In this respect, [PERSON] et al. [20] also suggest that implementing lightweight consensus mechanisms or using high-power devices within the IoUT ecosystem can further alleviate the computational burden on those with limited capabilities. Moreover, [PERSON] et al. [24] have developed an edge computing framework that integrates blockchains with federated learning for marine systems within the MIoT. This framework uses a Proof-of-Quality (PoQ) consensus mechanism to improve efficiency and data reliability in edge computing operations, particularly for low-energy distributed nodes. The PoQ mechanism reduces the communication overhead by directing consensus messages only to committee nodes, thus optimizing energy use and ensuring the validity of training model updates. Additionally, their solution includes a smart selection process that assesses edge nodes based on their reputation and the quality of their data. This approach ensures that only the most reliable and high-performing nodes participate in the federated learning tasks, increasing the model's overall effectiveness. For energy efficiency, [PERSON] et al. [21] proposed a decentralized authentication method called \"BCTrust\" using blockchain technology, which significantly reduces energy consumption while enhancing data transmission rates and reducing end-to-end delays. Built on Ethereum, BCTrust includes an added layer that transforms it from a permissionless blockchain to a permissioned one, allowing for controlled access while maintaining the benefits of the original Ethereum network. This method provides a transparent, energy-efficient solution that is well suited for IoUT environments with limited resources. In a related approach, [PERSON] et al. [22] proposed an energy-efficient decentralized authentication mechanism designed explicitly for IoUT environments. Their solution utilizes a permission-less blockchain with a Proof-of-Authentication (PoA\({}_{2}\)) consensus mechanism, which enables secure data exchange while reducing energy consumption by up to 74.63% compared to traditional methods. Specifically, the reduction in energy consumption is achieved by eliminating the need for frequent re-authentication during device migration between clusters. This method not only reduces end-to-end delays by 41.9% but also increases the packet delivery rate by 21.6%. When it comes to data security and management, [PERSON] [14] presents a system that combines blockchain technology, specifically Ethereum smart contracts, alongside the IPFS. Their approach improves IoT data management by encrypting and storing the data within the IPFS's decentralized storage system.