# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 2 Architectural Foundations of IoT, Blockchains, and MIoT/IoUT
### Architectures and Implementation of MIoT/IoUT Systems
This is because the system must adjust to different coverage areas, ranging from small regions to large, three-dimensional spaces, while also taking into account the mobility of its nodes, which can be either stationary or mobile [13]. The second process focuses on the real-time transmission of large volumes of underwater data, which is essential for maintaining effective communication within the IoUT. This process is mirrored in the \"Network\" layer, which manages bi-directional data packet handling between various endpoints, ensuring efficient data transmission via both wired and wireless links. This layer is tasked with translating protocols and maintaining data integrity as it flows between the \"Perception\" and \"Application\" layers, corresponding to the second step's emphasis on efficient data routing and timely delivery. The third and final step, which involves the intelligent processing of massive underwater data, is where the \"Application\" layer plays a crucial role. This layer handles the analysis, processing, and storage of collected data, offering a graphical user interface (GUI) that allows users to interact with the data and make decisions in real time. The \"Application\" layer's ability to process, track, store, and stream data directly supports [PERSON] and [PERSON] [16]'s emphasis on the need for advanced data processing capabilities to handle the increasing volumes of data generated by modern IoUT systems. While the models discussed above support the foundational communication and data management within IoUT systems, challenges such as increased data loads and network delays have emerged (as discussed in Section 4). To address these limitations and meet the growing demands of big data, the architecture of the MIoT/IoUT has evolved to incorporate edge computing [17; 24]. Initially introduced as a solution for traditional IoT systems, edge computing holds particular importance for IoUT systems due to the complexities of underwater communication. Unlike centralized cloud models, edge computing enables processing to occur locally within devices such as data acquisition equipment and mid-layer nodes. This decentralized or \"elastic computing\" approach helps mitigate inefficiencies inherent in centralized models while significantly lowering latency and reducing energy consumption, which are key considerations in energy-constrained underwater environments [37]. Recent advancements highlight the potential for improved performance in underwater networks, particularly in packet delivery and energy efficiency. One such example is the edge-IoUT architecture proposed by [PERSON] et al. [27], which integrates drones as edge devices within the IoUT architecture. To further optimize these networks, researchers suggest integrating edge computing with \"software-defined networking\", which enables interoperability across different network technologies. This combined approach is particularly effective in managing the vast amounts of data generated by marine vessels and ensuring that communication remains reliable and low-latency in complex ocean environments. Among other approaches, the next section will explore how the combination of edge computing and blockchains can lead to more efficient IoT networks.