With technological improvement such as ore exploration, robotics, and hydrodynamic lifting, deep-sea mining has attracted more attention from governments, companies, and scientific research institutions. Although its research and development has made great progress, there are still many obstacles in its industrial development, such as environmental pollution and sustainability development issues. This article analyses the research status of the sustainable development of deep-sea mining from an overall perspective. Through a literature review, this paper also discusses the application of the full life cycle assessment method to analyze environmental impact during the entire process of deep-sea mining ore application. Overall, this paper summarizes the research gaps that exist in the sustainable development of deep-sea mining, including the lack of sufficient quantitative research, environmental baseline data research, cumulative environmental impact assessment, resource recycling technology, and acceptable environmental impact range analysis. The significance of this article is to point out the most urgent problems to be solved in the research direction of the sustainable development of deep-sea mining in current academic circles. It has far-reaching potential to promote the industrialization process of the entire deep-sea mining industry.

Cascading failures are a critical factor affecting the reliability of industrial Internet of things (IIoT) systems. Establishing a realistic cascading failure model is of significant importance for researching and improving the reliability of IIoT. However, existing research on cascading failure modeling for IIoT lacks in-depth exploration of the actual characteristics of industrial scenarios, making it difficult to accurately characterize the cascading failure process in IIoT. In this work, based on the cyber–service coupling characteristic of IIoT systems, we establish a realistic interdependent network model, taking into full consideration the sensing-control data flow, the service community structure, and the diverse coupling patterns. On this basis, a cascading failure model for IIoT is developed, considering the routing-driven characteristic of the cyber network and the production–supply relationships among various manufacturing units in the service network. Extensive experiments are conducted to verify the rationality of the proposed model, and some meaningful findings are also obtained.

Unmanned aerial vehicle (UAV)-enabled Internet of things (IoT) systems are considered an effective solution for maritime environmental monitoring. However, in the maritime data collection scenarios, the coverage of maritime sensor network is significantly larger and changes over time. This makes existing UAV-enabled IoT solutions unable to meet the performance requirements of maritime environmental monitoring, especially in terms of data transmission latency. Therefore, we design a UAV-enabled maritime Internet of things (MIoT) data collection architecture with constrained aerial base stations. Based on this architecture, we propose a UAV-enabled MIoT collaborative data collection (UMCDC) scheme. In this scheme, we first introduce a distributed adaptive large neighborhood search algorithm to generate flight trajectories of UAVs. On this basis, we present a diffusion-based relay pairing method and a centripetal-based flight trajectory assignment method to establish relay pairing relationships between UAVs and assign flight trajectories to UAVs, respectively. Finally, a re-selection method is developed to extend the lifetime of the maritime sensor network by optimizing the hover points of UAVs and data collection nodes of subnets. Extensive experimental results have shown that the UMCDC scheme can achieve low data transmission latency and significantly extend the lifetime of the maritime sensor network.

Deep-sea mining is expected to be at commercial scale shortly. Accurate prediction of sliding wear rate is essential for estimating the lifespan of handling equipment in deep-sea mining. However, existing prediction methods rely on measured wear volume from experiments, which are time-consuming and challenging due to limited access to polymetallic nodule samples. This study establishes a Computational Fluid Dynamics (CFD)–Bonding Discrete Element Method (BDEM) coupled model (CFD-BDEM) to predict the wear rate of polymetallic nodules against four metals (Q235, 304 stainless steel, H62 brass, 6061-T6 aluminum alloy). Innovatively, debris generation during sliding wear was mimicked by the bonding fracture process in the CFD-BDEM model. Simulations under both dry and wet conditions were conducted. Wet wear simulations exhibited an oscillatory trend of wear rate, while dry wear simulations showed a linear increase. Furthermore, experiments showed that the BDEM model achieved a 9.83% prediction error under dry wear, and the CFD-BDEM model showed a 9.36% error under wet wear. Thus, the proposed models for wear rate prediction were verified. This study provides an efficient and reliable simulation tool for predicting wear rates between minerals and metal materials without extensive wear tests.