Regional high-resolution ocean environmental forecasting combines spatial numerical modeling with temporal prediction, and is essential for monitoring the ecological security of specific ocean regions. In recent years, deep learning methods are generally more computationally efficient than traditional numerical models and enable fast, accurate forecasting. However, as data resolution increases, the training and computational costs of existing approaches increase substantially. To address this issue, we introduce Slow-OCast, a transfer-learning based model designed for high-resolution ocean environmental forecasting. Specifically, Slow-OCast incorporates the slow-varying motion characteristics of the ocean and comprises two insightful modules. The Fluid Motion Separator that injects low-frequency background dynamics into the fine-tuning process of a foundation model, functioning as a "magnifier" to encode physical priors of ocean dynamics. The Hydrokinetic Energy Path Integrator that provides an implicit representation of flow-field evolution, serving as a "compass" to guide accurate change prediction. We evaluate Slow-OCast on two high-resolution Mediterranean datasets, and results demonstrate Slow-OCast consistently outperforms all baseline methods across forecasting tasks with different lead times.
Qixiu Li, Xiang Zhu, Xiaoyong Li et al.· Proceedings of the 32nd ACM...· 0 citations
As embodied intelligent agents, uncrewed aerial vehicles (UAVs) support low-altitude urban services, but their endurance is fundamentally constrained by limited onboard battery capacity. Existing solutions in dense urban environments incur high deployment costs, use coarse spatial layouts, and do not scale to large UAV fleets. We instead retrofit existing urban deployable infrastructure (UDI), such as traffic lights, street lamps, and communication base stations, as UAV docking points with charging capability. This UDI-based approach raises two coupled challenges: city-scale docking-point deployment over massive, spatially heterogeneous candidates, and coordinated multi-UAV access under queueing delays and residual-energy safety constraints. We jointly model docking queues, load, and energy consumption, and formulate a multi-objective optimization balancing energy consumption and load. To address these NP-hard deployment and scheduling subproblems, we propose a hierarchical UDI-based docking-point deployment algorithm (HUDD) that generates a scalable docking layout, and a charging access coordination algorithm based on convex relaxation and iterative rounding (CRIR) that coordinates energy-feasible, congestion-aware access for multiple UAVs on the obtained layout. Simulations on realistic urban datasets show that HUDD-CRIR outperforms baseline schemes in terms of energy consumption, response delay, queueing delay, and load distribution.
Wei Yang, Jiajie Xu, Jie Chen et al.· IEEE Transactions on Cogniti...· 0 citations
The global rollout of 5G networks has spurred the rapid deployments of edge servers for hosting latency-sensitive web applications, which improves quality of experience (QoE). However, current efforts fall short in the substantial energy costs associated with the 24/7 operation of edge servers and overlook user privacy by requiring accurate user information for service provision, eroding the sustainability of multi-access edge computing (MEC). To enhance the QoE and service performance while ensuring privacy in MEC, we systematically formulate the interaction among edge servers as a privacy-preserving experience-aware edge resource control (PEERC) problem. To address this, we conduct a global resource control and propose a collaborative resource allocation system named MERA. MERA leverages <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="xia-ieq1-3705464.gif"/></alternatives></inline-formula>-anonymity data obfuscation to protect user location and resource demand privacy while enhancing service performance and energy efficiency with mean-field multi-agent reinforcement learning. Extensive experiments based on a synthetic real-world dataset demonstrate that MERA significantly surpasses benchmarks in terms of QoE, user coverage, privacy, and energy efficiency by <inline-formula><tex-math notation="LaTeX">$1.18\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>18</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-3705464.gif"/></alternatives></inline-formula>, <inline-formula><tex-math notation="LaTeX">$1.24\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>24</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq3-3705464.gif"/></alternatives></inline-formula>, <inline-formula><tex-math notation="LaTeX">$1.63\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>63</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq4-3705464.gif"/></alternatives></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$1.27\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>27</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq5-3705464.gif"/></alternatives></inline-formula> on average.
Ziqi Wang, Xiaoyu Xia, Ibrahim Khalil et al.· IEEE Transactions on Knowled...· 0 citations