Paper Citation Record · LEDGER

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

As of 15 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2502.08119.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.08119 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:26:15.751732Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:28.863978Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T17:00:40.927890Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy15
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e2cf21e4-c63f-4510-8225-aa7df20d29bd · outbound

This paper cites A survey on mobility of edge computing networks in IoT: State-of-the-art, architectures, and challenges,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles A survey on mobility of edge computing networks in IoT: State-of-the-art, architectures, and challenges,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.937118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea9c9ed2-e96b-48f9-886c-fb6f77d75f85 · outbound

This paper cites Cooperative cognitive dynamic system in UA V swarms: Reconfigurable mechanism and framework,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Cooperative cognitive dynamic system in UA V swarms: Reconfigurable mechanism and framework,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.927149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d93d1f99-592c-44f5-9143-c4a538db77bd · outbound

This paper cites Deep reinforcement learning based computation offloading and trajectory planning for multi-UA V cooperative target search,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Deep reinforcement learning based computation offloading and trajectory planning for multi-UA V cooperative target search,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.917590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 30ee42e6-7fd9-4d10-9693-850c024914ed · outbound

This paper cites Adaptive QoE-aware SFC orchestra- tion in UA V networks: A deep reinforcement learning approach,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Adaptive QoE-aware SFC orchestra- tion in UA V networks: A deep reinforcement learning approach,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.907018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c7215792-64fb-478e-a1ed-44c8096d9ade · outbound

This paper cites Online trajectory and resource optimization for stochastic UA V-enabled MEC systems,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Online trajectory and resource optimization for stochastic UA V-enabled MEC systems,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.897100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f1a166f2-acd5-4a4c-bc8b-14e90711699a · outbound

This paper cites NFV-enabled service recovery in space-air-ground integrated networks: A matching game based approach,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles NFV-enabled service recovery in space-air-ground integrated networks: A matching game based approach,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.886227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cb9f630c-bba8-4794-8ba9-f444c67cd9ea · outbound

This paper cites Collaborative multi-agent deep reinforce- ment learning for energy-efficient resource allocation in heterogeneous mobile edge computing networks,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Collaborative multi-agent deep reinforce- ment learning for energy-efficient resource allocation in heterogeneous mobile edge computing networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.877359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7dd78f79-4a0b-449e-87a7-2184a609a5a1 · outbound

This paper cites Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UA Vs cooperative formation system for maritime parallel search,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UA Vs cooperative formation system for maritime parallel search,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.868037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 975e2f8e-95ef-4d3d-8655-78deabecefc9 · outbound

This paper cites 3U: Joint design of UA V-USV-UUV networks for cooperative target hunting,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles 3U: Joint design of UA V-USV-UUV networks for cooperative target hunting,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.858898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ebabb454-1fda-4798-8076-f2052b26421a · outbound

This paper cites Beyond the edge: An advanced exploration of reinforcement learning for mobile edge com- puting, its applications, and future research trajectories,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Beyond the edge: An advanced exploration of reinforcement learning for mobile edge com- puting, its applications, and future research trajectories,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.847961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 568504cd-fcdf-46de-b47b-27b52610d9b9 · outbound

This paper cites Multi- cluster cooperative offloading for VR task: A MARL approach with graph embedding,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Multi- cluster cooperative offloading for VR task: A MARL approach with graph embedding,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.836601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e35b1ebb-f9e0-48a7-8990-da3aeeed7b89 · outbound

This paper cites Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T10:26:15.737552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cfcf8cc-4e5e-4b1a-8506-1fcfcb03b322 · outbound

This paper cites On the approximation of cooperative heterogeneous multi-agent reinforcement learning (MARL) using mean field control (MFC),.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles On the approximation of cooperative heterogeneous multi-agent reinforcement learning (MARL) using mean field control (MFC),

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.825774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 15a136ca-7e59-44cb-9648-b6265a486e6c · outbound

This paper cites Joint HAP access and LEO satellite backhaul in 6G: Matching game-based approaches,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Joint HAP access and LEO satellite backhaul in 6G: Matching game-based approaches,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.815073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 902ef5cb-2d2d-4424-a98a-60a56956dbdc · outbound

This paper cites Mobility models, traces and impact of mobility on opportunistic routing algorithms: A survey,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Mobility models, traces and impact of mobility on opportunistic routing algorithms: A survey,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.804403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 055d61f4-5e9d-4572-8875-4d452ad726bb · outbound

This paper cites Heterogeneous-agent reinforcement learning,.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Heterogeneous-agent reinforcement learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:15.792948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 2ac633fd-aebe-410f-accd-a941e81fefdf · inbound

A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming cites this paper.

A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

Reference 152

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:00:40.987834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.