Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method | Paper Citation Record

Observation 8c90e1d0-e699-46b7-90ba-8ca654ee3baa · outbound

This paper cites Fair energy-efficient resource optimization for multi-UA V enabled Internet of Things,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Fair energy-efficient resource optimization for multi-UA V enabled Internet of Things,

Reference 1

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This paper cites Joint communication and trajectory optimization for multi-UA V enabled mobile Internet of Vehicles,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Joint communication and trajectory optimization for multi-UA V enabled mobile Internet of Vehicles,

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Observation 1cd5e635-78ed-49f7-a386-4eba3d65e23a · outbound

This paper cites Charging techniques for UA V-assisted data collection: Is laser power beaming the answer?.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Charging techniques for UA V-assisted data collection: Is laser power beaming the answer?

Reference 3

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This paper cites Reliable and energy-efficient UA V communications: A cost-aware perspective,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Reliable and energy-efficient UA V communications: A cost-aware perspective,

Reference 4

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This paper cites Age-optimal trajectory planning for UA V-assisted data collection,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Age-optimal trajectory planning for UA V-assisted data collection,

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This paper cites Energy- efficient trajectory optimization with wireless charging in UA V-assisted MEC based on multi-objective reinforcement learning,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Energy- efficient trajectory optimization with wireless charging in UA V-assisted MEC based on multi-objective reinforcement learning,

Reference 6

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Observation d5eb991c-c651-40a1-99e5-c416cea6602f · outbound

This paper cites Data collection in UA V- assisted wireless sensor networks powered by harvested energy,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Data collection in UA V- assisted wireless sensor networks powered by harvested energy,

Reference 7

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This paper cites Age-optimal data gathering and energy recharging of UA V in wireless sensor networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Age-optimal data gathering and energy recharging of UA V in wireless sensor networks,

Reference 8

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This paper cites URLLC-enabled by laser powered UA V relay: A quasi-optimal design of resource allocation, trajectory planning and energy harvesting,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method URLLC-enabled by laser powered UA V relay: A quasi-optimal design of resource allocation, trajectory planning and energy harvesting,

Reference 9

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Observation b1753fc0-fbc0-4dd2-9946-19ea046c0c60 · outbound

This paper cites Advancements in laser and LED-based optical wireless power transfer for IoT applications: A comprehensive review,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Advancements in laser and LED-based optical wireless power transfer for IoT applications: A comprehensive review,

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Observation 5e804294-7924-462b-b826-f0781544c7ea · outbound

This paper cites Aerial refueling: Scheduling wireless energy charging for UA V enabled data collection,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Aerial refueling: Scheduling wireless energy charging for UA V enabled data collection,

Reference 11

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Observation 8787ad22-88b8-4007-b946-4d681cdf75a3 · outbound

This paper cites Energy minimization for wireless communication with rotary-wing UA V,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Energy minimization for wireless communication with rotary-wing UA V,

Reference 12

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Observation d9fb0f4c-4093-412d-9e6f-4f9a03fe8c45 · outbound

This paper cites Load balance and trajectory design in multi-UA V aided large-scale wireless rechargeable networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Load balance and trajectory design in multi-UA V aided large-scale wireless rechargeable networks,

Reference 13

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Observation d244f073-ab88-4298-94ca-4b57ca16502a · outbound

This paper cites Optimal scheduling and deep reinforcement learning for multimodal charging system via unmanned aerial vehicles,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Optimal scheduling and deep reinforcement learning for multimodal charging system via unmanned aerial vehicles,

Reference 14

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Observation 44546fec-7a2f-4ffe-8e51-fa9d84e85580 · outbound

This paper cites AoI- minimal trajectory planning and data collection in UA V-assisted wire- less powered IoT networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI- minimal trajectory planning and data collection in UA V-assisted wire- less powered IoT networks,

Reference 15

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Observation 330c0b77-2d25-4408-9c5d-af384d66583f · outbound

This paper cites Deep rein- forcement learning for AoI minimization in UA V-aided data collection for WSN and IoT applications: A survey,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Deep rein- forcement learning for AoI minimization in UA V-aided data collection for WSN and IoT applications: A survey,

Reference 16

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Observation 846c7da4-1dfe-48ee-9529-c59d532d0b4d · outbound

This paper cites AoI-aware sensing scheduling and trajectory optimization for multi-UA V-assisted wireless backscatter networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI-aware sensing scheduling and trajectory optimization for multi-UA V-assisted wireless backscatter networks,

Reference 17

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Observation 0390e858-6cef-4e9f-8dbf-96584d594bc6 · outbound

This paper cites AoI, timely- throughput, and beyond: A theory of second-order wireless network optimization,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI, timely- throughput, and beyond: A theory of second-order wireless network optimization,

Reference 18

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Observation 45b2a5c7-eef3-41cd-b954-e7bbda477ac5 · outbound

This paper cites Convex optimization-based trajectory planning for quadrotors landing on aerial vehicle carriers,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Convex optimization-based trajectory planning for quadrotors landing on aerial vehicle carriers,

Reference 19

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Observation 357c42b3-7e5c-4cab-89f2-2355eb21d594 · outbound

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Interactive AI with retrieval-augmented generation for next generation networking,

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Observation 4f7e9ec8-a40e-4cc2-9e46-68cbce22d9f6 · outbound

This paper cites Generative AI for space-air-ground integrated networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Generative AI for space-air-ground integrated networks,

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This paper cites A survey on resource management in joint communication and computing- embedded SAGIN,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method A survey on resource management in joint communication and computing- embedded SAGIN,

Reference 22

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Observation 92362101-a40d-4375-8c6b-f83317e501bc · outbound

This paper cites Deep reinforcement learning for fresh data collection in UA V-assisted IoT networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Deep reinforcement learning for fresh data collection in UA V-assisted IoT networks,

Reference 23

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Observation faa44d12-5c6f-40e1-b7d0-806a3fe84a96 · outbound

This paper cites On-board deep Q-network for UA V-assisted online power transfer and data collection,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method On-board deep Q-network for UA V-assisted online power transfer and data collection,

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This paper cites Utility-oriented optimization for video streaming in UA V-aided MEC network: A DRL approach,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Utility-oriented optimization for video streaming in UA V-aided MEC network: A DRL approach,

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This paper cites DRL- driven optimization for energy efficiency and fairness in NOMA-UA V networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method DRL- driven optimization for energy efficiency and fairness in NOMA-UA V networks,

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This paper cites Applications of multi-agent reinforcement learning in future Internet: A comprehensive survey,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Applications of multi-agent reinforcement learning in future Internet: A comprehensive survey,

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Observation 7f2246eb-4314-439d-8edb-b7c7839ab204 · outbound

This paper cites Efficient task offloading strategy for energy-constrained edge computing environments: A hybrid optimization approach,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Efficient task offloading strategy for energy-constrained edge computing environments: A hybrid optimization approach,

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Observation a007537b-c83b-4f83-a5c7-3eb4681324c8 · outbound

This paper cites Laser-powered UA V trajectory and charging optimization for sustainable data-gathering in the Internet of Things,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Laser-powered UA V trajectory and charging optimization for sustainable data-gathering in the Internet of Things,

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Joint laser charging and DBS place- ment for drone-assisted edge computing,

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Laser-powered UA Vs for wireless communication coverage: A large-scale deployment strategy,

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Dynamic optical wireless power transfer for electric vehicles,

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Observation ccd9410e-8a66-4fef-b264-440c7fdd0a35 · outbound

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Privacy-aware laser wireless power transfer for aerial multi-access edge computing: A Colonel Blotto game approach,

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This paper cites Intelligent task offloading and energy allocation in the UA V-aided mobile edge-cloud continuum,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Intelligent task offloading and energy allocation in the UA V-aided mobile edge-cloud continuum,

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Dynamic trajectory design and bandwidth adjustment for energy-efficient UA V-assisted relaying with deep reinforcement learning in MEC IoT system,

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This paper cites Data collection in laser-powered UA V-assisted IoT networks: Phased scheme design based on improved clustering algorithm,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Data collection in laser-powered UA V-assisted IoT networks: Phased scheme design based on improved clustering algorithm,

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This paper cites Energy optimization of a laser-powered hovering-UA V relay in optical wireless backhaul,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Energy optimization of a laser-powered hovering-UA V relay in optical wireless backhaul,

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Observation 360cf543-3285-4457-8dfb-8d5b83479238 · outbound

This paper cites Laser charging enabled DBS placement for downlink communications,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Laser charging enabled DBS placement for downlink communications,

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Joint trajectory and charging power optimization for laser-charged UA V relaying networks,

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Green laser-powered UA V far- field wireless charging and data backhauling for a large-scale sensor network,

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This paper cites Operation optimization of laser-powered aerial data harvest- ing for passive IoT networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Operation optimization of laser-powered aerial data harvest- ing for passive IoT networks,

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Observation 9b5ee41e-d5aa-487d-849b-5708580a0a04 · outbound

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Resource allocation strategy for wireless powered communication networks with UA V- assisted edge computing,

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Air-ground coordinated MEC: Joint task, time allocation and trajectory design,

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This paper cites Laser-powered multi-UA V URLLC systems: Reliability and scheduling performance analysis,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Laser-powered multi-UA V URLLC systems: Reliability and scheduling performance analysis,

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method On the performance of laser-powered UA V-assisted SWIPT enabled multiuser communication network with hybrid NOMA,

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Observation a7c2faeb-5733-4ca5-b929-d1d40031866c · outbound

This paper cites Wireless powered metaverse: Joint task scheduling and trajectory design for multi-devices and multi-UA Vs,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Wireless powered metaverse: Joint task scheduling and trajectory design for multi-devices and multi-UA Vs,

Reference 46

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Joint optimization of 3D trajectory and scheduling for solar-powered UA V systems,

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Observation 39614289-6ae7-4e7b-982b-3a9ee2f3d583 · outbound

This paper cites Backscatter communication based sensor data collection using laser powered UA V,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Backscatter communication based sensor data collection using laser powered UA V,

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Observation 4b92d4cc-0340-44f0-8e0b-1bbdcb0b2af3 · outbound

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Dynamic charging and path planning for UA V-powered rechargeable WSNs using multi-agent deep reinforcement learning,

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Observation 835168d0-29ec-4af6-af88-1d4c7dcfea06 · outbound

This paper cites AoI minimization based on deep reinforcement learning and matching game for IoT information collection in SAGIN,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI minimization based on deep reinforcement learning and matching game for IoT information collection in SAGIN,

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Observation 3238aebc-297c-455b-b2f6-ff943b1c2391 · outbound

This paper cites UA V trajectory planning for AoI-minimal data collection in UA V-aided IoT networks by transformer,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method UA V trajectory planning for AoI-minimal data collection in UA V-aided IoT networks by transformer,

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This paper cites AoI oriented UA V trajectory planning in wireless powered IoT networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI oriented UA V trajectory planning in wireless powered IoT networks,

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Observation c28914e2-53eb-4ad2-8bc4-65a8c9b13613 · outbound

This paper cites Joint AoI-aware UA Vs trajectory planning and data collection in UA V-based IoT systems: A deep reinforcement learning approach,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Joint AoI-aware UA Vs trajectory planning and data collection in UA V-based IoT systems: A deep reinforcement learning approach,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T18:24:56.260735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03d1f391-e467-4be6-86f3-7da9cd35c86f · outbound

This paper cites AoI-aware energy efficiency resource allocation for integrated satellite-terrestrial IoT networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI-aware energy efficiency resource allocation for integrated satellite-terrestrial IoT networks,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T18:24:56.294109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e58d58d7-1c96-4719-a7ed-463187b8dafa · outbound

This paper cites Risk-aware and energy-efficient AoI optimization for multi-connectivity WNCS with short packet transmissions,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Risk-aware and energy-efficient AoI optimization for multi-connectivity WNCS with short packet transmissions,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:22.059302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f10f9dc-3bb7-43be-8e5e-a3feb88273c7 · outbound

This paper cites AoI and energy tradeoff for aerial-ground collaborative MEC: A multi- objective learning approach,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI and energy tradeoff for aerial-ground collaborative MEC: A multi- objective learning approach,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:22.019741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 07b9e2dd-111a-4624-9a5f-f4808714cf24 · outbound

This paper cites Efficient AoI- aware resource management in VLC-V2X networks via multi-agent RL mechanism,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Efficient AoI- aware resource management in VLC-V2X networks via multi-agent RL mechanism,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.989084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f46cd94-56ee-4339-bb7d-17850da881a0 · outbound

This paper cites AoI optimiza- tion in multi-source update network systems under stochastic energy harvesting model,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI optimiza- tion in multi-source update network systems under stochastic energy harvesting model,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.963286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 37e43be5-c019-4170-8641-a4be46ba5b29 · outbound

This paper cites Average AoI minimization with directional charging for wireless-powered network edge,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Average AoI minimization with directional charging for wireless-powered network edge,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.930823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02de602b-fc84-4347-8564-7cb4126e455d · outbound

This paper cites AoI- minimal clustering, transmission and trajectory co-design for UA V- assisted WPCNs,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI- minimal clustering, transmission and trajectory co-design for UA V- assisted WPCNs,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.907875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 488ce538-babe-4cbd-bd04-353e4afe8a2e · outbound

This paper cites Multitask transfer deep reinforcement learning for timely data collection in rechargeable- UA V-aided IoT networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Multitask transfer deep reinforcement learning for timely data collection in rechargeable- UA V-aided IoT networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.880140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b05d38be-0207-4035-a32b-f777370e5b30 · outbound

This paper cites AoI-energy tradeoff for data collection in UA V-assisted wireless networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI-energy tradeoff for data collection in UA V-assisted wireless networks,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.854248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b3ec9bed-1896-4a1d-bdd6-c01ec2adbcb0 · outbound

This paper cites Safe DQN-based AoI-minimal task offloading for UA V-aided edge computing system,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Safe DQN-based AoI-minimal task offloading for UA V-aided edge computing system,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.829945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec27374f-02bb-4aef-8661-b768d8f27d9c · outbound

This paper cites AoI- aware resource allocation with interference avoidance for ultra-dense industrial Internet of Things networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI- aware resource allocation with interference avoidance for ultra-dense industrial Internet of Things networks,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.805103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c71dafed-c593-4e1a-be83-b96110b6bf9c · outbound

This paper cites An AoI-aware data transmission algorithm in blockchain-based intelligent healthcare systems,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method An AoI-aware data transmission algorithm in blockchain-based intelligent healthcare systems,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.772528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 56dcf9ba-61bf-442d-b5ca-be3755cc97bb · outbound

This paper cites AoI-aware interference mitigation for task- oriented multicasting in multi-cell NOMA networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI-aware interference mitigation for task- oriented multicasting in multi-cell NOMA networks,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.748585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 939dc75a-962f-452e-84ba-d8aed5a66b8b · outbound

This paper cites AoI- guaranteed bandit: Information gathering over unreliable channels,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI- guaranteed bandit: Information gathering over unreliable channels,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.719641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51240e82-2bdc-4306-a9f6-2493ec7331a4 · outbound

This paper cites AoI-aware waveform design for cooperative joint radar-communications systems with online prediction of radar target property,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI-aware waveform design for cooperative joint radar-communications systems with online prediction of radar target property,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.698530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7ddd585b-ba86-4bdd-b4dd-ff6bf7a1058f · outbound

This paper cites Deep-reinforcement-learning-based AoI-aware resource allocation for RIS-aided IoV networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Deep-reinforcement-learning-based AoI-aware resource allocation for RIS-aided IoV networks,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.674834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bb0e6359-2ad5-445d-9215-ce6eac1e5609 · outbound

This paper cites Minimizing AoI in high-speed railway mobile networks: DQN-based methods,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Minimizing AoI in high-speed railway mobile networks: DQN-based methods,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.654157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 908294a8-bc49-421e-9b08-fece83cac3ad · outbound

This paper cites Enhancing the safety of autonomous driving systems via AoI-optimized task scheduling,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Enhancing the safety of autonomous driving systems via AoI-optimized task scheduling,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.634258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f736af5-df62-4110-a828-03ffd5fba089 · outbound

This paper cites Distributed real-time control for minimizing AoI in random access networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Distributed real-time control for minimizing AoI in random access networks,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.609739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4b803378-fa89-4731-bc72-429eaad0f92f · outbound

This paper cites Velocity-aware statistical analysis of peak AoI for ground and aerial users,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Velocity-aware statistical analysis of peak AoI for ground and aerial users,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.581858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c5aa7c84-901c-47e0-9d78-2cea08eb347f · outbound

This paper cites AoI- aware energy-efficient SFC in UA V-aided smart agriculture using asynchronous federated learning,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI- aware energy-efficient SFC in UA V-aided smart agriculture using asynchronous federated learning,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.554307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 68f6c78b-f0b8-4c2d-9a50-11977f6b5850 · outbound

This paper cites AoI-minimal task assignment and trajectory optimization in multi-UA V-assisted IoT networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI-minimal task assignment and trajectory optimization in multi-UA V-assisted IoT networks,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.529300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6718d547-3568-4074-988b-1f29db79a4fa · outbound

This paper cites UA V-enabled inspection system with no-fly zones: DRL-based joint mobile nest scheduling and UA V trajectory design,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method UA V-enabled inspection system with no-fly zones: DRL-based joint mobile nest scheduling and UA V trajectory design,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.500300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation add21824-c60c-4140-8674-92be9a3d7af0 · outbound

This paper cites Evolution- ary state estimation-based multi-strategy jellyfish search algorithm for multi-UA V cooperative path planning,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Evolution- ary state estimation-based multi-strategy jellyfish search algorithm for multi-UA V cooperative path planning,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.471718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation adf64d2b-8b28-4a1b-9a07-4108ff66796c · outbound

This paper cites Constrained multi-objective optimization for UA V-enabled mobile edge computing: Offloading optimization and path planning,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Constrained multi-objective optimization for UA V-enabled mobile edge computing: Offloading optimization and path planning,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.449143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 516d096e-3eee-4131-a610-5415d9488ec3 · outbound

This paper cites Multi-objective optimization for multi-UA V-assisted mobile edge computing,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Multi-objective optimization for multi-UA V-assisted mobile edge computing,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.420548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b6e62759-59d4-40ea-bad6-407f3478c6fd · outbound

This paper cites Optimizing multi-UA V multi-user system through integrated sensing and communi- cation for Age of Information (AoI) analysis,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Optimizing multi-UA V multi-user system through integrated sensing and communi- cation for Age of Information (AoI) analysis,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.391673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f13b8307-ee68-43df-a33c-fd78f68fd26e · outbound

This paper cites AoI-sensitive data collection in multi- UA V-assisted wireless sensor networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method AoI-sensitive data collection in multi- UA V-assisted wireless sensor networks,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.362631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e1834873-9725-45a9-a413-facc6614fb4f · outbound

This paper cites Sum rate maximization in IoT networks with diversity-enhanced energy harvesting: A DRL-guided approach,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Sum rate maximization in IoT networks with diversity-enhanced energy harvesting: A DRL-guided approach,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.340099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1debbb2a-ea5a-4f22-99f2-86d82e87081c · outbound

This paper cites Multi-objective trajectory planning for UA V-assisted IoT networks based on DRL approach,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Multi-objective trajectory planning for UA V-assisted IoT networks based on DRL approach,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.317870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2dbd7cd-0762-4879-a506-1518a5946bac · outbound

This paper cites Generative AI agents with large language model for satellite networks via a mixture of experts transmission,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Generative AI agents with large language model for satellite networks via a mixture of experts transmission,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.299803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 199aef3d-6f41-4260-b73d-fb5775e54a00 · outbound

This paper cites Energy efficiency maximization in RIS-assisted SWIPT networks with RSMA: A PPO-based approach,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Energy efficiency maximization in RIS-assisted SWIPT networks with RSMA: A PPO-based approach,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.279179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 490ebead-2424-4152-98b4-0c492394d0f0 · outbound

This paper cites A reinforcement learning- based fire warning and suppression system using unmanned aerial vehicles,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method A reinforcement learning- based fire warning and suppression system using unmanned aerial vehicles,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.260841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30f559e0-d01f-486f-92f3-9ba1687a39c5 · outbound

This paper cites Multiagent deep reinforce- ment learning for wireless-powered UA V networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Multiagent deep reinforce- ment learning for wireless-powered UA V networks,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.242857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09009f1f-99d6-4052-bee1-e994a326105f · outbound

This paper cites Multi-agent DRL-based large-scale heterogeneous task offloading for dynamic IoT systems,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Multi-agent DRL-based large-scale heterogeneous task offloading for dynamic IoT systems,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.217850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0bfc7c75-08b1-4219-b0b6-f98772a32671 · outbound

This paper cites UA V-assisted content caching for human-centric consumer applications in IoV,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method UA V-assisted content caching for human-centric consumer applications in IoV,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.195917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c210fb47-f543-49a4-a90f-5d6508c69e6a · outbound

This paper cites DRL-based resource allocation and trajectory planning for NOMA-enabled multi- UA V collaborative caching 6G network,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method DRL-based resource allocation and trajectory planning for NOMA-enabled multi- UA V collaborative caching 6G network,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.165438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 32cd622e-3086-43b7-a324-8a643e2c5628 · outbound

This paper cites Joint optimization of trajectory control, resource allocation, and user association based on DRL for multi-fixed- wing UA V networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Joint optimization of trajectory control, resource allocation, and user association based on DRL for multi-fixed- wing UA V networks,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.130373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b894d56d-42d5-4bea-a87b-070eca2ddf67 · outbound

This paper cites DRL-based joint task scheduling and trajectory planning method for UA V-assisted MEC scenarios,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method DRL-based joint task scheduling and trajectory planning method for UA V-assisted MEC scenarios,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.103478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation efa3e92f-ee32-4c92-80dd-c8bc5596e5eb · outbound

This paper cites A centralized multi-agent DRL-based trajectory control strategy for unmanned aerial vehicle-enabled wireless communications,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method A centralized multi-agent DRL-based trajectory control strategy for unmanned aerial vehicle-enabled wireless communications,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.080430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 324400b1-7090-414b-8b88-b6bb2bc03859 · outbound

This paper cites Minimizing age of information in UA V-assisted data collection with limited charging facilities,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Minimizing age of information in UA V-assisted data collection with limited charging facilities,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.054466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c8d70a7-43b5-4458-bbad-b2561f9856f8 · outbound

This paper cites UGV charging stations for UA V-assisted AoI-aware data collection,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method UGV charging stations for UA V-assisted AoI-aware data collection,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.028169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fec581e3-41b7-4700-b217-eca697c5310c · outbound

This paper cites Multi-agent DRL-based energy harvesting for freshness of data in UA V-assisted wireless sensor networks,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Multi-agent DRL-based energy harvesting for freshness of data in UA V-assisted wireless sensor networks,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:21.004068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 742621f0-fecf-4872-bfa9-a1e2c903f233 · outbound

This paper cites Joint optimization on trajectory, altitude, velocity, and link scheduling for minimum mission time in UA V-aided data collection,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Joint optimization on trajectory, altitude, velocity, and link scheduling for minimum mission time in UA V-aided data collection,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:20.981140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80f64dd9-631b-4d07-aae8-9962b4b4b0d0 · outbound

This paper cites Route coordination of UA V fleet to track a ground moving target in search and lock (SAL) task over urban airspace,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Route coordination of UA V fleet to track a ground moving target in search and lock (SAL) task over urban airspace,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:20.965413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f659ca8-6794-4f15-b923-df9632894be3 · outbound

This paper cites Optimal UA V caching and trajectory in aerial-assisted vehicular networks: A learning-based approach,.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Optimal UA V caching and trajectory in aerial-assisted vehicular networks: A learning-based approach,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:20.942577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bec1f8f2-6e49-4cf6-a43f-27aaebff2418 · outbound

This paper cites Securing the Sky: Integrated Satellite-UAV Physical Layer Security for Low-Altitude Wireless Networks.

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method Securing the Sky: Integrated Satellite-UAV Physical Layer Security for Low-Altitude Wireless Networks

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-06T18:25:19.567034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers