Paper Citation Record · LEDGER

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

As of 23 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 5 inbound Pith citation observations for arXiv:2504.15577.

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

pith.paper-citation-record.v1
2504.15577 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:26:30.742792Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:04:24.664816Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:32:36.566423Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy17
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c3c5174e-1950-4ffb-bad7-8df8eafc56cd · outbound

This paper cites Deep reinforcement learning-based energy-efficient edge computing for internet of vehicles,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Deep reinforcement learning-based energy-efficient edge computing for internet of vehicles,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:31.009623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9e2a3786-2395-4b2d-b21f-980de6003d5e · outbound

This paper cites Deep reinforcement learning-based workload scheduling for edge computing,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Deep reinforcement learning-based workload scheduling for edge computing,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.997138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4f1dbaec-e410-41ba-adf0-bb16bf24f30f · outbound

This paper cites A Reinforcement Learning Approach to Traffic Scheduling in Complex Data Center Topologies,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination A Reinforcement Learning Approach to Traffic Scheduling in Complex Data Center Topologies,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.982613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9129474b-2493-491e-a8e9-24da14901e45 · outbound

This paper cites Optimizing Distributed Computing Resources with Federated Learning: Task Scheduling and Communication Efficiency,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Optimizing Distributed Computing Resources with Federated Learning: Task Scheduling and Communication Efficiency,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.970372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6f43360c-7e00-4f8b-a9f7-ec53a2d84bf6 · outbound

This paper cites Dynamic Optimization of Human-Computer Interaction Interfaces Using Graph Convolutional Networks and Q-Learning,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Dynamic Optimization of Human-Computer Interaction Interfaces Using Graph Convolutional Networks and Q-Learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.956415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ea7643a1-b62b-4d04-9a41-01cb11e95759 · outbound

This paper cites Deep Learning-Based Gesture Key Point Detection for Human-Computer Interaction Applications,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Deep Learning-Based Gesture Key Point Detection for Human-Computer Interaction Applications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.943248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0581ddae-9bf6-433c-9798-e3caae979a61 · outbound

This paper cites Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:26:30.695991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71c9ce62-e5db-4f5d-8178-3617989d216e · outbound

This paper cites Efficient Compression of Large Language Models with Distillation and Fine-Tuning,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Efficient Compression of Large Language Models with Distillation and Fine-Tuning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.931642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 246e80ad-56b4-4aa3-9f10-3d59b4dcbdf1 · outbound

This paper cites Investigating Hierarchical Term Relationships in Large Language Models,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Investigating Hierarchical Term Relationships in Large Language Models,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:26:30.703270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d79820f7-b33a-4e81-b948-c38c4442758b · outbound

This paper cites Dynamic Distributed Scheduling for Data Stream Computing: Balancing Task Delay and Load Efficiency,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Dynamic Distributed Scheduling for Data Stream Computing: Balancing Task Delay and Load Efficiency,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.912129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 848d002c-284d-4391-ae13-fb064656525c · outbound

This paper cites Multivariate Time Series Forecasting and Classification via GNN and Transformer Models,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Multivariate Time Series Forecasting and Classification via GNN and Transformer Models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.899057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2600c89c-784b-4ecf-9c50-a15ad74fcc53 · outbound

This paper cites Improved Transformer for Cross-Domain Knowledge Extraction with Feature Alignment,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Improved Transformer for Cross-Domain Knowledge Extraction with Feature Alignment,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.886564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2dd46391-f7ef-4f13-9629-853b420733fa · outbound

This paper cites Mining Multimodal Data with Sparse Decomposition and Adaptive Weighting,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Mining Multimodal Data with Sparse Decomposition and Adaptive Weighting,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.873916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation db83e0af-e0d2-4392-83ce-d7dbf9f9fde9 · outbound

This paper cites A Self-Supervised Vision Transformer Approach for Dermatological Image Analysis,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination A Self-Supervised Vision Transformer Approach for Dermatological Image Analysis,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:26:30.721767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f4b1350-8022-4d16-a002-4587c69459d3 · outbound

This paper cites A Visual Communication Optimization Method for Human- Computer Interaction Interfaces Using Fuzzy Logic and Wavelet Transform,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination A Visual Communication Optimization Method for Human- Computer Interaction Interfaces Using Fuzzy Logic and Wavelet Transform,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.853578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aa882f1b-bbe0-4623-9d35-bf51ebc85656 · outbound

This paper cites Edge computing with artificial intelligence: A machine learning perspective,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Edge computing with artificial intelligence: A machine learning perspective,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.839071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4e07504f-41f3-4d46-b23c-5e5f5263b7de · outbound

This paper cites Sparse trace ratio LDA for supervised feature selection,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Sparse trace ratio LDA for supervised feature selection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.825430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7c3c5430-0f16-4b36-af0c-81f9dbce6ec3 · outbound

This paper cites Graph neural network meets multi-agent reinforcement learning: Fundamentals, applications, and future directions,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Graph neural network meets multi-agent reinforcement learning: Fundamentals, applications, and future directions,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.811735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 19055239-d0bd-46df-8c56-a7ea0f30ea38 · outbound

This paper cites A Graph Attention-Based Recommendation Framework for Sparse User-Item Interactions,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination A Graph Attention-Based Recommendation Framework for Sparse User-Item Interactions,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.799249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c6fab0cd-2ea4-4558-830c-9cf1c77b8f51 · outbound

This paper cites Optimizing gradient methods for IoT applications,.

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination Optimizing gradient methods for IoT applications,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:26:30.786104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation f05b866f-e388-471e-92be-f1e05438feb9 · inbound

Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks cites this paper.

Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:04:24.664816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82ac5100-0d0e-4b64-8ca4-d133eab126f2 · inbound

Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems cites this paper.

Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:33.206651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59a7bdcf-cde8-4c20-b82c-ac373c8cde69 · inbound

Structured Gradient Guidance for Few-Shot Adaptation in Large Language Models cites this paper.

Structured Gradient Guidance for Few-Shot Adaptation in Large Language Models State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:16.596012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3308bc83-9562-4cbb-b639-3dbc59520eec · inbound

Autonomous Resource Management in Microservice Systems via Reinforcement Learning cites this paper.

Autonomous Resource Management in Microservice Systems via Reinforcement Learning State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:40:06.200167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b701d6d8-ab46-4829-8e0a-f11ed5ecc6cc · inbound

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services cites this paper.

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:32:36.653057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.