# Age of Information Optimization

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/age-of-information-optimization/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on optimizing the freshness of information in communication networks, particularly in the context of real-time status updates, wireless networks, energy harvesting, and scheduling policies. The research explores age of information metrics, multi-hop networks, IoT monitoring systems, and queue management to minimize the age of information and improve network performance. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t13553 |
| Works | 16 |

## Topic papers all

Showing 15 of 16.

- [Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA](https://scholariq.org/papers/task-offloading-and-resource-allocation-for-mobile-edge-computing-by-deep/)
- [Autonomic computation offloading in mobile edge for IoT applications](https://scholariq.org/papers/autonomic-computation-offloading-in-mobile-edge-for-iot-applications/)
- [Intelligent task prediction and computation offloading based on mobile-edge cloud computing](https://scholariq.org/papers/intelligent-task-prediction-and-computation-offloading-based-on-mobile-edge/)
- [Software defined satellite networks: A survey](https://scholariq.org/papers/software-defined-satellite-networks-a-survey/)
- [Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network](https://scholariq.org/papers/energy-aware-task-scheduling-and-offloading-using-deep-reinforcement-learning-in/)
- [Multi-Agent Deep Reinforcement Learning-Based Flexible Satellite Payload for Mobile Terminals](https://scholariq.org/papers/multi-agent-deep-reinforcement-learning-based-flexible-satellite-payload-for/)
- [Fresh Data Collection for UAV-Assisted IoT Based on Aerial Collaborative Relay](https://scholariq.org/papers/fresh-data-collection-for-uav-assisted-iot-based-on-aerial-collaborative-relay/)
- [HealthEdge: Task scheduling for edge computing with health emergency and human behavior consideration in smart homes](https://scholariq.org/papers/healthedge-task-scheduling-for-edge-computing-with-health-emergency-and-human/)
- [Joint Optimization of Resource Utilization and Load Balance with Privacy Preservation for Edge Services in 5G Networks](https://scholariq.org/papers/joint-optimization-of-resource-utilization-and-load-balance-with-privacy/)
- [Multiagent Meta-Reinforcement Learning for Optimized Task Scheduling in Heterogeneous Edge Computing Systems](https://scholariq.org/papers/multiagent-meta-reinforcement-learning-for-optimized-task-scheduling-in/)
- [A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing](https://scholariq.org/papers/a-new-task-scheduling-scheme-based-on-genetic-algorithm-for-edge-computing/)
- [Distributed Deep Neural Network Deployment for Smart Devices from the Edge to the Cloud](https://scholariq.org/papers/distributed-deep-neural-network-deployment-for-smart-devices-from-the-edge-to/)
- [Graph Attention Reinforcement Learning for Multicast Routing and Age-Optimal Scheduling](https://scholariq.org/papers/graph-attention-reinforcement-learning-for-multicast-routing-and-age-optimal/)
- [Optimizing Task Completion Rate in Multi-user Edge Intelligence Networks Through Neural Network-Based Energy Management with Partitioning and Offloading](https://scholariq.org/papers/optimizing-task-completion-rate-in-multi-user-edge-intelligence-networks-through/)
- [AI-Driven V2G Optimization: A Comparative Analysis of Q-Learning and Evolutionary Strategies](https://scholariq.org/papers/ai-driven-v2g-optimization-a-comparative-analysis-of-q-learning-and-evolutionary/)

## Topic primary papers

- [Graph Attention Reinforcement Learning for Multicast Routing and Age-Optimal Scheduling](https://scholariq.org/papers/graph-attention-reinforcement-learning-for-multicast-routing-and-age-optimal/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.