Age of Information

Also known as AoI

Age of Information measures the time since the newest information at a destination was generated, making freshness a design objective for wireless networks, edge systems, robotics, and networked control. Work in the field uses queueing and stochastic control, scheduling and threshold policies, Lyapunov optimization, and reinforcement learning to decide which updates to send and when. Researchers also extend freshness metrics to account for semantic value, task accuracy, deadlines, and the ages of multiple information sources. A key tension is that low average AoI does not always mean good task performance: update-interval distributions, information relevance, communication costs, and delayed actions can all matter, while heavy-tailed delays remain difficult to analyze.

Latest papers

19 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

K-AoI: A Spatio-Temporal Synchronization Metric for Digital Twin-based Teleoperation in Dynamic Network Environments
Oct 19, 2026Lu Tian, Zhenhui Yuan, Zexin Deng +1Age of Information Robot Teleoperation
School of Engineering, University of Warwick, Coventry, UK · Warwick Business School, University of Warwick, Coventry, UK
Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made
Oct 13, 2026Ayoub Merimi, Souhail Bakkali, Abdeljalil SakatLLM Decision-Making Temporal Reasoning in Language Models
Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning
Oct 9, 2026Sajjad Baghaee, Aimin Li, Shanmugarajan Balasubramanian +1Wireless Communications Age of Information
Communication Networks Research Group (CNG), Dept. of Electrical and Electronics Engineering, Middle East Technical University (METU), Ankara, Türkiye
Distributed Stochastic Approximation Algorithms and Heavy-Tailed Age of Information
Sep 23, 2026Adrian Redder, Arunselvan Ramaswamy, Holger KarlStochastic Approximation Age of Information
Dept. of Electrical Engineering and Information Technology, Paderborn University, Germany · Dept. of Industrial Engineering and Operations Research, Indian Institute of Technology Bombay, India · Hasso-Plattner Institute (HPI), Digital Engineering Faculty, University of Potsdam, Germany
Scalable Edge-assisted Fusion and Path Prediction for Connected Autonomous Vehicles
Sep 3, 2026Tyler Landle, Jackson Isenberg, Abhijit Chatterjee +2Cooperative Perception World Models for Autonomous Driving
Georgia Institute of Technology · University of Edinburgh
SafeStep: An Interactive Demonstration of Semantic Communication for Pedestrian Safety Monitoring
Aug 27, 2026Christian McDowell, Andrea Panebianco, Jeremiah Yang +4Semantic Communication Task-Oriented Semantic Communication
Department of Electrical and Computer Engineering, Auburn University, Alabama, USA
TALSC: Timeliness-Aware Large-Small VLM Collaboration for Infrastructure-Assisted Autonomous Driving
Aug 3, 2026Mengmeng Zhu, Yuxuan Sun, Wei Chen +1Vision-Language Models VLMs for Autonomous Driving
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks
Aug 2, 2026Yixin Zhang, Zhuohui Yao, Wenchi Cheng +1Multi-Agent Control Age of Information
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China · Bradley Department of Electrical and Computer Engineering, Virginia Tech, Alexandria, VA, USA
Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning
Jul 30, 2026Wentao Zhang, Wentao MoOnline Convex Optimization Real-Time Scheduling
Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen, China
The Half-Lives of Generative-AI Evidence: A 40-Record Audit, a Claim-Currency Framework, and a Reflexive Case of Frontier-Model-Assisted Research
Jul 27, 2026Carlo IaconoLLM Auditing AI-Assisted Scientific Research
Charles Sturt University, Australia
Delay-Aware Active Triangulation with Uncertainty-Driven Multi-Agent Reinforcement Learning for Counter-UAS
Jul 7, 2026Seungwook Lee, David Hyunchul ShimAge of Information Active Sensing
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
Minimizing Quantized Semantic Age of Information (QSAoI) in Foundation Model-Based Semantic Communications
Jun 30, 2026Huanyu Zhang, Yulin Hu, Xiaopeng Yuan +2Mixed-Precision Quantization Semantic Communication
INDA Chair, RWTH Aachen University, Germany · School of Electronic Information, Wuhan University, China · Department of Digital Communication Systems, Ruhr University Bochum, Germany
Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation
Jun 9, 2026Chanuka A. S. Hewa Kaluannakkage, Rajkumar BuyyaFederated Learning Aggregation Asynchronous Federated Learning
Quantum Cloud and Distributed Systems (qCLOUDS) Lab School of Computing and Information Systems The University of Melbourne, Australia
When Mean Age Is Not Enough: Distribution-Aware Scheduling for Networked LQR Control
Jun 3, 2026Abdullah Y. Etcibasi, C. Emre Koksal, Eylem EkiciAge of Information Feedback Control
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.
A Kinetic Theory of Encounter-Based Information Propagation in Multi-Robot Systems
Jun 1, 2026Alkesh K. Srivastava, Philip DamesMulti-Robot Systems Age of Information
Temple University, Philadelphia, PA, 19122 USA
When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization
May 6, 2026Yalin E. Sagduyu, Tugba ErpekSemantic Communication Age of Information
Nexcepta, Gaithersburg, MD, USA
AoI-Aware Multi-Robot Sensing and Transport on Connected Graphs
May 4, 2026John TadrousMulti-Robot Systems Age of Information
Electrical and Computer Engineering Department, Gonzaga University, WA 99258
Beyond Freshness and Semantics: A Coupon-Collector Framework for Effective Status Updates
Mar 27, 2026Youssef Ahmed, Arnob Ghosh, Chih-Chun Wang +1Age of Information Wireless Resource Allocation
Department of ECE, The Ohio State University, Columbus, OH, USA · Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA · Elmore Family School of ECE, Purdue University, West Lafayette, IN, USA +1
Multimodal Remote Inference
Aug 11, 2025Keyuan Zhang, Yin Sun, Bo JiSemi-Markov Decision Process Efficient Multimodal Inference
Department of Computer Science, Virginia Tech · Department of Electrical and Computer Engineering, Auburn University

Oct 19, 2026J/K move · Enter open · S save

K-AoI: A Spatio-Temporal Synchronization Metric for Digital Twin-based Teleoperation in Dynamic Network Environments

Lu Tian, Zhenhui Yuan, Zexin Deng, Wenjuan Zhang

School of Engineering, University of Warwick, Coventry, UK · Warwick Business School, University of Warwick, Coventry, UK

Age of Information Robot Teleoperation Digital Twins

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Synchronization between a digital twin (DT) and its physical robot is critical for effective teleoperation, as mismatches directly degrade operator awareness and task performance. Existing synchronization metrics are predominantly time-based and rarely capture the spatial consequences of delayed information during motion. In this paper, we introduce Kinematic Age of Information (K-AoI), a spatio-temporal metric that weights AoI by instantaneous Cartesian velocity, translating temporal staleness into physically meaningful spatial divergence. We implement and evaluate K-AoI on a DT teleoperation testbed comprising a six-degrees-of-freedom (6-DOF) manipulator, a Unity-based twin, and controlled network impairments using tc netem across seven network conditions. Per-sample analysis shows that K-AoI exhibits rank-correlations with AoI at

ρ\leq 0.65

across all networks, dropping to

ρ= 0.22

under satellite connections. This shows that K-AoI is not a simple transformation of AoI, but instead captures velocity-dependent degradation effects that time-only metrics cannot resolve. We further analyze AoI tail behavior using Ultra-Low AoI (ULAoI) and find that the commonly assumed Generalized Pareto Distribution (GPD) model is rejected. This reflects the impact of deterministic forwarding and bounded jitter, which violate standard extreme-value assumptions. Overall, K-AoI provides a complementary and physically grounded synchronization metric, linking network delay to task-relevant spatial error in digital-twin teleoperation systems. The dataset and testbed software are publicly available at: https://github.com/luziyaotian/kaoi-testbed.