On The Age Of Information In Status Update Systems With Packet Management
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Abstract (arXiv)
We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed since the last received update was generated. In this paper, we model the source-destination link using queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 0; 2025: 5.
10 papers.
- 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
- 2023Age of Information in Deep Learning-Driven Task-Oriented Communicationscited by 9
- 2025"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systemscited by 7
- 2023Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approachcited by 6
- 2021AoI-minimizing Scheduling in UAV-relayed IoT Networkscited by 2
- 2025Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guaranteescited by 1
- 2025Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaversecited by 0
- 2025Timely Information Updating for Mobile Devices Without and With ML Advicecited by 0
- 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learningcited by 0
- 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning24 Dec 2025
- 2025Timely Information Updating for Mobile Devices Without and With ML Advice19 Dec 2025
- 2025Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse28 Aug 2025
- 2025"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems25 Jul 2025
- 2025Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees11 Jul 2025
- 2023Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach1 Dec 2023
- 2023Age of Information in Deep Learning-Driven Task-Oriented Communications11 Jan 2023
- 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications19 Jul 2022
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges14 Nov 2021
- 2021AoI-minimizing Scheduling in UAV-relayed IoT Networks12 Jul 2021