Real-time status: How often should one update?

By Sanjit Krishnan Kaul, Roy D. Yates, Marco Gruteser. 2012

2,261 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks (2019, methodology): “In order to quantify the staleness of each update, we leverage the concept of information freshness [17] and define a metric termed Age-of-Update (AoU).”
Real-time status updating: Multiple sources (2012, methodology): “M/M/1 FCFS – TWO SOURCES In [8], we analyzed E [TX] for a variety of FCFS queues serving the status updates of only a single source.”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, neutral): “While these works considered explicit source and channel statistics, and treated the end-to-end optimization problem as JSCC with a delay-constrained distortion measure, there has been a significant research interest from the networking community to treat the physical channel as a random queue [286]”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “tions, a new metric was proposed in [240], that is, Age of Information (AoI).”
Age of Information: A New Concept, Metric, and Tool (2018, methodology): “Already the first AoI lower bound had appeared in [33] and we discuss it in Section 2.”
Optimizing age-of-information in a multi-class queueing system (2015, methodology): “For a multi-class G/G/ 1 queue, we also know that the AoI is given by [4]: (5) Comparing (5) and (4), we have: Equation (6) provides a way for checking how close the two metrics are to each other.”
Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks (2018, methodology): “The problem of minimizing AoI was introduced in [2] and has been explored using different approaches.”
Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source (2017, methodology): “The corresponding AoI has been analyzed in single-source singleserver queues [4], the M/M/1 Last-Come First-Served (LCFS) queue with preemption in service [5], the M/M/1 First-Come First-Served (FCFS) system with multiple sources [6], [7], and a multiple-source M/M/1 system which only keeps the late”
Update or wait: How to keep your data fresh (2016, positive): “In this model, the source keeps monitoring the network server's idle/busy state, and in contrast to [1]–[7], is able to generate status updates at any time by its own will.”
Status updates through queues (2012, methodology): “Finally, in [1] we look at minimizing age for the queue discipline of FCFS.”
Deep Learning With Edge Computing: A Review (2019, background): “For example, should a new camera frame inference request from device A receive higher priority than the hundredth frame from device B? Incorporating freshness metrics, such as the age of information [147], could allow for more intelligent scheduling decisions by the edge server.”
Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks (2018, background): “AoI measures packet freshness at the destination accounting for the time elapsed since the last update generated by a given source [1].”