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1. Age of Information in Uncoordinated Unslotted Updating

   https://doi.org/10.1109/ISIT44484.2020.9174098  

   Yates, Roy D.; Kaul, Sanjit K. (June 2020, 2020 IEEE International Symposium on Information Theory (ISIT))

   null (Ed.)

   Sensor sources submit updates to a monitor through an unslotted, uncoordinated, unreliable multiple access collision channel. The channel is unreliable; a collision-free transmission is received successfully at the monitor with some transmission success probability. For an infinite-user model in which the sensors collectively generate updates as a Poisson process and each update has an independent exponential transmission time, a stochastic hybrid system (SHS) approach is used to derive the average age of information (AoI) as a function of the offered load and the transmission success probability. The analysis is then extended to evaluate the individual age of a selected source. When the number of sources and update transmission rate grow large in fixed proportion, the limiting asymptotic individual age is shown to provide an accurate individual age approximation, even for a small number of sources. 

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2. Timely Cloud Computing: Preemption and Waiting

   https://doi.org/10.1109/ALLERTON.2019.8919891  

   Arafa, Ahmed; Yates, Roy D.; Poor, H. Vincent (September 2019, 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton))

   The notion of timely status updating is investigated in the context of cloud computing. Measurements of a time-varying process of interest are acquired by a sensor node, and uploaded to a cloud server to undergo some required computations. These computations have random service times that are independent and identically distributed across different uploads. After the computations are done, the results are delivered to a monitor, constituting an update. The goal is to keep the monitor continuously fed with fresh updates over time, which is assessed by an age-of-information(AoI) metric. A scheduler is employed to optimize the measurement acquisition times. Following an update, an idle waiting period may be imposed by the scheduler before acquiring a new measurement. The scheduler also has the capability to preempt a measurement in progress if its service time grows above a certain cutoff time, and upload a fresher measurement in its place. Focusing on stationary deterministic policies, in which waiting times are deterministic functions of the instantaneous AoI and the cutoff time is fixed for all uploads, it is shown that the optimal waiting policy that minimizes the long term average AoI has a threshold structure, in which a new measurement is uploaded following an update only if the AoI grows above a certain threshold that is a function of the service time distribution and the cutoff time. The optimal cutoff is then found for standard and shifted exponential service times. While it has been previously reported that waiting before updating can be beneficial for AoI, it is shown in this work that preemption of late updates can be even more beneficial. 

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3. Average Age of Information in Update Systems with Active Sources and Packet Delivery Errors

   https://doi.org/10.1109/LWC.2020.2983397  

   Farazi, S.; Klein, A.; Brown, D. (August 2020, IEEE wireless communications letters)

   This paper studies the “age of information” (AoI) in a multi-source status update system where N active sources each send updates of their time-varying process to a monitor through a server with packet delivery errors. We analyze the average AoI for stationary randomized and round-robin scheduling policies. For both of these scheduling policies, we further analyze the effect of packet retransmission policies, i.e., retransmission without re- sampling, retransmission with resampling, or no retransmission, when errors occur. Expressions for the average AoI are derived for each case. It is shown that the round-robin schedule policy in conjunction with retransmission with resampling when errors occur achieves the lowest average AoI among the considered cases. For stationary randomized schedules with equiprobable source selection, it is further shown that the average AoI gap to round-robin schedules with the same packet management policy scales as O(N). Finally, for stationary randomized policies, the optimal source selection probabilities that minimize a weighted sum average AoI metric are derived. 

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4. Timely Offloading in Mobile Edge Cloud Systems

   https://doi.org/10.1109/ITW61385.2024.10806971  

   Sathyavageeswaran, Nitya; Yates, Roy D; Sarwate, Anand D; Mandayam, Narayan (November 2024, IEEE)

   Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud (MEC) can reduce the completion time of these tasks. However, using the MEC may come at a cost such as related to use of a cloud service or privacy. In this paper, we consider a source that generates time-stamped status updates for delivery to a monitor after processing by the mobile device or MEC. We study how a scheduler must forward these updates to achieve timely updates at the monitor but also limit MEC usage. We measure timeliness at the monitor using the age of information (AoI) metric. We formulate this problem as an infinite horizon Markov decision process (MDP) with an average cost criterion. We prove that an optimal scheduling policy has an age-threshold structure that depends on how long an update has been in service. 

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5. Anti-Aging Scheduling in Single-Server Queues: A Systematic and Comparative Study

   https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162767  

   Liu, Zhongdong; Huang, Liang; Li, Bin; Ji, Bo (July 2020, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

   The Age-of-Information (AoI) is a new performance metric recently proposed for measuring the freshness of information in information-update systems. In this work, we conduct a systematic and comparative study to investigate the impact of scheduling policies on the AoI performance in single-server queues and provide useful guidelines for the design of AoI-efficient scheduling policies. Specifically, we first perform extensive simulations to demonstrate that the update-size information can be leveraged for achieving a substantially improved AoI compared to non-size-based (or arrival-time-based) policies. Then, by utilizing both the update-size and arrival-time information, we propose three AoI-based policies. Observing improved AoI performance of policies that allow service preemption and that prioritize informative updates, we further propose preemptive, informative, AoI-based scheduling policies. Our simulation results show that such policies empirically achieve the best AoI performance among all the considered policies. Interestingly, we also prove sample-path equivalence between some size-based policies and AoI-based policies. This provides an intuitive explanation for why some size-based policies, such as Shortest-Remaining-Processing-Time (SRPT), achieve a very good AoI performance. 

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