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1. Distribution-Oblivious Online Algorithms for Age-of-Information Penalty Minimization

   https://doi.org/10.1109/TNET.2022.3230009  

   Tsai, Cho-Hsin; Wang, Chih-Chun (January 2023, IEEE/ACM Transactions on Networking)

   The ever-increasing needs of supporting real-time applications have spurred new studies on minimizing Age-of-Information (AoI), a novel metric characterizing the data freshness of the system. This work studies the single-queue information update system and strengthens the seminal results of Sun et al. on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new fixed-point-based method is proposed with quadratic convergence rate, an order-of-magnitude improvement over the state-of-the-art; (ii) When the distributional knowledge is unavailable (which is the norm in practice), two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to upgrade certain components to handle additional practical challenges. Two such upgrades are proposed for the situations: (iii.1) The AoI penalty function is also unknown and must be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. The performance of our schemes is either provably optimal or within 3% of the omniscient optimal offline solutions in all simulation scenarios. 

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2. Goal-Oriented Communications for Remote Inference Under Two-Way Delay with Memory

   https://doi.org/10.1109/ISIT57864.2024.10619298  

   Ari, Cagri; Shisher, Md_Kamran Chowdhury; Uysal, Elif; Sun, Yin (July 2024, IEEE)

   We study the design of a goal-oriented sampling and scheduling strategy through a channel with highly variable two-way random delay, which can exhibit memory (e.g., Delay and Disruption Tolerant Networks). The objective of the communication is to optimize the performance of remote inference, where an inference algorithm (e.g., a trained neural network) on the receiver side predicts a time-varying target signal using the data samples transmitted by a sensor. Previous formulations to this problem either assumed a channel with IID transmission delay, neglecting feedback delay or considered the monotonic relation that the performance only gets worse as the input information ages. We show how, with delayed feedback, one can effectively exploit the knowledge about delay memory through an index-based threshold policy. This policy minimizes the expected time-average inference error that can be monotone or non-monotone in age. The index function is expressed in terms of the Age of Information (AoI) on the receiver side and a parameter regarding the distribution of subsequent transmission delay, both of which can readily be tracked. 

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3. Age of Information Aware Cache Updating with File- and Age-Dependent Update Durations

   Tang, H.; Ciblat, P.; Wang, J.; Wigger, M.; Yates, R. (June 2020, Proceedings of the International Symposium on Modeling and Optimization in Mobile Ad Hoc and Wireless Networks)

   null (Ed.)

   We consider a system consisting of a library of time-varying files, a server that at all times observes the current version of all files, and a cache that at the beginning stores the current versions of all files but afterwards has to update these files from the server. Unlike previous works, the update duration is not constant but depends on the file and its Age of Information (AoI), i.e., of the time elapsed since it was last updated. The goal of this work is to design an update policy that minimizes the average AoI of all files with respect to a given popularity distribution. Actually a relaxed problem, close to the original optimization problem, is solved and a practical update policy is derived. The update policy relies on the file popularity and on the functions that characterize the update durations of the files depending on their AoI. Numerical simulations show a significant improvement of this new update policy compared to the so-called square-root policy that is optimal under file-independent and constant update durations. 

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4. 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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5. Unifying AoI Minimization and Remote Estimation — Optimal Sensor/Controller Coordination with Random Two-way Delay

   https://doi.org/10.1109/INFOCOM41043.2020.9155238  

   Tsai, Cho-Hsin; Wang, Chih-Chun (July 2020, IEEE International Conference on Computer Communications (INFOCOM))

   null (Ed.)

   The ubiquitous usage of communication networks in modern sensing and control applications has kindled new interests on the timing-based coordination between sensors and controllers, i.e., how to use the “waiting time” to improve the system performance. Contrary to the common belief that a zero-wait policy is always optimal, Sun et al. showed that a controller can strictly improve the data freshness, the so-called Age-of-Information (AoI), by postponing transmission in order to lengthen the duration of staying in a good state. The optimal waiting policy for the sensor side was later characterized in the context of remote estimation. Instead of focusing on the sensor and controller sides separately, this work develops the optimal joint sensor/controller waiting policy in a Wiener-process system. The results can be viewed as strict generalization of the above two important results in the sense that not only do we consider joint sensor/controller designs (as opposed to sensor-only or controller only schemes), but we also assume random delay in both the forward and feedback directions (as opposed to random delay in only one direction). In addition to provable optimality, extensive simulation is used to verify the performance of the proposed scheme in various settings. 

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