Sampling and Remote Estimation for the Ornstein-Uhlenbeck Process through Queues: Age of Information and Beyond

Ornee, Tasmeen Zaman; Sun, Yin

doi:10.48550/arxiv.1902.03552

Cited by 11 publications

(22 citation statements)

References 55 publications

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“…Unlike the traditional queueing delay that is negligible in the case with a low sampling rate (i.e., low arrival rate), the AoI is dominated by the inter-arrival time and thus is rather large in the low sampling rate regime. This key difference has spurred AoI research in several aspects in recent years, e.g., AoI analysis and optimization (e.g., [16], [17]), AoI in vehicular networks (e.g., [18], [19]), online sampling and remote estimation (e.g., [20], [21]), AoI and energy harvesting (e.g., [22], [23], [24]), just to name a few.…”

Section: Related Work and Contextmentioning

confidence: 99%

Efficient Learning-based Scheduling for Information Freshness in Wireless Networks

2021

Preprint

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Motivated by the recent trend of integrating artificial intelligence into the Internet-of-Things (IoT), we consider the problem of scheduling packets from multiple sensing sources to a central controller over a wireless network. Here, packets from different sensing sources have different values or degrees of importance to the central controller for intelligent decision making. In such a setup, it is critical to provide timely and valuable information for the central controller. In this paper, we develop a parameterized maximum-weight type scheduling policy that combines both the AoI metrics and Upper Confidence Bound (UCB) estimates in its weight measure with parameter η.Here, UCB estimates balance the tradeoff between exploration and exploitation in learning and are critical for yielding a small cumulative regret. We show that our proposed algorithm yields the running average total age at most by O(N 2 η). We also prove that our proposed algorithm achieves the cumulative regret over time horizon T at most by O(N T /η+ √ N T log T ). This reveals a tradeoff between the cumulative regret and the running average total age: when increasing η, the cumulative regret becomes smaller, but is at the cost of increasing running average total age. Simulation results are provided to evaluate the efficiency of our proposed algorithm.

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Section: Related Work and Contextmentioning

confidence: 99%

Efficient Learning-based Scheduling for Information Freshness in Wireless Networks

2021

Preprint

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“…The timeliness can be measured by the age of information (AoI) metric, defined as the time elapsed since the latest message has been retrieved. AoI has been originally studied in queuing networks, e.g., [37], [38], and has found its way into other numerous contexts, see, e.g., [39]- [53], and the recent survey in [54].…”

Section: Introductionmentioning

confidence: 99%

Timely Private Information Retrieval

Banawan¹,

Arafa²,

Ulukuş³

2021

Preprint

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We introduce the problem of timely private information retrieval (PIR) from N non-colluding and replicated servers. In this problem, a user desires to retrieve a message out of M messages from the servers, whose contents are continuously updating. The retrieval process should be executed in a timely manner such that no information is leaked about the identity of the message. To assess the timeliness, we use the age of information (AoI) metric. Interestingly, the timely PIR problem reduces to an AoI minimization subject to PIR constraints under asymmetric traffic. We explicitly characterize the optimal tradeoff between the PIR rate and the AoI metric (peak AoI or average AoI) for the case of N = 2, M = 3. Further, we provide some structural insights on the general problem with arbitrary N , M .

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“…It focuses on the freshness of information, which is linear with time, and is irrelevant to context and status evolution. AoI has been studied extensively in recent literatures [5]- [13]. To overcome the linearity limitation of AoI, ref.…”

Section: Introductionmentioning

confidence: 99%

Beyond Age: Urgency of Information for Timeliness Guarantee in Status Update Systems

Zheng

Zhou

Niu

2020

2020 2nd 6G Wireless Summit (6G SUMMIT)

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Timely status updating is crucial for future applications that involve remote monitoring and control, such as autonomous driving and Industrial Internet of Things (IIoT). Age of Information (AoI) has been proposed to measure the freshness of status updates. However, it is incapable of capturing critical systematic context information that indicates the timevarying importance of status information, and the dynamic evolution of status. In this paper, we propose a context-based metric, namely the Urgency of Information (UoI), to evaluate the timeliness of status updates. Compared to AoI, the new metric incorporates both time-varying context information and dynamic status evolution, which enables the analysis on context-based adaptive status update schemes, as well as more effective remote monitoring and control. The minimization of average UoI for a status update terminal with an updating frequency constraint is investigated, and an update-index-based adaptive scheme is proposed. Simulation results show that the proposed scheme achieves a near-optimal performance with a low computational complexity.

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Sampling and Remote Estimation for the Ornstein-Uhlenbeck Process through Queues: Age of Information and Beyond

Cited by 11 publications

References 55 publications

Efficient Learning-based Scheduling for Information Freshness in Wireless Networks

Efficient Learning-based Scheduling for Information Freshness in Wireless Networks

Timely Private Information Retrieval

Beyond Age: Urgency of Information for Timeliness Guarantee in Status Update Systems

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