Optimal Sampling and Scheduling for Timely Status Updates in Multi-Source Networks

Bedewy, Ahmed M.; Sun, Yin; Kompella, Sastry; Shroff, Ness B.

doi:10.1109/tit.2021.3060387

Cited by 60 publications

(28 citation statements)

References 44 publications

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“…So we consider the corresponding mean-field model ( ) { , ( )} instead. This model is characterized by a set of ordinary differential equations as (9) to (14).…”

Section: ( )mentioning

confidence: 99%

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Status Update for Accurate Remote Estimation: Centralized and Decentralized Schemes

Sun

Zhou

et al. 2022

IEICE Trans. Commun.

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In this work, we consider a remote estimation system where a remote controller estimates the status of heterogeneous sensing devices with the information delivered over wireless channels. Status of heterogeneous devices changes at different speeds. With limited wireless resources, estimating as accurately as possible requires careful design of status update schemes. Status update schemes can be divided into two classes: centralized and decentralized. In centralized schemes, a central scheduler coordinates devices to avoid potential collisions. However, in decentralized schemes where each device updates on its own, update decisions can be made by using the current status which is unavailable in centralized schemes. The relation between these two schemes under the heterogeneous devices case is unclear, and thus we study these two schemes in terms of the mean square error (MSE) of the estimation. For centralized schemes, since the scheduler does not have the current status of each device, we study policies where the scheduling decisions are based on age of information (AoI), which measures the staleness of the status information held in the controller. The optimal scheduling policy is provided, along with the corresponding MSE. For decentralized schemes, we consider deviation-based policies with which only devices with estimation deviations larger than prescribed thresholds may update, and the others stay idle. We derive an approximation of the minimum MSE under the deviation-based policies and show that it is e/3 of the minimum MSE under the AoI-based policies. Simulation results further show that the actual minimum MSEs of these two policies are even closer than that shown by the approximation, which indicates that the cost of collision in the deviation-based policy cancels out the gain from exploiting status deviations.

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“…So we consider the corresponding mean-field model ( ) { , ( )} instead. This model is characterized by a set of ordinary differential equations as (9) to (14).…”

Section: ( )mentioning

confidence: 99%

“…Ref. [9] studies the problem where the time needed to transmit is random. Some recent papers also study the estimation error of AoI-based methods.…”

Section: Introductionmentioning

confidence: 99%

Status Update for Accurate Remote Estimation: Centralized and Decentralized Schemes

Sun

Zhou

et al. 2022

IEICE Trans. Commun.

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“…MAF scheduling policies are intuitive since one focuses on minimizing the cumulative AoI of all sources. In addition, they have been shown optimal when channel conditions are symmetric across sources [49], and also in [50] through a stochastic ordering argument when all sources incur the same age-penalty. Observe that our system model is symmetric since updates from all sources encounter the same channel with i.i.d.…”

Section: B Perfect Updating Feedbackmentioning

confidence: 99%

Timely Status Updating Over Erasure Channels Using an Energy Harvesting Sensor: Single and Multiple Sources

Arafa¹,

Yang²,

Ulukuş³

et al. 2021

Preprint

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A status updating system is considered in which data from multiple sources are sampled by an energy harvesting sensor and transmitted to a remote destination through an erasure channel. The goal is to deliver status updates of all sources in a timely manner, such that the cumulative long-term average age-of-information (AoI) is minimized. The AoI for each source is defined as the time elapsed since the generation time of the latest successful status update received at the destination from that source. Transmissions are subject to energy availability, which arrives in units according to a Poisson process, with each energy unit capable of carrying out one transmission from only one source. The sensor is equipped with a unit-sized battery to save the incoming energy. A scheduling policy is designed in order to determine which source is sampled using the available energy. The problem is studied in two main settings: no erasure status feedback, and perfect instantaneous feedback.

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“…In several different queuing systems, the Last-Generated, First-Served (LGFS) policy is shown to achieve age-optimality [18]- [20]. Scheduling policies in various wireless networks are studied to minimize age [11]- [13], [16], [17]. A literature review of the recent studies in age of information is provided in [3].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sampling for Data Freshness: Unreliable Transmissions with Random Two-way Delay

Pan¹,

Bedewy²,

Yin³

et al. 2022

Preprint

Self Cite

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In this paper, we study a sampling problem, in which freshly sampled data is sent to a remote destination via an unreliable channel, and acknowledgments are sent back on a feedback channel. Both the forward and feedback channels are subject to random transmission times. We optimize the sampling strategy at the source (e.g., a sensor), aiming to enhance the freshness of data samples delivered to the destination (e.g., an estimator). This sampling problem is motivated by a distributed sensing system, where an estimator estimates a signal by combining noisy signal observations collected from a local sensor and accurate signal samples received from a remote sensor. We show that the optimal estimation error is an increasing function of the age of received signal samples. The optimal sampling problem for general non-decreasing age functions is formulated as an MDP with an uncountable state space. An exact solution to this problem is derived, which has a simple thresholdtype structure. The threshold can be calculated by low-complexity bisection search and fixed-point iterations. We find that, after a successful packet delivery, the optimal sampler may wait before taking the next sample and sending it out, whereas no waiting time should be added if the previous transmission failed. 1 This assumption arises from the stop-and-wait Automatic Repeat Request (ARQ) mechanism. When the backward delay X i,j = 0, the policy that samples ahead of receiving feedback is always suboptimal. Because such a policy takes a new sample when the channel is busy and can be replaced by a policy that samples at exact time of receiving feedback [5]. When X i,j = 0, however, it may be optimal to transmit before receiving acknowledgement, which is out of the scope of this paper.

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Optimal Sampling and Scheduling for Timely Status Updates in Multi-Source Networks

Cited by 60 publications

References 44 publications

Status Update for Accurate Remote Estimation: Centralized and Decentralized Schemes

Status Update for Accurate Remote Estimation: Centralized and Decentralized Schemes

Timely Status Updating Over Erasure Channels Using an Energy Harvesting Sensor: Single and Multiple Sources

Optimal Sampling for Data Freshness: Unreliable Transmissions with Random Two-way Delay

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