Minimizing age-of-information in multi-hop wireless networks

Talak, Rajat; Karaman, Sertaç; Modiano, Eytan

doi:10.1109/allerton.2017.8262777

Cited by 203 publications

(118 citation statements)

References 14 publications

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“…In the above inequalities, (a) follows from (25) in Lemma 4 and (27); (b) follows from (26) in Lemma 4; and (c) follows from the monotonicity of f (·) in condition (C1), and by recalling that m(k) max{t q ∈ I : t q ≤ k}. Finally, (d) follows by recalling that g(k) = f (m(k)).…”

Section: Proof (Theorem 1)mentioning

confidence: 97%

“…Accordingly, it can reject estimates that are relatively stale -a fact that contributes to the stability of the error dynamics. We point out that while this is perhaps the first use of the notion of age-of-information (AoI) in a networked control/estimation setting, such a concept has been widely employed in the study of various queuing-theoretic problems arising in wireless networks [24]- [26]. 2 To sum up, we propose the first single-time-scale distributed state estimation algorithm that provides both finite-time and exponentially fast convergence guarantees, under significantly milder assumptions on the time-varying graph sequences than those in the existing literature.…”

mentioning

confidence: 99%

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Finite-Time Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information

Mitra¹,

Richards

Bagchi³

et al. 2019

2019 American Control Conference (ACC)

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We study the problem of designing a distributed observer for an LTI system over a time-varying communication graph. The limited existing work on this topic imposes various restrictions either on the observation model or on the sequence of communication graphs. In contrast, we propose a single-timescale distributed observer that works under mild assumptions. Specifically, our communication model only requires strongconnectivity to be preserved over non-overlapping, contiguous intervals that are even allowed to grow unbounded over time. We show that under suitable conditions that bound the growth of such intervals, joint observability is sufficient to track the state of any discrete-time LTI system exponentially fast, at any desired rate. In fact, we also establish finite-time convergence based on our approach. Finally, we develop a variant of our algorithm that is provably robust to worst-case adversarial attacks, provided the sequence of graphs is sufficiently connected over time. The key to our approach is the notion of a "freshness-index" that keeps track of the age-of-information being diffused across the network. Such indices enable nodes to reject stale estimates of the state, and, in turn, contribute to stability of the error dynamics.1 Such approaches, referred to as two-time-scale approaches, incur high communication cost, and might hence be prohibitive for real-time applications. arXiv:2001.07006v1 [eess.SY]

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Section: Proof (Theorem 1)mentioning

confidence: 97%

mentioning

confidence: 99%

Finite-Time Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information

Mitra¹,

Richards

Bagchi³

et al. 2019

2019 American Control Conference (ACC)

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show abstract

“…Now, notice that P [S AoI < X], being probability, is bounded by 1. Therefore, for (17) to hold, it must be the case that lim AoI→Amin E [min (X, S AoI )] = 0.…”

Section: Proof: Letmentioning

confidence: 99%

Age-Delay Tradeoffs in Single Server Systems

Talak

Modiano

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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We consider an M server system in which each server can service at most one update packet at a time. The system designer controls (1) scheduling -the order in which the packets get serviced, (2) routing -the server that an arriving update packet joins for service, and (3) the service time distribution with fixed service rate. Given a fixed update generation process, we prove a strong age-delay and age-delay variance tradeoff, wherein, as the average AoI approaches its minimum, the packet delay and its variance approach infinity. In order to prove this result, we consider two special cases of the M server system, namely, a single server system with last come first server with preemptive service and an infinite server system. In both these cases, we derive sufficient conditions to show that three heavy tailed service time distributions, namely Pareto, lognormal, and Weibull, asymptotically minimize the average AoI as their tail gets heavier, and establish the age-delay tradeoff results. We provide an intuitive explanation as to why such a seemingly counter intuitive age-delay tradeoff is natural, and that it should exist in many systems.

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“…In [4], status updating from multiple sources was analyzed. In [5] and [6], minimizing AoI for multi-hop wireless networks with interference-free networks and general interference constrains were considered, respectively. The above references focus on the update messages stochastically generated at the source.…”

Section: Introductionmentioning

confidence: 99%

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

Kuang

Gong

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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In status update scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this paper, we study the AoI for computation-intensive messages with MEC, and consider three computing schemes: local computing, remote computing at the MEC server, and partial computing, i.e., some part of computing tasks are performed locally, and the rest is executed at the MEC server. Zero-wait policy is adopted in all three schemes. Specifically, in local computing, a new message is generated immediately after the previous one is revealed by computing. While in remote computing and partial computing, a new message is generated once the previous one is received by the remote MEC server. With infinite queue size and exponentially distributed transmission time, closed-form average AoI for exponentially distributed computing time is derived for the three computing schemes. For deterministic computing time, the average AoI is analyzed numerically. Simulation results show that by carefully partitioning the computing tasks, the average AoI in partial computing is the smallest compared to local computing and remote computing. The results also indicate numerically the conditions on which remote computing attains smaller average AoI compared with local computing.

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Minimizing age-of-information in multi-hop wireless networks

Cited by 203 publications

References 14 publications

Finite-Time Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information

Finite-Time Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information

Age-Delay Tradeoffs in Single Server Systems

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

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