Age of information in wireless networks

Kadota, Igor; Rahman, Muhammad Shahir; Modiano, Eytan

doi:10.1145/3372224.3418171

Cited by 13 publications

(4 citation statements)

References 6 publications

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“…where g(T ) ≡ ∑ j g j (T ). Using the inequality (27), we can further lower bound the inequality (26) as:…”

Section: Ni(t ) J=1mentioning

confidence: 99%

“…In the same paper, the authors also propose a 4-optimal Max-Weight scheduling policy (MW) for an arbitrary static network consisting of a single access point. The proposed MW policy has been reported to practically improve the information freshness in WiFi networks by two orders of magnitude [27]. The paper [28] extends the MW policy by taking into account additional throughput constraints.…”

Section: Introductionmentioning

confidence: 98%

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Optimizing Age-of-Information in Adversarial and Stochastic Environments

Sinha¹,

Bhattacharjee²

2020

Preprint

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We study a multi-user downlink scheduling problem for optimizing the freshness of information available to users roaming across multiple cells. We consider both adversarial and stochastic settings and design scheduling policies that optimize two distinct information freshness metrics, namely the average age-of-information and the peak age-of-information. We show that a natural greedy scheduling policy is competitive with the optimal offline policy in the adversarial setting. We also derive fundamental lower bounds to the competitive ratio achievable by any online policy. In the stochastic environment, we show that a Max-Weight scheduling policy that takes into account the channel statistics achieves an approximation factor of 2 for minimizing the average age of information in two extreme mobility scenarios. We conclude the paper by establishing a large deviation optimality result achieved by the greedy policy for minimizing the peak age of information for static users situated at a single cell.

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“…where g(T ) ≡ ∑ j g j (T ). Using the inequality (27), we can further lower bound the inequality (26) as:…”

Section: Ni(t ) J=1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Optimizing Age-of-Information in Adversarial and Stochastic Environments

Sinha¹,

Bhattacharjee²

2020

Preprint

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“…Age aware protocols have been introduced in the literature. Freshness aware Medium Access Control (MAC) protocols were introduced in [18,24], while IoT latency/age aware protocols were introduced in [7,10,11,26,29,36,39,41]. Additionally, in [3], a complete real life implementation of an IoT network was carried out and AoI measurements taken.…”

Section: Introductionmentioning

confidence: 99%

Statistical guarantee of timeliness in networks of IoT devices

et al. 2022

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The Internet of Things (IoT) paradigm, has opened up the possibility of using the ubiquity of small devices to route information without the necessity of being connected to a Wide Area Network (WAN). Use cases of IoT devices sending updates that are routed and delivered by other IoT devices have been proposed in the literature. In this paper we focus on receivers only interested in the freshest updates from the sending device. In particular, the dynamic network created by routing/gossiping through small devices creates the possibility of delivering updates out of order. Thus, the entire process can be studied well through a queueing system with infinitely many servers, all serving updates with a random service time. Age of Information (AoI) was proposed as the main metric to measure information freshness. We study the amount of time that the AoI is over a certain threshold at the receiver end as a Quality of Service (QoS) measure, called update outage probability. Particularly, given the recent interest in the literature for time domain analysis of the AoI, we obtain the exact expressions for the AoI, peak AoI (pAoI), effective service time and effective departure time distributions for an M/M/$$\infty $$ ∞ queuing system from a time domain perspective, and study the interdependence between the various parameters involved in order to satisfy a given statistical constraint on timeliness.

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“…There have been a relatively small number of implementation studies on AoI to date [1][2][3][4][5][6][7][8][9]. The goal of this chapter is to compile and discuss some results on practical aspects of AoI from this burgeoning literature.…”

Section: Introductionmentioning

confidence: 99%

Age of Information in Practice

Uysal-Biyikoğlu¹,

Kaya²,

Baghaee³

et al. 2021

Preprint

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While age of Information (AoI) has gained importance as a metric characterizing the freshness of information in information-update systems and time-critical applications, most previous studies on AoI have been theoretical. In this chapter, we compile a set of recent works reporting AoI measurements in real-life networks and experimental testbeds, and investigating practical issues such as synchronization, the role of various transport layer protocols, congestion control mechanisms, application of machine learning for adaptation to network conditions, and device related bottlenecks such as limited processing power.

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Age of information in wireless networks

Cited by 13 publications

References 6 publications

Optimizing Age-of-Information in Adversarial and Stochastic Environments

Optimizing Age-of-Information in Adversarial and Stochastic Environments

Statistical guarantee of timeliness in networks of IoT devices

Age of Information in Practice

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