Timely Status Update in Wireless Uplinks: Analytical Solutions With Asymptotic Optimality

Jiang, Zhiyuan; Krishnamachari, Bhaskar; Zheng, Xinqian; Zhou, Sheng; Niu, Zhisheng

doi:10.1109/jiot.2019.2893319

Cited by 106 publications

(59 citation statements)

References 29 publications

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“…This index-based approach is shown to be nearoptimal, supported by numerous evaluation results although not theoretically proven. For theoretical optimality, it is shown by [5] that a round-robin scheduling scheme which requires small signaling overhead, is asymptotically optimal with a large number of terminals under certain conditions such as no transmission failures. In [6]- [9], another important aspect regarding the uplink uncoordinated access design is investigated.…”

Section: Information Latency Optimization In Wireless Network: mentioning

confidence: 99%

“…In particular, this definition is similar with the non-linear AoI [12] (or value of information) definition, with distinction that it is formulated under the physicalvirtual world framework in this article. Unlike communication latency, information latency accounts for not only the end-toend latency, but also communication reliability (unsuccessful updates cannot improve the information latency), sensing Scheduling [3] Bernoulli Ideal [4] Buffered sources Physical model [5] Round-robin Bernoulli Ideal [6] Prioritized CSMA ptx,n(t) [7] ALOHA Active sources i.i.d. error ptx,n [8] CSMA and round-robin Bernoulli/Active sources Ideal [9] CSMA Poisson Backoff window [10] General network ALOHA Active/Buffered sources ptx,n [11] Active sources i.i.d.…”

Section: Introductionmentioning

confidence: 99%

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AI-Assisted Low Information Latency Wireless Networking

Jiang

Zhou

et al. 2020

IEEE Wireless Commun.

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The 5G Phase-2 and beyond wireless systems will focus more on vertical applications such as autonomous driving and industrial Internet-of-things, many of which are categorized as ultra-Reliable Low-Latency Communications (uRLLC). In this article, an alternative view on uRLLC is presented, that information latency, which measures the distortion of information resulted from time lag of its acquisition process, is more relevant than conventional communication latency of uRLLC in wireless networked control systems. An AI-assisted Situationally-aware Multi-Agent Reinforcement learning framework for wireless neTworks (SMART) is presented to address the information latency optimization challenge. Case studies of typical applications in Autonomous Driving (AD) are demonstrated, i.e., dense platooning and intersection management, which show that SMART can effectively optimize information latency, and more importantly, information latency-optimized systems outperform conventional uRLLC-oriented systems significantly in terms of AD performance such as traffic efficiency, thus pointing out a new research and system design paradigm.

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Section: Information Latency Optimization In Wireless Network: mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AI-Assisted Low Information Latency Wireless Networking

Jiang

Zhou

et al. 2020

IEEE Wireless Commun.

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“…Due to its specialty in characterizing the timeliness of updates and transmissions, AoI is closely relative to various real-time scenarios and has been widely applied to sensorbased monitoring [15]- [17], cognitive radio-based IoT systems [18], and two-way data exchanging systems [19], [20]. In [15], the authors proposed to minimize the average AoI of updates and increase the life-time of mobile devices by transmitting some assisting updates from a correlated source.…”

Section: Related Workmentioning

confidence: 99%

“…In [15], the authors proposed to minimize the average AoI of updates and increase the life-time of mobile devices by transmitting some assisting updates from a correlated source. In multiterminal based monitoring systems, the age-energy trade-off and link-layer retransmission schemes were investigated in [16] while the multiple-access-layer load balancing schemes (e.g., round-robin) was investigated in [17]. For cognitive radio-based IoT networks, the critical update rate optimizing the primary system was obtained asymptotically in [18], which provided solid foundation to determine whether the overlay scheme outperforms the underlay scheme or not.…”

Section: Related Workmentioning

confidence: 99%

Age-Upon-Decisions Minimizing Scheduling in Internet of Things: To Be Random or To Be Deterministic?

Dong

Chen

Liu

et al. 2020

IEEE Internet Things J.

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We consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as Age upon Decisions (AuD), which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision making process. Theoretical results show that 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and would be minimized if the arrival process is periodic (i.e., deterministic); 2) when both the decision process and the arrive process are periodic, the average AuD is larger than, but decreases with decision rate to, the average AuD of the corresponding system with Poisson decisions (i.e., random); 3) when both the decision process and the arrive process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making periodical updates and decisions with properly controlled offset also is a promising solution if the timing information of the two processes can be accessed by the monitor.Index Terms-Age of information, age upon decisions, Internet of Things, update-and-decide systems, decision scheduling.

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“…where Ω P S is the average channel power gain between the PD and the SAP, and η = IC ΩSS 4 6 8 10 12 14 16 18 20 22 24 Index of time slot t As in [28], we assume that new status updates are generated at the PD and the SD according to an independent and identically distributed (i.i.d.) Bernoulli process with rates p and q.…”

Section: A Overlay and Underlay Schemesmentioning

confidence: 99%

Minimizing Age of Information in Cognitive Radio-Based IoT Systems: Underlay or Overlay?

Chen

Zhai

et al. 2019

IEEE Internet Things J.

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We consider a cognitive radio-based Internet-of-Things (CR-IoT) network consisting of one primary IoT (PIoT) system and one secondary IoT (SIoT) system. The IoT devices of both the PIoT and the SIoT respectively monitor one physical process and send randomly generated status updates to their associated access points (APs). The timeliness of the status updates is important as the systems are interested in the latest condition (e.g., temperature, speed and position) of the IoT device. In this context, two natural questions arise: (1) How to characterize the timeliness of the status updates in CR-IoT systems? (2) Which scheme, overlay or underlay, is better in terms of the timeliness of the status updates. To answer these two questions, we adopt a new performance metric, named the age of information (AoI). We analyze the average peak AoI of the PIoT and the SIoT for overlay and underlay schemes, respectively. Simple asymptotic expressions of the average peak AoI are also derived when the PIoT operates at high signalto-noise ratio (SNR). Based on the asymptotic expressions, we characterize a critical generation rate of the PIoT system, which can determine the superiority of overlay and underlay schemes in terms of the average peak AoI of the SIoT. Numerical results validate the theoretical analysis and uncover that the overlay and underlay schemes can outperform each other in terms of the average peak AoI of the SIoT for different system setups.

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Timely Status Update in Wireless Uplinks: Analytical Solutions With Asymptotic Optimality

Cited by 106 publications

References 29 publications

AI-Assisted Low Information Latency Wireless Networking

AI-Assisted Low Information Latency Wireless Networking

Age-Upon-Decisions Minimizing Scheduling in Internet of Things: To Be Random or To Be Deterministic?

Minimizing Age of Information in Cognitive Radio-Based IoT Systems: Underlay or Overlay?

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