Sampling for data freshness optimization: Non-linear age functions

Sun, Yin; Cyr, Benjamin

doi:10.1109/jcn.2019.000035

Cited by 161 publications

(135 citation statements)

References 81 publications

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“…The result in Theorem 3 exhibits a structural property of optimal policies which also appears in the sampling problem that was studied in [19] . The sampling problem in [19] considered sources without energy harvesting, where the packet transmission times were i.i.d. and non-zero.…”

Section: Resultsmentioning

confidence: 77%

Optimal status updating with a finite-battery energy harvesting source

et al. 2019

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We consider an energy harvesting source equipped with a finite battery, which needs to send timely status updates to a remote destination. The timeliness of status updates is measured by a non-decreasing penalty function of the Age of Information (AoI). The problem is to find a policy for generating updates that achieves the lowest possible time-average expected age penalty among all online policies. We prove that one optimal solution of this problem is a monotone threshold policy, which satisfies (i) each new update is sent out only when the age is higher than a threshold and (ii) the threshold is a non-increasing function of the instantaneous battery level. Let τB denote the optimal threshold corresponding to the full battery level B, and p(·) denote the age-penalty function, then we can show that p(τB) is equal to the optimum objective value, i.e., the minimum achievable timeaverage expected age penalty. These structural properties are used to develop an algorithm to compute the optimal thresholds. Our numerical analysis indicates that the improvement in average age with added battery capacity is largest at small battery sizes; specifically, more than half the total possible reduction in age is attained when battery storage increases from one transmission's worth of energy to two. This encourages further study of status update policies for sensors with small battery storage.Index Terms-Age of information; age-energy tradeoff; nonlinear age penalty, threshold policy; optimal threshold; energy harvesting; battery capacity. which implies :Now, consider the case when r = 0 and ℓ = B − 2 for (50):which implies:Suppose that the inequality below is true for j ≥ ℓ + 1:J α (0, j + 1) − J α (0, j + 2) ≤ J α (0, j) − J α (0, j + 1). (53)

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Section: Resultsmentioning

confidence: 77%

Optimal status updating with a finite-battery energy harvesting source

et al. 2019

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“…with probability 0.05 being 100 and probability 0.95 being 1. 5 10 15 20 25 30 35 Contention Window Length W / mini-slots 1) Distributed Scheduling Scheme: Under the distributed scheduling scheme, a time slot is slightly longer due to the existence of contention window, which consists of W mini-slots. In the simulation, the length of a mini-slot is set to 10µs (For comparison, in IEEE 802.11g, the length of a mini-slot is 9µs), so that the length of a time slot in the distributed scheme is 1 + W 100 ms.…”

Section: B Multi-terminal Schedulingmentioning

confidence: 99%

“…In particular, ref. [5]- [9] characterize the nonlinear loss caused by information staleness as a function (e.g., exponential, logarithmic, and step function) of AoI, which adds non-linearity to the analysis of status information. However, by investigating the mean square error (MSE) minimization in the remote estimation of Wiener process [29] and Ornstein-Uhlenbeck process [30], it is proved that AoI-based sampling and transmission is not optimal when status information is observable.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Information Lapse for Timely Status Updates in Remote Control Systems

Zheng

Zhou

Niu

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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As 5G and Internet-of-Things (IoT) are deeply integrated into vertical industries such as autonomous driving and industrial robotics, timely status update is crucial for remote monitoring and control. In this regard, Age of Information (AoI) has been proposed to measure the freshness of status updates.However, it is just a metric changing linearly with time and irrelevant of context-awareness. We propose a context-based metric, named as Urgency of Information (UoI), to measure the nonlinear time-varying importance and the non-uniform context-dependence of the status information. This paper first establishes a theoretical framework for UoI characterization and then provides UoI-optimal status updating and user scheduling schemes in both single-terminal and multi-terminal cases. Specifically, an update-indexbased scheme is proposed for a single-terminal system, where the terminal always updates and transmits when its update index is larger than a threshold. For the multi-terminal case, the UoI of the proposed scheduling scheme is proven to be upper-bounded and its decentralized implementation by Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) is also provided. In the simulations, the proposed updating and scheduling schemes notably outperform the existing ones such as round robin and AoI-optimal schemes in terms of UoI, error-bound violation and control system stability. Index TermsRemote control systems, status update, transmission scheduling, Age-of-Information, 5G, Internet of Things.

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“…However, it cannot straightly describe the performance degradation caused by the lag in status update. A thorough survey on the performance degradation caused by information staleness can be found in [3]. The performance degradation of a system, such as the inaccuracy in monitoring and the invalidity of control decisions, varies based on the scenario and is generally non-linear on AoI.…”

Section: Introductionmentioning

confidence: 99%

Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

Zheng

Zhou

Jiang

et al. 2019

IEEE Trans. Wireless Commun.

106

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Timely status updates are crucial to enabling applications in massive Internet of Things (IoT). This paper measures the data-freshness performance of a status update system with an energy harvesting transmitter, considering the randomness in information generation, transmission and energy harvesting.The performance is evaluated by a non-linear function of age of information (AoI) that is defined as the time elapsed since the generation of the most up-to-date status information at the receiver. The system is formulated as two queues with status packet generation and energy arrivals both assumed to be Poisson processes. With negligible service time, both First-Come-First-Served (FCFS) and Last-Come-First-Served (LCFS) disciplines for arbitrary buffer and battery capacities are considered, and a method for calculating the average penalty with non-linear penalty functions is proposed. The average AoI, the average penalty under exponential penalty function, and AoI's threshold violation probability are obtained in closed form. When the service time is assumed to follow exponential distribution, matrix geometric method is used to obtain the average peak AoI. The results illustrate that under the FCFS discipline, the status update frequency needs to be carefully chosen according to the service rate and energy arrival rate in order to minimize the average penalty. Index TermsAge of information, energy harvesting, status update, queuing theory, internet of things.

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Sampling for data freshness optimization: Non-linear age functions

Cited by 161 publications

References 81 publications

Optimal status updating with a finite-battery energy harvesting source

Optimal status updating with a finite-battery energy harvesting source

Context-Aware Information Lapse for Timely Status Updates in Remote Control Systems

Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

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