Sampling for Data Freshness Optimization: Non-linear Age Functions

Sun, Yin; Cyr, Benjamin

doi:10.48550/arxiv.1812.07241

Cited by 7 publications

(11 citation statements)

References 72 publications

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“…where the expectation is taken with respect to the measure induced by policy π. Note that, the analytical framework and results for the linear age function in (11) also hold for non-decreasing non-linear age functions (see examples in [9]).…”

Section: Problem Formulationmentioning

confidence: 93%

“…The AoI has been recently studied under various communication system settings [8]- [18]. The authors in [8] and [9] propose optimal update generating policies to minimize the average AoI for a status update system with a single source node under general age penalty functions. In [10], the authors propose an age-optimal sampling and scheduling policy for a multi-source status update system with random transmission times.…”

Section: Introductionmentioning

confidence: 99%

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Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Zhou

Saad

2020

IEEE Trans. Wireless Commun.

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In this paper, a real-time Internet of Things (IoT) monitoring system is considered in which the IoT devices are scheduled to sample associated underlying physical processes and send the status updates to a common destination. In a real-world IoT, due to the possibly different dynamics of each physical process, the sizes of the status updates for different devices are often different and each status update typically requires multiple transmission slots. By taking into account such multi-time slot transmissions with nonuniform sizes of the status updates under noisy channels, the problem of joint device scheduling and status sampling is studied in order to minimize the average age of information (AoI) at the destination.This stochastic problem is formulated as an infinite horizon average cost Markov decision process (MDP). The monotonicity of the value function of the MDP is characterized and then used to show that the optimal scheduling and sampling policy is threshold-based with respect to the AoI at each device.To overcome the curse of dimensionality, a low-complexity suboptimal policy is proposed through a semi-randomized base policy and linear approximated value functions. The proposed suboptimal policy is shown to exhibit a similar structure to the optimal policy, which provides a structural base for its effective performance. A structure-aware algorithm is then developed to obtain the suboptimal policy.The analytical results are further extended to the IoT monitoring system with random status update arrivals, for which, the optimal scheduling and sampling policy is also shown to be threshold-based with the AoI at each device. Simulation results illustrate the structures of the optimal policy and show a near-optimal AoI performance resulting from the proposed suboptimal solution approach. Index TermsInternet of things, status update, age of information, optimization, scheduling.

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Section: Problem Formulationmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Zhou

Saad

2020

IEEE Trans. Wireless Commun.

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“…In [8] the authors study the problem of giving pre-emptive priority to one flow over another, in a single queue system, and obtain closed-form expressions for average age and peak age. In works such as [9], [10], the problem of optimal sampling in order to minimize age is addressed, in the context of single hop transmission. However, they do not take into account the effects on queueing owing to a higher sampling rate.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Age of Information in a Multihop Wireless Network

Krishnan

Sharma

2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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We consider the problem of minimizing age in a multihop wireless network. There are multiple source-destination pairs, transmitting data through multiple wireless channels, over multiple hops. We propose a network control policy which consists of a distributed scheduling algorithm, utilizing channel state information and queue lengths at each link, in combination with a packet dropping rule. Dropping of older packets locally at queues is seen to reduce the average age of flows, even below what can be achieved by Last Come First Served (LCFS) scheduling. Dropping of older packets also allows us to use the network without congestion, irrespective of the rate at which updates are generated. Furthermore, exploiting system state information substantially improves performance. The proposed scheduling policy obtains average age values close to a theoretical lower bound as well.

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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.

102

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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 7 publications

References 72 publications

Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Minimizing Age of Information in a Multihop Wireless Network

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

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