Timeliness in Lossless Block Coding

Zhong, Jing; Yates, Roy D.

doi:10.1109/dcc.2016.113

Cited by 47 publications

(38 citation statements)

References 12 publications

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“…The age-of-information under energy-harvesting constraints was studied in [27], [28]. Source coding techniques for reducing the age were evaluated in [29]. In [19], [20], it was proven that the LGFS policy achieves a smaller age process (in a stochastic ordering sense) than any other causal policy for multi-channel and multi-hop networks with an arbitrary (e.g., out-of-order) update arrival process.…”

Section: Related Workmentioning

confidence: 99%

Update or Wait: How to Keep Your Data Fresh

Sun

Uysal-Biyikoğlu

Yates

et al. 2017

IEEE Trans. Inform. Theory

Self Cite

786

619

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Abstract-In this work, we study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, which is defined as how old the freshest received update is since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero-wait policy, i.e., the source node submits a fresh update once the previous update is delivered and the channel becomes free, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero-wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semiMarkov decision problem (SMDP) with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies, and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if (i) the age penalty function grows quickly with respect to the age, (ii) the packet transmission times over the channel are positively correlated over time, or (iii) the packet transmission times are highly random (e.g., following a heavy-tail distribution).

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Section: Related Workmentioning

confidence: 99%

Update or Wait: How to Keep Your Data Fresh

Sun

Uysal-Biyikoğlu

Yates

et al. 2017

IEEE Trans. Inform. Theory

Self Cite

786

619

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“…The relationship between AoI and the MMSE in remote estimation of a Wiener process is investigated in [22], [23]. Various source and channel coding techniques for AoI optimization have been discussed in [24]- [28]. AoI optimization for data storage has been studied in [29].…”

Section: Introductionmentioning

confidence: 99%

Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source

Yang

2018

IEEE Trans. on Green Commun. Netw.

309

266

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In this paper, we consider a scenario where an energy harvesting sensor continuously monitors a system and sends time-stamped status updates to a destination. The destination keeps track of the system status through the received updates. We use the metric Age of Information (AoI), the time that has elapsed since the last received update was generated, to measure the "freshness" of the status information available at the destination. We assume energy arrives randomly at the sensor according to aPoisson process, and each status update consumes one unit of energy. Our objective is to design optimal online status update policies to minimize the long-term average AoI, subject to the energy causality constraint at the sensor. We consider three scenarios, i.e., the battery size is infinite, finite, and one unit only, respectively.For the infinite battery scenario, we adopt a best-effort uniform status update policy and show that it minimizes the long-term average AoI. For the finite battery scenario, we adopt an energy-aware adaptive status update policy, and prove that it is asymptotically optimal when the battery size goes to infinity.For the last scenario where the battery size is one, we first show that within a broadly defined class of online policies, the optimal policy should have a renewal structure, i.e., the status update epochs form a renewal process, and the length of each renewal interval depends on the first energy arrival over that interval only. We then focus on a renewal interval, and prove that if the AoI in the system is below a threshold when the first energy arrives, the sensor should store the energy and hold status update until the AoI reaches the threshold; otherwise, it updates the status immediately. We analytically characterize the long-term average AoI under such a threshold-based policy, and explicitly identify the optimal threshold. Simulation results corroborate the theoretical bounds.

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“…An alternative formulation for minimum age source coding is considered in the recent work [26]. Unlike our formulation, skipping of symbols is prohibited in [26].…”

Section: Introductionmentioning

confidence: 99%

“…In our applications of interest, the allowed communication rates are much lower than the rate at which data is generated, and there is no hope of transmitting all the data at bounded delay, as mandated by the formulations available hitherto. Nonetheless, our setting is related closely to that in [26] and provides a complementary formulation for age optimal source coding.…”

Section: Introductionmentioning

confidence: 99%

Optimal Source Codes for Timely Updates

Mayekar

Parag

Tyagi

2020

IEEE Trans. Inform. Theory

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A transmitter observing a sequence of independent and identically distributed random variables seeks to keep a receiver updated about its latest observations. The receiver need not be apprised about each symbol seen by the transmitter, but needs to output a symbol at each time instant t. If at time t the receiver outputs the symbol seen by the transmitter at time U (t) ≤ t, the age of information at the receiver at time t is t − U (t). We study the design of lossless source codes that enable transmission with minimum average age at the receiver. We show that the asymptotic minimum average age can be roughly attained by Shannon codes for a tilted version of the original pmf generating the symbols, which can be computed easily by solving an optimization problem. Furthermore, we exhibit an example with alphabet X where the Shannon codes for the original pmf incur an asymptotic average age of a factor O( log |X |) more than that achieved by our codes. Underlying our prescription for optimal codes is a new variational formula for integer moments of random variables, which may be of independent interest. Also, we discuss possible extensions of our formulation to randomized schemes and erasure channel, and include a treatment of the related problem of source coding for minimum average queuing delay. T t=1 A(t).This formulation is apt for the timely update problem where the transmitter need not send each update and strives only to reduce the average age of the information at the receiver. 1 Our analysis of average age extends to randomized schemes as well; see Section VI.

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Timeliness in Lossless Block Coding

Cited by 47 publications

References 12 publications

Update or Wait: How to Keep Your Data Fresh

Update or Wait: How to Keep Your Data Fresh

Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source

Optimal Source Codes for Timely Updates

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