Age-optimal constrained cache updating

Yates, Roy D.; Ciblat, Philippe; Yener, Aylin; Wigger, Micheie

doi:10.1109/isit.2017.8006506

Cited by 166 publications

(118 citation statements)

References 8 publications

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“…The AoI metric has been studied in the literature under various settings; mainly through modeling the update system as a queuing system and analyzing the long term average AoI, and through using optimization tools to characterize optimal status update policies, see, e.g., [24][25][26][27][28][29][30][31][32][33][34][35][36], and also the recent survey in [37]. In this work, we employ tools from optimization theory to devise age-minimal online status update policies in systems where sensors are energy-constrained, and rely on energy harvested from nature to transmit status updates.…”

Section: Introductionmentioning

confidence: 99%

Age-Minimal Transmission for Energy Harvesting Sensors With Finite Batteries: Online Policies

Arafa

Yang

Ulukuş

et al. 2020

IEEE Trans. Inform. Theory

190

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An energy-harvesting sensor node that is sending status updates to a destination is considered. The sensor is equipped with a battery of finite size to save its incoming energy, and consumes one unit of energy per status update transmission, which is delivered to the destination instantly over an error-free channel. The setting is online in which the harvested energy is revealed to the sensor causally over time after it arrives, and the goal is to design status update transmission times (policy) such that the long term average age of information (AoI) is minimized. The AoI is defined as the time elapsed since the latest update has reached at the destination. Two energy arrival models are considered: a random battery recharge (RBR) model, and an incremental

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Section: Introductionmentioning

confidence: 99%

Age-Minimal Transmission for Energy Harvesting Sensors With Finite Batteries: Online Policies

Arafa

Yang

Ulukuş

et al. 2020

IEEE Trans. Inform. Theory

190

View full text Add to dashboard Cite

show abstract

“…Session time is T = 19. We apply the change of parameters in Theorem 3 to get new energy arrival times s = [3,7,9,12,15], new transmission delay d = 3, and new session time T = 20. Then, we solve problem (18) to get the optimal inter-update times, using the new parameters.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…We consider another example where energy arrives at times s = [0, 4, 4, 9, 13] ands = [1,3,6,10,12], with T = 16. Applying the change of parameters in Theorem 3 we get T = 17 < (N + 1)d = 18, and hence we use the results of Theorem 1 to get x * = [5,6,6,6,6,3].…”

Section: Numerical Resultsmentioning

confidence: 99%

Timely Updates in Energy Harvesting Two-Hop Networks: Offline and Online Policies

Arafa

Ulukuş

2019

IEEE Trans. Wireless Commun.

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A two-hop energy harvesting communication network is considered, in which measurement updates are transmitted by a source to a destination through an intermediate relay. Updates are to be sent in a timely fashion that minimizes the age of information, defined as the time elapsed since the most recent update at the destination was generated at the source. The source and the relay communicate using energy harvested from nature, which is stored in infinite-sized batteries. Both nodes use fixed transmission rates, and hence updates incur fixed delays (service times). Two problems are formulated:an offline problem, in which the energy arrival information is known a priori, and an online problem, in which such information is revealed causally over time. In both problems, it is shown that it is optimal to transmit updates from the source just in time as the relay is ready to forward them to the destination, making the source and the relay act as one combined node. A recurring theme in the optimal policy is that updates should be as uniformly spread out over time as possible, subject to energy causality and service time constraints. This is perfectly achieved in the offline setting, and is achieved almost surely in the online setting by a best effort policy. I. INTRODUCTIONDeveloping low-latency communication policies is a crucial requirement for next generation communication networks, especially in applications pertaining to real-time status monitoring where information needs to be kept as fresh as possible at interested destinations. The age of information (AoI) metric has been recently introduced in the literature as a suitable performance

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“…• Inspired by [28], [34], [35], in Section IV-(A), we first relax the hard bandwidth constraint (5b) and adopt a sensor level decomposition by using Lagrange multiplier. After relaxation, multiple sensors can be scheduled simultaneously.…”

Section: Problem Formulationmentioning

confidence: 99%

Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

Tang

Wang

Song

et al. 2020

IEEE J. Select. Areas Commun.

137

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This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are Markov time-varying and scheduling decisions are restricted to bandwidth constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) by relaxing the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small AoI performance: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied.

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Age-optimal constrained cache updating

Cited by 166 publications

References 8 publications

Age-Minimal Transmission for Energy Harvesting Sensors With Finite Batteries: Online Policies

Age-Minimal Transmission for Energy Harvesting Sensors With Finite Batteries: Online Policies

Timely Updates in Energy Harvesting Two-Hop Networks: Offline and Online Policies

Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

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