Optimum Energy Efficiency and Age-of-Information Tradeoff in Multicast Scheduling

Nath, Samrat; Wu, Jingxian; Yang, Jing

doi:10.1109/icc.2018.8422521

Cited by 17 publications

(11 citation statements)

References 16 publications

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“…Our work builds upon the pursuit of maintaining energy efficiency through controlling loss of status updates. We also note recent works on energy efficient delay minimization in [23], [24]. In these works, receiver nodes power down under no incoming traffic, and then, wake up for successful reception.…”

Section: Introductionmentioning

confidence: 94%

Active Status Update Packet Drop Control in an Energy Harvesting Node

Rafiee¹,

Özel²

2019

Preprint

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This paper considers an energy harvesting sensor node with battery size Bmax that recharges its battery through an incremental energy harvesting process and receives updates from a single information source in slotted time. The node actively decides to power down (OFF) or up (ON) the communication circuitry for a portion of its operation time in order to maintain energy efficiency. Update packets arriving in ON (OFF) periods are received (discarded). A deterministic energy cost per time is paid during ON periods. The power down decision can be in partial or full nature, yielding various options for deciding ON-OFF intervals. We develop age-threshold based power ON-OFF schemes to minimize age of information at the node subject to energy harvesting constraints with partial and full power down options for Bmax = 1 and Bmax = ∞ cases.

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Section: Introductionmentioning

confidence: 94%

Active Status Update Packet Drop Control in an Energy Harvesting Node

Rafiee¹,

Özel²

2019

Preprint

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“…If device n successfully receives the status update, then the instantaneous AoI is reset to T N (K) when Case C S,1 occurs. In this case, the first and second moments of T N (K) in (31) and (32) are presented in Corollary 1. Corollary 1.…”

Section: B First and Second Moments Of Inter-generation Time X S N Fmentioning

confidence: 99%

“…Corollary 1. The first and second moments of T N (K) conditioning on the occurrence of Case C S,1 is given by 1 , P(C S,1 ), and P(C S,2 ) into (31) and (32), we obtain the first and second moments of inter-generation time X S n for the random deadline case, i.e., E[X S n ] and E X S n 2 .…”

Section: B First and Second Moments Of Inter-generation Time X S N Fmentioning

confidence: 99%

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Age of Information for Multicast Transmission With Fixed and Random Deadlines in IoT Systems

Zhou

Chen

2020

IEEE Internet Things J.

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In this paper, we consider the multicast transmission of a real-time Internet of Things (IoT) system, where an access point (AP) transmits time-stamped status updates to multiple IoT devices. Different from the existing studies that only considered multicast transmission without deadlines, we enforce a deadline for the service time of each multicast status update, taking into account both the fixed and randomly distributed deadlines. In particular, a status update is dropped when either its deadline expires or it is successfully received by a certain number of IoT devices. Considering deadlines is important for many emerging IoT applications, where the outdated status updates are of no use to IoT devices. We evaluate the timeliness of the status update delivery by applying a recently proposed metric, named the age of information (AoI), which is defined as the time elapsed since the generation of the most recently received status update. After deriving the distributions of the service time for all possible reception outcomes at IoT devices, we manage to obtain the closed-form expressions of both the average AoI and the average peak AoI. Simulations validate the performance analysis, which reveals that the multicast transmission with deadlines achieves a lower average AoI than that without deadlines and there exists an optimal value of the deadline that can minimize the average (peak) AoI. Results also show that the fixed and random deadlines have respective advantages in different deadline regimes.Index Terms-Age of information, fixed deadline, randomly distributed deadline, multicast transmission, information freshness.J. Li is with the

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“…The age of information has been analyzed for several queueing models, e.g., [1,[4][5][6][7]; meanwhile, scheduling for minimizing the average age of information has also been explored, e.g., [8][9][10][11]. Moreover, some works investigated the age-energy tradeoff (with or without energy harvesting) in various scenarios, e.g., [3,[12][13][14]. Despite many works on the scheduling design or on the age-energy tradeoff, their design and analysis were based on stochastic models with some stationary assumptions but cannot be applied in nonstationary settings.…”

Section: B Related Workmentioning

confidence: 99%

Online Energy-Efficient Scheduling for Timely Information Downloads in Mobile Networks

Tseng

Hsu

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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We consider a mobile network where a mobile device is running an application that requires timely information. The information at the device can be updated by downloading the latest information through neighboring access points. The freshness of the information at the device is characterized by the recently proposed age of information. However, minimizing the age of information by frequent downloading increases power consumption of the device. In this context, an energyefficient scheduling algorithm for timely information downloads is critical, especially for power-limited mobile devices. Moreover, unpredictable movement of the mobile device causes uncertainty of the channel dynamics, which is even non-stationary within a finite amount of time for running the application. Thus, in this paper we devise a randomized online scheduling algorithm for mobile devices, which can move arbitrarily and run the application for any amount of time. We show that the expected total cost incurred by the proposed algorithm, including an age cost and a downloading cost, is (asymptotically) at most e/(e − 1) ≈ 1.58 times the minimum total cost achieved by an optimal offline scheduling algorithm.Index Terms-Age of information, scheduling algorithms, competitive online algorithms.

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Optimum Energy Efficiency and Age-of-Information Tradeoff in Multicast Scheduling

Cited by 17 publications

References 16 publications

Active Status Update Packet Drop Control in an Energy Harvesting Node

Active Status Update Packet Drop Control in an Energy Harvesting Node

Age of Information for Multicast Transmission With Fixed and Random Deadlines in IoT Systems

Online Energy-Efficient Scheduling for Timely Information Downloads in Mobile Networks

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