Age-Minimal Transmission in Energy Harvesting Two-Hop Networks

Arafa, Ahmed; Ulukuş, Şennur

doi:10.1109/glocom.2017.8254156

Cited by 125 publications

(71 citation statements)

References 27 publications

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“…Authors in [37] focused on a line network with one sensor, one destination, and multiple relay nodes, and studied the effect of preemption on the average AoI at each node. The energy and data causality constraints in a two-hop network were considered in [38], and the optimal scheduling policy was proposed to minimize the total AoI of a session. Considering multi-hop networks and utilizing graph theory, the optimal scheduling policies for the networks with and without pre-defined source/destination pairs were investigated in [39] and [40], respectively.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Information Freshness in Computing-Enabled IoT Networks

Yang

Wang

et al. 2020

IEEE Internet Things J.

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Internet of Things (IoT) has emerged as one of the key features of the next generation wireless networks, where timely delivery of status update packets is essential for many realtime IoT applications. To provide users with context-aware services and lighten the transmission burden, the raw data usually needs to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this work, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue, and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a minmax optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and effectiveness of the proposed algorithm are verified with extensive simulation results.Index Terms-Internet of things, information freshness, peak age of information, data preprocessing, derivative-free optimization.

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Section: Related Workmentioning

confidence: 99%

Optimizing Information Freshness in Computing-Enabled IoT Networks

Yang

Wang

et al. 2020

IEEE Internet Things J.

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“…Age of type I updates at an individual node is given by (8). However, type I update interarrival to a node, S I , is now equal to S I = X k1:n + Z + j+M1−1 (9).…”

Section: B Exogenous Update Arrivalsmentioning

confidence: 99%

Age of Information in Multicast Networks with Multiple Update Streams

Buyukates

Soysal

Ulukuş

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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We consider the age of information in a multicast network where there is a single source node that sends timesensitive updates to n receiver nodes. Each status update is one of two kinds: type I or type II. To study the age of information experienced by the receiver nodes for both types of updates, we consider two cases: update streams are generated by the source node at-will and update streams arrive exogenously to the source node. We show that using an earliest k1 and k2 transmission scheme for type I and type II updates, respectively, the age of information of both update streams at the receiver nodes can be made a constant independent of n. In particular, the source node transmits each type I update packet to the earliest k1 and each type II update packet to the earliest k2 of n receiver nodes. We determine the optimum k1 and k2 stopping thresholds for arbitrary shifted exponential link delays to individually and jointly minimize the average age of both update streams and characterize the pareto optimal curve for the two ages.

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“…5 presents J * (s 0 ) as a function of P e , for different values of the transmission success probability P s . In this set of experiments, P z is the matrix P z defined in (14) and E max was set to 5. Fig.…”

Section: Resultsmentioning

confidence: 99%

Control of Status Updates for Energy Harvesting Devices That Monitor Processes with Alarms

Stamatakis

Παππάς

Traganitis

2019

2019 IEEE Globecom Workshops (GC Wkshps)

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In this work, we derive optimal transmission policies in an energy harvesting status update system. The system monitors a stochastic process which can be either in a normal or in an alarm state of operation. We capture the freshness of status updates for each state of the stochastic process by introducing two Age of Information (AoI) variables and extend the definition of AoI to account for the state changes of the stochastic process. We formulate the problem at hand as a Markov Decision Process which, under the assumption that the demand for status updates is higher when the stochastic process is in the alarm state, utilizes a transition cost function that applies linear and nonlinear penalties based on AoI and the state of the stochastic process. Finally, we evaluate numerically the derived policies and illustrate their effectiveness for reserving energy in anticipation of future alarm states.

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Age-Minimal Transmission in Energy Harvesting Two-Hop Networks

Cited by 125 publications

References 27 publications

Optimizing Information Freshness in Computing-Enabled IoT Networks

Optimizing Information Freshness in Computing-Enabled IoT Networks

Age of Information in Multicast Networks with Multiple Update Streams

Control of Status Updates for Energy Harvesting Devices That Monitor Processes with Alarms

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