Updating Strategies in the Internet of Things by Taking Advantage of Correlated Sources

Hribar, Jernej; Costa, Maice; Kaminski, Nicholas J.; DaSilva, Luiz A.

doi:10.1109/glocom.2017.8255056

Cited by 17 publications

(14 citation statements)

References 11 publications

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“…Assuming perfect knowledge of the server state, the authors in [54] look at the optimal rate control policy at the transmitter and illustrate when it is preferable to wait for a certain amount of time before generating a new update, instead of generating an update as soon as the previous update finishes service [75]. The problem of identifying the optimal sampling strategy of a communication system in relation to AoI, has attracted particular attention due to the connection it implies with estimation errors [76,77,78,79]. In [80] the authors formulate a dynamic programming problem and utilize the framework of Markov decision processes (MDP) to study the interaction of heterogeneous traffic flows in a shared queue.…”

Section: Optimization and Controlmentioning

confidence: 99%

Age of Information Aware Communication Systems : Modeling and Performance Analysis

Kosta

2020

Linköping Studies in Science and Technology. Dissertations

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Section: Optimization and Controlmentioning

confidence: 99%

Age of Information Aware Communication Systems : Modeling and Performance Analysis

Kosta

2020

Linköping Studies in Science and Technology. Dissertations

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“…In [47], [48], the authors propose various effective age metrics to minimize the estimation/prediction error. Exploring the correlation among multiple sources has been studied in [49], [50]. For a more broad view of the works on AoI we direct the reader to our summary article in [51].…”

Section: Introductionmentioning

confidence: 99%

The Cost of Delay in Status Updates and Their Value: Non-Linear Ageing

Kosta

Παππάς

Ephremides

et al. 2020

IEEE Trans. Commun.

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We consider a status update communication system consisting of a source-destination link. A stochastic process is observed at the source, where samples are extracted at random time instances, and delivered to the destination, thus, providing status updates for the source. In this paper, we expand the concept of information ageing by introducing the cost of update delay (CoUD) metric to characterize the cost of having stale information at the destination. The CoUD captures the freshness of the information at the destination and can be used to reflect the information structure of the source. Moreover, we introduce the value of information of update (VoIU) metric that captures the reduction of CoUD upon reception of an update. Using the CoUD, its by-product metric called peak cost of update delay (PCoUD), and the VoIU, we evaluate the performance of an M/M/1 system in various settings that consider exact expressions and bounds. Our results indicate that the performance of CoUD differs depending on the cost assigned per This work extends the preliminary study in [1]. A. Kosta, N. Pappas, A. Ephremides, and V. Angelakis are with the

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“…It describes the timeliness of information at the receiver, see e.g. [1], [2]. Common to most AoI scenarios is that the information should be delivered in a timely manner, and usually only the most recent information is of interest.…”

Section: Introductionmentioning

confidence: 99%

“…Cameras with overlapping scenes are connected to the same destination nodes, which delay the processing until frames from all cameras that capture a specific scene have been received. A scenario where two sensors observe correlated Gaussian processes is presented in [2]. The two sources sample and transmit updates periodically at fixed (but possibly different) intervals, and the authors study the estimation error resulting from various update strategies.…”

Section: Introductionmentioning

confidence: 99%

“…The two sources sample and transmit updates periodically at fixed (but possibly different) intervals, and the authors study the estimation error resulting from various update strategies. The system in [2] differs from our system model as we treat centralized scheduling with arbitrary scheduling intervals, as well as discrete updates rather than a continuous process.…”

Section: Introductionmentioning

confidence: 99%

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Minimizing the Age of Information From Sensors With Common Observations

Kalør

Popovski

2019

IEEE Wireless Commun. Lett.

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We study the average Age of Information (AoI) in a system where physical sources produce independent discretetime updates that are each observed by several sensors. We devise a model that is simple, but still capable to capture the main tradeoffs. Two sensor scheduling policies are proposed to minimize the AoI of the sources; one in which the system parameters are assumed known, and one in which they are learned. Both policies are able to exploit the common sensor information to reduce the AoI, resulting in large reductions in AoI compared to common schedules.

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Updating Strategies in the Internet of Things by Taking Advantage of Correlated Sources

Cited by 17 publications

References 11 publications

Age of Information Aware Communication Systems : Modeling and Performance Analysis

Age of Information Aware Communication Systems : Modeling and Performance Analysis

The Cost of Delay in Status Updates and Their Value: Non-Linear Ageing

Minimizing the Age of Information From Sensors With Common Observations

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