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Age of Information of a Dual Queue Status Update System: A Stochastic Hybrid Systems Method
Chongqing Univ, Peoples R China.
Chongqing Univ, Peoples R China.
Univ Illinois, IL USA; Zhejiang Univ, Peoples R China.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4416-7702
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2023 (English)In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 27, no 7, p. 1714-1718Article in journal (Refereed) Published
Abstract [en]

Using redundant devices to update the status can improve the robustness against transmission failure, thus improving timeliness of information. However, out of order update arrivals resulting from multiple devices impose a significant challenge to the analysis of timeliness of information in such systems. This letter studies the average age of information (AoI) of a dual queue status update system under zero-wait policy. We leverage tools from stochastic hybrid systems to derive closed-form expression for the average AoI of the dual queue system and extend the result to the three-queue system. The results show that the average AoI of the dual queue system can be reduced by 37.5% compared to that using only a single queue.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2023. Vol. 27, no 7, p. 1714-1718
Keywords [en]
Status update system; age of information; stochastic hybrid systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-197583DOI: 10.1109/LCOMM.2023.3274348ISI: 001029234900012OAI: oai:DiVA.org:liu-197583DiVA, id: diva2:1795957
Note

Funding Agencies|National Natural Science Foundation of China [62271092, 62001074, 62271513]; Chongqing Postdoctoral Special Research Program [2022XM2006]; Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory [GXKL06230206]; Zhejiang Provincial Natural Science Foundation of China [LGJ22F010001]; Zhejiang-Singapore Innovation and AI Joint Research Lab; Swedish Research Council (VR); ELLIIT; European Union [101096526]

Available from: 2023-09-11 Created: 2023-09-11 Last updated: 2023-09-11

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