A Markovian Model for the Analysis of Age of Information in IoT Networks

Abbas, Q. and Hassan, S.A. and Pervaiz, H. and Ni, Q. (2021) A Markovian Model for the Analysis of Age of Information in IoT Networks. IEEE Wireless Communications Letters, 10 (7). pp. 1596-1600. ISSN 2162-2337

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Abstract

Age of Information (AoI) is a critical metric in status update systems as these systems require the fresh updates. This paper investigates the uplink of an Internet-of-thing (IoT) network where L nodes transmit their information packets to a base station. The effects of the arrival rate of packets at the nodes, the number of nodes in the system, and queue length of each node have been studied by devising a discrete time Markov chain (MC) model. This model helps in predicting the values of AoI and probability of packet drops in such systems. The notion of first-in first-out is used for queuing, which transmits the oldest packet first, resulting in decreasing the overall AoI of the system. The results show that AoI increases with the increase in queue length, number of nodes and arrival rate and we quantify the aforementioned metrics using the MC model.The results found using the MC model are also validated using extensive simulations. IEEE

Item Type:

Journal Article

Journal or Publication Title:

IEEE Wireless Communications Letters

Additional Information:

©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/2200/2207

Subjects:

?? age of informationagricultureanalytical modelsarrival rateinternet-of-thingmarkov chainmarkov processesmeasurementqueueing analysisqueuing theory.real-time systemsuplinkmarkov chainsqueueing theorydiscrete time markov chainsextensive simulationsfirst in f ??

Departments:

Faculty of Science and Technology > School of Computing & Communications

ID Code:

154948

Deposited By:

ep_importer_pure

Deposited On:

17 May 2021 13:10

Refereed?:

Yes

Published?:

Published