Checco, A. orcid.org/0000-0002-0981-3409, Lancia, C. and Leith, D.J. (2018) Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance. Wireless Communications and Mobile Computing, 2018. 9028427. ISSN 1530-8669
Abstract
In modern wireless networks deployments, each serving node needs to keep its Neighbour Cell List (NCL) constantly up to date to keep track of network changes. The time needed by each serving node to update its NCL is an important parameter of the network’s reliability and performance. An adequate estimate of such parameter enables a significant improvement of self-configuration functionalities. This paper focuses on the update time of NCLs when an approach of crowdsourced user reports is adopted. In this setting, each user periodically reports to the serving node information about the set of nodes sensed by the user itself. We show that, by mapping the local topological structure of the network onto states of increasing knowledge, a crisp mathematical framework can be obtained, which allows in turn for the use of a variety of user mobility models. Further, using a simplified mobility model we show how to obtain useful upper bounds on the expected time for a serving node to gain Full Knowledge of its local neighbourhood.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 A. Checco et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Feb 2018 16:18 |
Last Modified: | 23 Feb 2018 16:18 |
Published Version: | https://doi.org/10.1155/2018/9028427 |
Status: | Published |
Publisher: | Hindawi Publishing Corporation |
Refereed: | Yes |
Identification Number: | 10.1155/2018/9028427 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127738 |