Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance

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 es...

Full description

Bibliographic Details
Main Authors: A. Checco, C. Lancia, D. J. Leith
Format: Article
Language:English
Published: Hindawi-Wiley 2018-01-01
Series:Wireless Communications and Mobile Computing
Online Access:http://dx.doi.org/10.1155/2018/9028427
id doaj-5b04be187c6e4d1e88ce7170b438632e
record_format Article
spelling doaj-5b04be187c6e4d1e88ce7170b438632e2020-11-25T01:14:46ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772018-01-01201810.1155/2018/90284279028427Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time PerformanceA. Checco0C. Lancia1D. J. Leith2University of Sheffield, Sheffield, UKLeiden University, Leiden, NetherlandsTrinity College Dublin, Dublin, IrelandIn 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.http://dx.doi.org/10.1155/2018/9028427
collection DOAJ
language English
format Article
sources DOAJ
author A. Checco
C. Lancia
D. J. Leith
spellingShingle A. Checco
C. Lancia
D. J. Leith
Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance
Wireless Communications and Mobile Computing
author_facet A. Checco
C. Lancia
D. J. Leith
author_sort A. Checco
title Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance
title_short Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance
title_full Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance
title_fullStr Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance
title_full_unstemmed Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance
title_sort updating neighbour cell list via crowdsourced user reports: a framework for measuring time performance
publisher Hindawi-Wiley
series Wireless Communications and Mobile Computing
issn 1530-8669
1530-8677
publishDate 2018-01-01
description 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.
url http://dx.doi.org/10.1155/2018/9028427
work_keys_str_mv AT achecco updatingneighbourcelllistviacrowdsourceduserreportsaframeworkformeasuringtimeperformance
AT clancia updatingneighbourcelllistviacrowdsourceduserreportsaframeworkformeasuringtimeperformance
AT djleith updatingneighbourcelllistviacrowdsourceduserreportsaframeworkformeasuringtimeperformance
_version_ 1725156689762582528