Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/89945
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Lima, Emanuel Ribeiro | por |
dc.contributor.author | Aguiar, Ana | por |
dc.contributor.author | Carvalho, Paulo | por |
dc.contributor.author | Viana, Aline Carneiro | por |
dc.date.accessioned | 2024-03-25T11:42:04Z | - |
dc.date.available | 2024-03-25T11:42:04Z | - |
dc.date.issued | 2022-06 | - |
dc.identifier.citation | Lima, E., Aguiar, A., Carvalho, P., & Viana, A. C. (2022, June). Human Mobility Support for Personalized Data Offloading. IEEE Transactions on Network and Service Management. Institute of Electrical and Electronics Engineers (IEEE). http://doi.org/10.1109/tnsm.2022.3153804 | - |
dc.identifier.issn | 1932-4537 | por |
dc.identifier.uri | https://hdl.handle.net/1822/89945 | - |
dc.description.abstract | WiFi Access Points (APs) can be used to offload data or computation tasks while users are commuting. However, due to APs' limited coverage, offloading performance is heavily impacted by the users' mobility. This work proposes to leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We define Offloading Regions (ORs) as areas where a commuter's mobility would enable offloading, and propose an unsupervised learning methodology to extract ORs from mobility traces. Then, we characterise and analyse ORs according to offloading opportunity metrics such as type, availability, total time to offload, and offloading delay. Results show that in 50% of the trips, users spend more than 48% of the travel time inside ORs extracted according to the proposed methodology. The ability to predict the next ORs would benefit offloading orchestration. Offloading mobility predictability, although crucial, proves to be challenging, expressed by the poor predictive performance of well-known models (approximate to 37% acc. for the best predictor). We show that mobility regularity proper- ties improve predictive performance up to approximate to 35%. Finally, we look into the impact of further OR extraction and prediction parameters. We show that the exploration phase length does not impact the discovery of low relevance ORs, and that both filtering low relevance OR and predicting multiple ORs increase predictability. By characterising the trade-off between mobility predictability and offloading opportunities in transit, we highlighting the need for offloading systems to adopt hybrid strategies, i.e., mixing opportunistic and predictive strategies. The conclusions and findings on offloading mobility properties are likely to generalise for varied urban scenarios given the high degree of similarity between the results obtained for the two different and independently collected mobility datasets. | por |
dc.description.sponsorship | - This work is a result of the project FLOYD (POCI-010247-FEDER-045912), funded by the European Regional Development Fund (FEDER), through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) and by National Funds (OE), through Fundacao para a Ciencia e Tecnologia, I.P.; and UIDB/50008/2020 funded by the applicable financial framework (FCT/MCTES) (PIDDAC). It also has been supported by FCT-Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and is related to the Hi!Paris project entitled "AI for More Accessible Cities." | por |
dc.language.iso | eng | por |
dc.publisher | IEEE | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT | por |
dc.rights | openAccess | por |
dc.subject | Data offloading | por |
dc.subject | Human mobility | por |
dc.subject | Mobility predictability | por |
dc.subject | Offloading mobility properties | por |
dc.subject | Offloading systems | por |
dc.title | Human mobility support for personalized data offloading | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9718527 | por |
oaire.citationStartPage | 1505 | por |
oaire.citationEndPage | 1520 | por |
oaire.citationIssue | 2 | por |
oaire.citationVolume | 19 | por |
dc.date.updated | 2024-03-17T17:02:03Z | - |
dc.identifier.eissn | 1932-4537 | - |
dc.identifier.doi | 10.1109/TNSM.2022.3153804 | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 13524 | - |
sdum.journal | IEEE Transactions on Network and Service Management | por |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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On_the_Properties_of_Human_Mobility_for_Mobile_Data_Offloading_Short_Version.pdf | 25,02 MB | Adobe PDF | Ver/Abrir |