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MALOT

Financiado

Cerrado

Managing Mobility Data Quality for Location of Things

Location of Things (LoT) is an Internet of Things paradigm for mobility analytics. In LoT, massive mobility data is being gathered, processed and transmitted among heterogeneous data nodes in a decentralized architecture. Traditio... ver más

28/07/2022

AAU

219K€

Presupuesto del proyecto: 219K€

Líder del proyecto

AALBORG UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-07-28

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

AAU

219.31K€ | Lider

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Duración del proyecto: 28 meses Fecha Inicio: 2020-03-25
Fecha Fin: 2022-07-28

Líder del proyecto

AALBORG UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 219K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Location of Things (LoT) is an Internet of Things paradigm for mobility analytics. In LoT, massive mobility data is being gathered, processed and transmitted among heterogeneous data nodes in a decentralized architecture. Traditional centralized data quality management techniques cannot cope with such characteristics of LoT, making the management of data quality for LoT a prominent challenge. In the project MALOT, the researcher aims at designing a set of new techniques that are particularly adaptive to the decentralized and heterogeneous LoT architecture for assessing and enhancing mobility data quality. Specifically, the research actions of MALOT include (1) a core model for assessing mobility data quality at decentralized and dynamic data nodes; (2) effective quality-aware data enhancement algorithms to handle the heterogeneity and inconsistency of LoT mobility data; (3) a mechanism for scheduling quality management tasks among relevant nodes in an efficiency-optimal fashion. With the research actions dedicated to decentralized modelling, heterogeneous data integration, and mobile task planning, MALOT will firmly strengthen the researcher's scientific skills and innovative competences. Through many inter-sectoral training and communication activities planned for the project, the researcher will have great opportunities to diversify his skillsets and enhance his future career prospects. A two-way knowledge transfer is guaranteed since MALOT combines the researcher's expertise in mobility analytics and the participating organizations' expertise in big data management and decentralized information systems. Committed to the mobility data quality management for IoT-like architecture, MALOT is not only expected to benefit the academic development of the host and the researcher but will contribute to Europe's IoT innovation and applications.

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