A Cost-Effective Distributed Framework for Data Collection in Cloud-based Mobile Crowd Sensing Architectures
Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | IMDEA Networks Institute |
| Repositorio: | IMDEA Networks Institute Digital Repository |
| Idioma: | inglés |
| OAI Identifier: | oai:dspace.networks.imdea.org:20.500.12761/342 |
| Acceso en línea: | http://hdl.handle.net/20.500.12761/342 https://dx.doi.org/https://doi.org/10.1109/TSUSC.2017.2666043 |
| Access Level: | acceso abierto |
| Palabra clave: | Mobile crowd sensing energy-efficient data collection opportunistic sensing |
| Sumario: | Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis on the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns. |
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