Sociability-Driven Framework for Data Acquisition in Mobile Crowdsensing over Fog Computing Platforms for Smart Cities
Smart cities exploit the most advanced information technologies like Internet of Things to improve and add value to existing public services. Having citizens involved in the process through mobile crowdsensing (MCS) techniques augments the capabilities of the platform without additional costs. In th...
| Autores: | , , , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2017 |
| País: | España |
| Recursos: | IMDEA Networks Institute |
| Repositório: | IMDEA Networks Institute Digital Repository |
| Idioma: | inglês |
| OAI Identifier: | oai:dspace.networks.imdea.org:20.500.12761/397 |
| Acesso em linha: | http://hdl.handle.net/20.500.12761/397 https://dx.doi.org/doi:10.1109/TSUSC.2017.2702060 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Data acquisition fog computing internet of things mobile crowdsensing smart city sensing sustainability |
| Resumo: | Smart cities exploit the most advanced information technologies like Internet of Things to improve and add value to existing public services. Having citizens involved in the process through mobile crowdsensing (MCS) techniques augments the capabilities of the platform without additional costs. In this paper, we propose a novel framework for data acquisition in MCS deployed over a fog computing platform which facilitates important operations like user recruitment and task completion. Proper data acquisition minimizes the monetary expenditure the platform sustains to recruit and compensate users and the energy they spend to sense and deliver data. We propose a new user recruitment policy called DSE (Distance, Sociability, Energy). The policy exploits three criteria: i) spatial distance between users and tasks, ii) user sociability, which is an estimate of the willingness of users to contribute to sensing tasks, and iii) remaining battery charge the devices. Performance evaluation is conducted in a real urban environment for a large number of participants with new metrics that assess the efficiency of the recruitment policy and the accuracy of task completion. Results reveal that the average number of recruited users improves by nearly 20% if compared to policies using only spatial distance as selection criterion. |
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