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Horizontal Integrated Framework for Mobile Crowdsensing
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AI
DOI：10.1109/ACCESS.2021.3112272.png)
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Abstract
En 中文
Mobile crowdsensing is a promising paradigm to leverage the power of people to collect large-scale spatially distributed data. This concept has been intensely studied to efficiently and securely complete sensing tasks at lower cost. The development of a unified platform designed to provide various types of sensing applications is among the major approaches to economical crowdsourcing. However, existing previous frameworks were not optimized for shared use among multiple organizers because they were largely vertically integrated systems. Security and user trust and confidence is also a significant issue a crowdsensing frameworks, given the potential security concerns. Therefore, in this study, we propose a network-side task allocation (NeSTA) framework to address the existing problems in mobile crowdsensing. The proposed framework enables the horizontal integration of sensing applications, in which mobile networks mediate communication among organizers and participants, significantly reducing the installation cost of individual applications. Privacy preservation is achieved by task distribution and allocation procedures, where the participants were obscured by organizers. The validity of the proposed NeSTA was confirmed by simulations with an analytical model using an open dataset. The results show that the proposed method exhibited computational efficiency over two orders of magnitude greater than the conventional approach. This advantage originates from the reduction of problem size by dividing the original problem into subproblems.
Keywords:
Crowdsourcing
mobile applications
mobile communication
mobile computing
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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