The vehicular fog is a relatively new computing paradigm where fog computing works with the vehicular network. It provides computation, storage, and location-aware services with low latency to the vehicles in close proximity. A vehicular fog network can be formed on-the-fly by adding underutilized or unused resources of nearby parked or moving vehicles. Interested vehicles can outsource their resources or data by being added to the vehicular fog network while maintaining proper security and privacy. Client vehicles can use these resources or services for performing computation-intensive tasks, storing data, or getting crowdsource reports through the proper secure and privacy-preserving communication channel. As most vehicular network applications are latency and location sensitive, fog is more suitable than the cloud because of the capability of performing calculations with low latency, location awareness, and the support of mobility. Architecture, security, and privacy models of vehicular fog are not well defined and widely accepted yet as it is in its early stage. In this paper, we have analyzed existing studies on vehicular fog to determine the requirements and issues related to the architecture, security, and privacy of vehicular fog computing. We have also identified and highlighted the open research problems in this promising area.
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Privacy-aware Task Allocation and Data Aggregation in Fog-assisted Spatial Crowdsourcing
Spatial crowdsourcing (SC) enables task owners (TOs) to outsource spatial-related tasks to a SC-server who engages mobile users in collecting sensing data at some specified locations with their mobile devices. Data aggregation, as a specific SC task, has drawn much attention in mining the potential value of the massive spatial crowdsensing data. However, the release of SC tasks and the execution of data aggregation may pose considerable threats to the privacy of TOs and mobile users, respectively. Besides, it is nontrivial for the SC-server to allocate numerous tasks efficiently and accurately to qualified mobile users, as the SC-server has no knowledge about the entire geographical user distribution. To tackle these issues, in this paper, we introduce a fog-assisted SC architecture, in which many fog nodes deployed in different regions can assist the SC-server to distribute tasks and aggregate data in a privacy-aware manner. Specifically, a privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption. PTAA supports representative aggregate statistics (e.g.,sum, mean, variance, and minimum) with efficient data update while providing strong privacy protection. Security analysis shows that PTAA can achieve the desirable security goals. Extensive experiments also demonstrate its feasibility and efficiency.
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- NSF-PAR ID:
- 10096002
- Date Published:
- Journal Name:
- IEEE Transactions on Network Science and Engineering
- ISSN:
- 2334-329X
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TNSE.2019.2892583
