Details of Research Outputs

Vehicles in urban city are equipped with more and more sensing units, and sensed data are continuously generated in large amount. These sensed data could be filtered and preprocessed before being shared or uploaded to the road side units and the cloud for efficiency. In this paper we propose a filter-based framework called FERA (Filter-based Efficient Request Answering), which combines the concept of fog computing and vehicular sensing, and adopts the pull/push strategies to adaptively and efficiently gather the requested data in vehicular ad hoc networks. Filters are defined based on the ratio of cost between the push and the pull methods to control the passage or blockage of the data readings. Moreover, filter cubes are defined to manage large number of filters, where efficient algorithms are developed to construct, update and store the filter cubes so that the matched data readings are pushed upward and unmatched data readings are blocked effectively. Extended simulated experiments demonstrate the proposed scheme has a much higher success ratio of request answering than existing schemes, e.g. REED (Abadiet al., 2005) and GeoVanet (Delotet al., 2011). Up to 94 percent of the requests could be successfully processed, while at the same time maintaining a relatively low query cost.