Fog-Computing-Based Approximate Spatial Keyword Queries With Numeric Attributes in IoV

Abstract

Due to the popularity of onboard geographic devices, a large number of spatial–textual objects are generated in the Internet of Vehicles (IoV). This development calls for approximate spatial keyword queries with numeric attributes in IoV (A2SKIV), which takes into account the locations, textual descriptions, and numeric attributes of spatial–textual objects. Considering large amounts of objects involved in the query processing, this article comes up with the idea of utilizing vehicles as fog-computing resource and proposes the network structure called FCV, and based on which the fog-based top- $k~\text{A}^{2}$ SKIV query is explored and formulated. In order to effectively support network distance pruning, textual semantic pruning, and numerical attribute pruning, simultaneously, a two-level spatial–textual hybrid index STAG-tree is designed. Based on STAG-tree, an efficient top- $k~\text{A}^{2}$ SKIV query processing algorithm is presented. The simulation results show that our STAG-based approach is about $1.87\times $ ( $17.1\times $ , resp.) faster in search time than the compared ILM (DBM, resp.) method, and our approach is scalable.