Fog-Based Pub/Sub Index With Boolean Expressions in the Internet of Industrial Vehicles

Abstract

Structured publish/subscribe (pub/sub) is a promising technique adopted on kinds of vehicle applications of Internet of industrial vehicles (IoIV), which uses Boolean expressions to capture the items with thousands of different attributes, values and spatial locations, and then processes and analyzes the vast amounts of data collected to obtain users' interests. However, existing pub/sub work with Boolean expressions either ignores spatial requirement or focuses on Euclidean space. This paper aims to fill this gap by addressing the issue of fog-based spatial-textual pub/sub problem with Boolean expressions in IoIV. A novel hybrid index called RnetBE is proposed, which exquisitely organizes traffic network structure, Boolean expressions, and spatial information of subscriptions. And RnetBE can prune huge numbers of unqualified subscriptions based on both spatial constraint and Boolean expressions, thus achieving high efficiency in indexing and matching. Moreover, range-tree deletion and orderly group processing optimization techniques are proposed to save storage space and further improve the subscription pruning efficiency. Simulation results show that RnetBE and the proposed algorithm are efficient in terms of memory consumption and matching time.