Answering Multiattribute Top-$k$ Queries in Fog-Supported Wireless Sensor Networks Leveraging Priority Assignment Technology

Abstract

The large-scale and distributed characteristic of multiattribute sensors requires the fog computing paradigm to support location-awareness and latency-sensitive monitoring and query in industrial applications. In these settings, supporting the preference top- $k$ query processing in skewness distribution is a challenge. In this paper, we propose to mitigate the problem of processing a large number of continuous multiattribute (i.e., multidimensional) top- $k$ queries, each with its specific preference, in fog-supported wireless sensor networks. Specifically, a priority-aware index tree is constructed to support the efficient filtering through querying branch nodes according to their top- $k$ result generation probabilities. We have also considered three situations to generate the filter thresholds for the preference user queries. To further eliminate the transmission of invalid thresholds and query results, an enhanced top- $k$ query processing mechanism based on dual transform and K -skyband is developed. Experiments using synthetic dataset and Intel Berkeley Lab dataset show that our proposed approach can have significant improvements in energy efficiency over other reactive methods.