Toward an Effective Locality-Sensitive Hashing Search for WMSNs Based on the Neighborhood Rough Set Approach

With the advent of the 5G era, wireless multimedia sensor networks (WMSNs) will be more widely used in security monitoring, environmental monitoring, intelligent transportation, and other applications of the Internet of Things (IoT). Data collected by WMSNs, part of IoT systems, are high-dimensional, multilevel, unstructured, and large-scale complex data. Effective nearest neighbor searching faces the “dimension curse” problem. To this end, by incorporating the neighborhood rough set (NRS) approach, this article proposes a novel and effective locality-sensitive hashing (LSH) method for high-dimensional WMSN data based on the neighborhood (NLSH). This method innovatively combines the neighborhood knowledge representation method with the LSH mechanism and extends the indexing ability of LSH. Moreover, this method does not require any prior knowledge and has good universality. The indexing method of neighborhood bucket building can reduce the number of searches and improve the response speed of the WMSN system. Extensive experiments have been carried out on several real-world multimedia data sets. The results show that a large-scale high-dimensional NLSH search based on the NRS approach can efficiently query the target point and produce very accurate results. The NLSH algorithm based on the NRS approach outperforms other advanced mainstream LSH algorithms.