Search Method for Moving Sensors on a Big Data-based Road Network

Abstract

KNN search, which is a representative location-based query, is used to search the K moving sensors with the closest network distance from the query point by setting the network peripheral object as the query point. Typical examples of the KNN search method in a road network include the IER and INE techniques. However, existing KNN search methods have the disadvantage that the storage space increases as the number of moving sensors increases, and the search time length increases due to the inefficient search process. Therefore, this paper proposes a Middle Point-based QR-tree (MPBQR) based on a QR-tree using the midpoint and an efficient KNN search method that uses MPBQR to solve the problem of existing KNN search methods in road network environments and to support the efficient processing of large capacity sensor data.