ESCAPE: Effective Scalable Clustering Approach for Parallel Execution of Continuous Position-Based Queries in Position Monitoring Applications

Massive data sets of continuous position-based queries (CPQs) in position monitoring applications offer a challenge of time ingestion while forwarding the position-based data within wireless search space area. As a result of recurrent modifications in network topology due to the mobility of users, processing of large CPQ data sets and roaming CPQs is one of the challenges in position monitoring. Therefore, the parallel algorithm is proposed in this paper for clustering and parallel processing of roaming CPQs which will recognize solid clusters in the wireless search space area. We present an algorithm which proposes the use of search space areas for clustering and introduce a parallel framework for parallel processing of CPQ data sets. The wireless search space area from wireless networks are used for scalable and effective calculation of clusters and dimensions in wireless networks. Present continuous query processing techniques cannot competently process roaming CPQs in the wireless search space area. The proposed algorithm is demonstrated to have practically best speedups in processing roaming CPQs. Results indicate that the proposed work determine improved ability of CPQs over existing mechanisms and attain small query latency and high precision in position monitoring applications.