Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm

Generally, human and machine-based query operations can be modified with the use of crowdsourcing. Location-based queries are classified into range and k-nearest neighbor (KNN) queries. Space and point of interest (POI) information can be obtained from both range and KNN queries. In this paper, we expose the trust stage computation of range and KNN query answers with the help of the whale optimization algorithm (WOA). The system chooses either parallel or serial processing, and the experiments are carried out using real-time crowdsourcing. The effectiveness of the proposed concept is evaluated through various consequences such as gang dimension, POI information, space information, and range and KNN query consequences. Each of these effects produces an optimal and reliable result. Finally, the computation time and communication overhead performance of serial and parallel processing are analyzed by examining consequences and production of optimal outcomes.