Adaptive Density-Based Localization Algorithm Using Particle Swarm Optimization and DBSCAN Clustering Approach (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Wireless Sensor Network (WSN) is a self-directed distributed wireless system independent, low-cost, power deficient. Localization is an important requirement in WSN for chasing and investigating identified data. In maximum appliances of WSN, the data without its area data has no importance. Provided the hardware restrictions and physical atmosphere where the sensors ought to function, along with recurrent alternations in-network prototype and its density, the algorithm needs to be developed to attain a vigorous and energy effective communicating methodology. In this regard, this paper suggested an algorithm for the adaptive behavior of wireless sensor networks. The complete methodology is implemented in two modules i.e. clustering the complete wireless network depending on the density using the Density-based spatial clustering of applications with noise (DBSCAN)approach and estimating the un-localized nodes within each cluster using Particle Swarm Optimization (PSO) based location estimation algorithm. The performance of the suggested methodology is supported using NS2 Simulator. The results inferred that the proposed methodology has a superior packet delivery ratio, advanced energy efficiency, network throughput, and less data packet ratio relative to present localization approaches.