Data aggregation in wireless sensor networks using Eurasian oystercatcher optimizer algorithm and SVM.

Huge number of nodes of sensor make wireless sensor networks (WSNs) that not just gather data associated with various happenings but also submit those events to central station hop-by-hop. Data submitted through nodes of neighbor may be extra as well as duplicate. In addition, generated data number in wide WSNs might be a lot to be processed in sink. The most essential variable for gathering WSNs data is optimum energy usage to integrate also gather data. The accuracy of the information is usually assessed by detecting the error of the data contained in the CHs. This process helps increase the integrity and reliability of the WSN application. Basically, data identification is a classification issue. Support Vector Machine (SVM) is one of the most common classification approaches used in this paper to create an identification model. The SVM classification parameters will also be optimized by the Eurasian oystercatcher optimizer algorithm (EOO). The simulation results show that the proposed method is better than the basic paper in terms of 5 specific criteria. The proposed method has improved by about 1.02% compared to the other methods. [ABSTRACT FROM AUTHOR]