Proposing New Method for Clustering and Optimizing Energy Consumption in WSN
Due to the variety of applications of sensor networks as well as the importance of multiple ports or sinks to receive valuable data as well as security issues and energy consumption constraints in these networks, problem-solving of multi-sink sensor networks is of potential importance. The optimal solution to this problem can have a significant impact on the functioning of these networks. In this study, we attempt to solve the problem of multi-sink sensor network partitioning, which results in improved network efficiency by reducing energy consumption. To achieve this goal, the proposed approach focuses on two criteria: the number of headers and the number of jumps required to send packets to the sink, because reducing these two criteria, in addition to maintaining network balance, will result in reduced energy consumption and thus increase the lifetime of the sensor network. The purpose of this paper is to present an efficient model for wireless sensor networks using node variable number scenario based on the particle swarm algorithm. In the proposed method based on the particle swarm algorithm, the beads are first identified for each particle. It then identifies its adjacent nodes by considering the distance between the nodes and the neighboring radius. Then, the partitioning of the beads between the sinks is dealt with, and each particle provides a unique partition. In the meantime, particles with better separation will have the most impact on the continuity of particle motions until eventually the best solution is accepted as the final fraction. Based on the results of the research scenario, the proposed method for the number of jumps compared to the base method is up to 24% and for the number of headings up to 48%.