A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

Establishment of energy efficient and reliable data routing in wireless sensor network (WSN) is one of the most critical and challenging task in the recent days. Also, the overall performance and lifetime of WSN is highly depends on the energy level of sensor nodes, hence it is most essential to save the energy of network. For this purpose, the different types of clustering and data aggregation mechanisms are developed in the conventional works, which are focusing on improving both the energy conservation and lifetime of network. Yet, it facing the challenges of increased computational complexity, inefficient routing of data, high controlling overhead, and reduced reliability. Thus, the proposed work objects to develop a novel energy efficient mechanism by integrating the functionalities of advanced clustering, path selection, and data aggregation methodologies. Here, the spider monkey optimization based energy efficient routing protocol is developed for optimally selecting the cluster head (CH) based on certain parameters of energy, distance, and weight value. In this framework, the data transmission is performed between the source to destination nodes through the relay nodes and CHs, which helps to minimize the energy consumption of network. Then, the Classy Bellman-Ford algorithm is deployed for identifying the best paths having shortest distance with the sink nodes. Consequently, an anticipated data aggregation mechanism is utilized for ensuring the security and reliability of data transmission in WSN. For evaluation assessment, various performance metrics have been utilized to validate the results of proposed methodology, and also the obtained values are compared with some other recent state-of-the-art models for proving the betterment of proposed mechanism.