Multi-objective African Vultures Optimization for Energy Efficient Wireless Sensor Network

In Wireless Sensor Network (WSN), the Cluster Head (CH) and route path selection are important to reduce data loss and detect node failures. The WSN contains a collection of inexpensive and small nodes which utilized to monitor the various environmental parameters like temperature, wind speed and humidity for desired data. However, the sensor nodes have limited energy so the random deployment of sensors makes problems while managing the resources for efficient data through the network. Integrating clustering and routing strategies are the optimal techniques for saving sensor node energy. Therefore, Multi-objective African Vultures Optimization (MAVO) is proposed to maximize the efficiency of energy in WSN. The optimum Cluster heads from the network are selected and the path through the Cluster Head (CH) is identified by utilizing MAVO. The proposed MAVO decreases the usage of energy nodes when maximizing the data transmission in WSN. The performance of MAVO is evaluated using throughput, packet delivery ratio, delay and energy efficiency by various nodes of 100, 200, 300, 400 and 500. The MAVO model attained a high throughput of 97.5% and packet delivery ratio of 99.2% with less delay of 0.012ms and energy consumption of 5.861J for 100 nodes when compared to other existing methods like Meta-Heuristic Secure and Energy-Efficient Routing protocol (MHSEER) and Low-Energy Adaptive Clustering Hierarchy-Improved Spider Monkey Optimization (LEACH-ISMO).