Energy Efficient Routing in IoT Network Using Non-Convergence Factor Based Northern Goshawk Optimization Algorithm

The Internet of Things (IoT) is a network that enables users to connect with various objects and devices via the Internet, driving the development of Wireless Sensor Networks (WSN) applications.However, existing optimization-based methods for minimizing node energy using clustering techniques often fail to maintain a balanced energy distribution due to frequent network changes and uneven energy consumption.To address these limitations, this research proposes a Non-Convergence Factor based Northern Goshawk Optimization (NCF-NGO) method for energy-efficient Cluster Head (CH) selection and routing in IoT networks.The keen vision and random wide exploration behavior of the NGO birds enhance the search for optimal and energy-efficient CHs in homogeneous IoT networks.The proposed NCF strategy tackles premature convergence in the NGO algorithm, effectively selecting optimal CHs and routing paths for efficient data transmission.Furthermore, the NCF strategy improves the balance between exploration and exploitation, thus extending the network lifetime.Experimental results show that the proposed NCF-NGO method outperforms the existing EOR-IABC, achieving lower energy consumption of 6 J and 13 J for 10 and 40 rounds, respectively.