Synergistic Intelligence: FCM-MGWO Fusion for Cluster-Based Heterogeneous Sensor Networks

Heterogeneous Sensor Networks (HSNs) have emerged as a prominent and versatile paradigm for various real-world applications, encompassing diverse sensor nodes with varying capabilities and characteristics. The integration of heterogeneous devices with distinct energy levels, computing power, and communication ranges poses unique challenges and opportunities in the design and operation of such networks. This paper presents a novel approach that leverages the Fuzzy C-means (FCM) clustering algorithm for cluster formation and the Modified Grey Wolf Optimization (MGWO) algorithm for Cluster Head (CH) selection in heterogeneous sensor networks. The primary goal of this approach is to optimize node energy consumption and extend the overall network lifespan. The FCM algorithm accommodates the heterogeneity of sensor nodes by allowing data points to belong to multiple clusters with varying degrees of membership. Subsequently, the Modified GWO algorithm, an updated version of GWO, incorporates Levy flight and decay function to balance exploration and exploitation phases effectively. Additionally, we enhance the MGWO fitness function by incorporating four parameters: residual energy, communication distance, connection requests to the node, and maximum communication region. Results revealed that proposed model is outperforming traditional MOD- LEACH, MGEAR and TEZEM models in terms of dead nodes, stability period, instability period and network lifetime to prove its supremacy.