## 2 Related Work
[49], have sought to address heterogeneities in computational power, communication link quality, and node mobility. Moreover, [PERSON] et al. also developed an energy efficient heterogeneous DEEC protocol [50], which introduces three-level heterogeneity. However, these protocols frequently encounter issues such as inefficient load distribution, increased overhead due to frequent cluster reformation, and vulnerability to data loss when cluster-head communication is interrupted. The introduction of more sophisticated protocols, such as multi-level HEED (ML-HEED) [51], which supports up to six levels of energy heterogeneity, demonstrates the potential for improved network lifespan and energy usage. However, the frequent need for cluster reformation at the start of each round consumes significant energy, which could otherwise be allocated to more critical network operations. Recent protocols like the improved energy efficient clustering protocol (IEECP) by [PERSON] et al. [6] and hybrid routing approaches proposed by [PERSON] et al. [38] and [PERSON] et al. [36] focus on prolonging network lifetime through optimised cluster formation and the use of relay nodes for long-distance communication. While these methods represent valuable progress, they are often limited by their focus on homogeneous networks or their application to only two levels of energy heterogeneity, restricting their effectiveness in more complex IoT-based HWSNs. The authors in [39] introduced a hybrid routing approach to enhance energy efficiency in sensor networks, encompassing both normal and advanced nodes. This method involves segmenting the network into smaller regions and deploying relay nodes to optimise long-distance communication energy. Routing is performed between nodes and the base station, cluster heads, relays, and relays to the base station. However, the scheme restricts advanced and normal nodes to their predefined regions, limiting its applicability and only considering two levels of energy heterogeneity. Moreover, innovations like the threshold-oriented and energy-harvesting enabled multi-level stable election protocol (TEM-SEP) [52] and the non-threshold-based cluster-head rotation scheme (NCHRA) [22] are indicative of the ongoing efforts to support large-scale, heterogeneous networks. However, these protocols still primarily address energy heterogeneity and often neglect other critical parameters, such as data rate variability and computational capabilities, which are essential for efficient network management in real-life IoT deployments. Finally, [PERSON] et al. [40] and [PERSON] et al. [41] have proposed clustering-based protocols that attempt to refine the cluster-head selection process by considering node deployment and residual energy. While these methods contribute to prolonging network lifetime, they often introduce additional complexity or overhead, which may not be sustainable in large-scale, dynamic environments. Enhanced hierarchical clustering node collaborative scheduling (EHNCS) [17] also rotates the cluster-head role between nodes by constructing a CH rotation candidate. Although EHCHCS achieves significant energy efficiency and network lifetime, its applications are limited to homogeneous sensor networks. Moreover, it compromises the complete network coverage to enhance network lifetime. [PERSON] and [PERSON] [53] proposed a method that incorporates a new CH rotating algorithm along with suppression of unnecessary data transmissions. This method exploited energy heterogeneity and only cluster-head rotation and sleep-awake scheduling was considered to enhance the network lifetime. The consideration for large-scale networks and multi-hop inter-cluster communication was not included. Therefore, balance between cluster heads due to inter-cluster communication was not thoroughly considered. Centralised energy-efficient clustering (CEEC) [20] also includes the cluster-head rotation for energy balancing among the nodes in same cluster.