## 2 Related Work  
Clustering and routing approaches have consistently demonstrated their significance in conventional wireless sensor networks (WSNs), particularly due to their contributions to scalability and energy efficiency [33; 34; 35; 20]. While these approaches have been well studied in conventional WSNs, they can also be effectively utilised in IoT-based heterogeneous wireless sensor networks (HWSNs) to achieve energy efficiency in complex and diverse network environments [36; 37; 38; 39; 40; 41]. Numerous protocols, such as SEP (stable election protocol) [30], DEEC (distributed energy efficient clustering) [31], and their derivatives like D-DEEC [42], E-DEEC [43], ED-DEEC [44], DRE-SEP [45], DARE-SEP [36], and DE-SEP [37], have been developed based on the foundational principles established by LEACH [27]. These protocols employ strategies such as randomised cluster-head rotation to evenly distribute workload, data aggregation to conserve energy, and local coordination to maintain scalability and robustness, particularly in dynamic environments. SEP (stable election protocol), proposed by [PERSON] et al. [30], is a LEACH-inspired clustering method that addresses two-tier energy heterogeneity by differentiating between normal and advanced nodes, the latter having greater resources. While SEP enhances the management of energy variance and introduces specific operational epochs for each node type, it faces limitations such as the necessity for frequent cluster reformation in each round and its restriction to only two levels of energy heterogeneity. DEEC [31], which introduces a probabilistic cluster-head selection mechanism based on residual and average network energy, attempts to distribute the energy load more effectively. However, it inadvertently penalises high-energy nodes by depleting their resources at the same rate as lower-energy nodes, which can lead to suboptimal energy distribution and shortened network lifespan. Subsequent enhancements like D-DEEC [42] and E-DEEC [43] aim to address these issues by incorporating multiple levels of energy heterogeneity. Nonetheless, these protocols often fall short in environments where node capabilities vary significantly, as they tend to treat advanced nodes similarly once their energy levels converge, thus failing to fully exploit the heterogeneity within the network. Further, protocols such as ED-DEEC [44] and [PERSON] et al. 's four-tier model [46] expand on these ideas by introducing more granular classifications of node energy levels and tailored probabilistic equations for cluster-head selection. However, these methods still face challenges in maintaining efficiency as node energy levels deplete, often leading to a homogenisation of nodes that undermines the benefits of the initial heterogeneity. [PERSON] and [PERSON]'s DRE-SEP [45], an enhancement of SEP [30], addresses three levels of energy heterogeneity, particularly in event-driven applications, by using a weighted probabilistic formula for cluster-head selection that factors in node distance, energy levels,and type-specific epochs. It also facilitates dual-hop communication to reduce energy consumption. Similarly, (distance-aware residual energy efficient stable election protocol) DARE-SEP [36] and (distance and distance and energy aware) DE-SEP [37] refine the SEP approach by incorporating node distance and energy levels into cluster-head selection, with DE-SEP limiting the number of cluster heads to optimise energy efficiency. However, these protocols remain primarily focused on energy heterogeneity, often neglecting other important factors such as computational capabilities, link quality, and data generation rates--key considerations in large-scale, heterogeneous IoT environments. Another key challenge in existing approaches is the management of network heterogeneity beyond energy levels. Protocols like the energy-driven unequal clustering (EDUC) [47] and adaptations of HEED [29], such as those proposed by [PERSON] et al. [48] and [PERSON] et al.