## 1 Introduction
### Problem Statement and Motivation  
The management of vast IoT sensor networks poses significant challenges, particularly in terms of energy efficiency, data transmission reliability, and network longevity. One critical issue is the selection of DFHs, which are pivotal in clustering operations [10]. DFHs are responsible for aggregating data from their respective DFGMs, performing data processing, and transmitting the aggregated data to the sink node or other relay DFHs. However, the role of DFHs is energy-intensive, and inefficient DFHs selection can lead to the rapid depletion of energy resources, uneven load distribution, and shortened network lifespan [11]. Consequently, optimal DFH selection is vital for the success of clustering strategies [12]. Another key challenge is defining the optimal number of DFGs and achieving an even distribution of SNs within groups. IoT networks often operate in dynamic environments where network topology and conditions can change rapidly, making traditional DFH selection methods, often reliant solely on remaining energy (RE), inadequate. These conventional approaches struggle to adapt to IoT's dynamic nature, resulting in energy imbalances and premature node failures [13]. Emerging technologies like machine learning offer promising solutions by introducing adaptability and context-awareness to DFH selection, addressing these limitations effectively [14; 15]. Furthermore, optimization within DFGs remains critical since SNs typically rely on limited power sources such as batteries, and frequent recharging or replacement is often infeasible [16]. Optimizing energy consumption while ensuring reliable data transmission and minimal latency is essential, especially for real-time applications such as healthcare monitoring, where timely and accurate data are critical. The dynamic nature of IoT networks demands that algorithms adapt to changes in topology, balance the load among SNs, and efficiently manage limited computational and storage resources. Data fusion presents another significant challenge in IoT networks. The process of aggregating and synthesizing data from multiple SNs to produce meaningful and accurate outputs is computationally intensive and often constrained by limited resources [17]. Inefficient data fusion mechanisms can result in redundant transmissions, increased energy consumption, and higher latency. Additionally, ensuring the quality of fused data is critical for applications such as environmental monitoring and industrial automation, where decisions depend on accurate, real-time information. Despite the availability of sophisticated data fusion techniques, their integration into resource-constrained IoT networks poses challenges related to scalability, adaptability, and energy efficiency [18]. The integration of emerging technologies such as machine learning and Artificial Intelligence (AI), particularly Computational Intelligence (CI), into DFH selection, data fusion grouping, and data fusion offers both opportunities and challenges [19]. Optimization methods such as Genetic Algorithms (GA) [20], Particle Swarm Optimization (PSO) [21], and Ant Colony Optimization (ACO) [22] have demonstrated significant potential in enabling context-aware decision-making by considering multiple criteria, including RE, node connectivity, communication overhead, and data redundancy [23]. These methods can significantly enhance clustering and data fusion efficiency, resulting in balanced energy consumption, reduced data redundancy, and enhanced network longevity [24]. However, choosing the appropriate optimization technique requires a careful consideration of network characteristics, such as node mobility, density, heterogeneity, and resource constraints, underscoring the need for lightweight, adaptive, and real-time algorithms tailored for specific IoT deployment scenarios [25]. Routing is equally critical, as it manages the pathways for data packets traveling from SNs to the sink node or relay DFHs.