## 1 Introduction
### Research Gaps, Our Methodology, and Contributions  
In the realm of IoT networks, significant research gaps persist in the areas of clustering, optimization methods, and data fusion for DFH selection and routing. Current clustering and data fusion techniques often struggle with scalability and adaptability to dynamic network conditions, leading to inefficient resource utilization and reduced network lifespan. Moreover, optimization methods for DFH selection require further refinement to effectively balance energy consumption, ensure load distribution, and maintain robust communication links. Existing algorithms frequently overlook heterogeneous network environments, where SNs may have varying energy levels and capabilities. Additionally, routing protocols face challenges in handling the diverse and fluctuating data traffic typical in IoT applications. Many protocols lack the ability to dynamically adjust routes in real-time, resulting in increased latency and energy expenditure. Addressing these gaps necessitates the development of more advanced, adaptable algorithms that can optimize performance across varied IoT network scenarios, ensuring reliability, energy efficiency, and prolonged operational life. Essentially, there is an interest in utilizing CI and data fusion for routing protocols in IoT applications. These applications require simple, stable, and reasonably priced networking solutions. In addition, improving network performance presents a complex optimization problem. In response to the aforementioned challenges, bioinspired evolutionary approaches like PSO [29] and ACO [22] have gained importance in determining the optimal paths for the routing protocols. These optimization techniques compute the Data Fusion Fitness Function (DFFF) iteratively throughout a population and compare every solution to the best one in order to identify the optimum routing option. Indeed, DFFF definitions have a major influence on selecting optimal DFHs. In the existing literature, DFFFs have frequently been designed with uniform or arbitrary weight values, regardless of the location of SNs or their RE. Furthermore, selecting the optimization approach is seen to be a difficult task. It is important to note that network performance concerns are exacerbated by the expanding size of the network and the number of SNs. IoT researchers have responded to this by proposing energy-efficient routing protocols that might allow networks to function for extended periods of time without requiring battery replacements or recharges, especially in harsh environments. As a result, the primary objective of this work is to put forward a practical routing protocol that will improve network performance and cure previous issues. Only a limited number of metaheuristic algorithms have gained broad traction in general. This is mainly because of its energy-inefficient and computationally intensive features, which are unable to ensure that there is enough RE to increase network lifespan. Therefore, this is why the most appropriate metaheuristic algorithm must be chosen. Importantly, this work tackles the faults of currently available clustering and data fusion algorithms while likewise utilizing a superior metaheuristic algorithm, named the Marine Predators Algorithm (MPA) for Primary DFH (PDFH) selection in this particular context, to overcome energy efficiency considerations [30]. Under the proposed protocol, the network lifespan is distributed evenly among rounds. In fact, the setup and steady state phases are covered in the first round, and only the steady state phase is addressed in the following rounds for data fusion and transmission purposes. On the other hand, the setup step could be carried out during regrouping (PDFH reselection). The unique feature of this protocol is that SNs are able to perceive and send information over the duration of the Round Time (RT) to handle data fusion in a professional manner. Additionally, a set of SNs is randomly distributed inside a rectangle network sensory geographical area.