## 2 Related Work  
A thorough examination of the existing work underscores the wealth of research and innovations surrounding WSNs. Researchers have delved into various facets of WSNs, encompassing critical aspects such as energy consumption, scalability, security, and network lifetime. The body of literature reflects the relentless pursuit of solutions to these challenges, with a focus on enhancing the overall performance of WSNs [10; 11]. A significant portion of the research has been dedicated to tackling the issue of energy consumption in WSNs. Scholars have explored novel techniques and algorithms aimed at minimizing energy expenditure while ensuring efficient data collection and transmission. These efforts strive to extend the operational lifetime of sensor nodes, a key factor in the sustainability of WSNs. The study in [15] delves into the critical issue of limited power resources influencing the likelihood of sensor network malfunctions. The research focuses on calculating the energy consumption of sensor nodes and estimating the service life of their power supply elements. The study compares energy consumption between conventional methods and neural network-based approaches, revealing that while neural networks can enhance processing efficiency, they also introduce additional power overheads. However, the research does not address potential strategies for optimizing neural network energy consumption or integrating energy-harvesting techniques to extend network lifetime, leaving room for further exploration into energy-efficient WSN deployments. Recent research has explored UAV-enabled mobile crowdsensing (UMCS) as a promising approach for large-scale data collection [16]. However, relying solely on UAVs with limited energy poses significant challenges for covering an entire city efficiently. A hybrid optimization framework for age-of-information (AoI) minimization has been proposed, integrating distributed workers as the primary data collectors while utilizing UAVs to gather data from sensor nodes (SNs) that lack worker connectivity. While this approach improves data collection coverage, it has some limitations. The framework depends heavily on the availability and reliability of distributed workers, which may not always be consistent, leading to potential data gaps. Furthermore, its computational complexity may pose scalability challenges in large-scale networks with dynamic environments. In the realm of wireless sensor networks, the conventional model grapples with inherent limitations stemming from finite battery energy, posing a substantial constraint on network lifetime. Addressing the problem of the finite battery energy of WSNs, the authors in [17] explore the viability of a sensor network operating on wireless energy transfer. The study envisions a scenario where a mobile charging vehicle periodically traverses the sensor network, wirelessly replenishing the energy of sensor nodes. While the proposed scheme in [17] is helpful, it is also variable and insufficient to meet high energy demands. Scalability is another pivotal area of investigation in WSNs. As the demand for larger and more complex networks grows, researchers have sought to develop scalable solutions that can accommodate the expansion of WSNs without compromising performance. Scalability improvements involve innovative clustering algorithms and network topology control techniques. To address the challenges posed by the uneven distribution of nodes and limited energy resources in WSNs, the work in [18] introduces a clustering routing algorithm based on hierarchical clustering. Notably, the algorithm eliminates the need for pre-calculating the optimal number of cluster heads. Initially, the randomly distributed nodes in the network are organized into several primary clusters using the hierarchical clustering algorithm. During network operation, cluster selection is based on both the node's residual energy and its position within the original clusters.