## 3 Methodology
### The Proposed Scheme  
To solve these issues, we propose a new scheme, with Figure 2 illustrating the network architecture. The network area is divided into two regions, the first region is at the center of the network and is represented as a circle area, and it contains types of nodes called standard nodes. The second region is at both sides of the circle and is divided into four clusters, two at each side of the circle. The proposed scheme encompasses three categories of nodes distinguished by their power levels: standard, advanced, and helper nodes. These sensor nodes are categorized based on their deployment method within the network, their specific locations, and their initial energy reserves. While standard and advanced nodes are placed randomly throughout the network, the helper nodes are positioned at fixed locations, as Figure 2 illustrates.  
The proposed scheme utilizes a UAV as a mobile sink, traversing the network to collect data from both the BS and helper nodes. Simultaneously, it wirelessly charges the helper nodes using WPT and adjusts data collection rates based on available energy, effectively mitigating the hotspot problem and enhancing overall network connectivity and performance. By optimizing the data collection rate and considering the energy levels in  
Figure 1: Hotspot example in a WSN.  
Figure 2: The network architecture of the proposed scheme.  
both the sensor nodes and the mobile sink, the scheme efficiently addresses the hotspot issue. The employed mobile sink follows a predefined short path to collect environmental data from both helper nodes and the fixed sink, utilizing WPT through inductive coupling or magnetic resonance coupling methods. Hence, the proposed scheme reduces the necessity for sensor nodes to engage in a higher number of transmission hops.  
Furthermore, to address energy imbalances, the scheme determines the optimal number of clusters by taking into account factors such as nodes per hop and available energy. It also selects a CH for each cluster. The mobile sink, typically a UAV like a quadcopter, follows its path, stops at each helper node, retrieves aggregated data, and provides energy as needed. After completing its route, the UAV returns to its starting point, where users can access the collected data and recharge or replace the UAV's battery for the next round. This design choice which Figure 2 presents is based on the following criteria:  
1. Energy distribution and load balancing: Dividing the secondary region into four clusters ensures that the data transmission burden is evenly distributed among the advanced nodes. A smaller number of clusters would lead to excessive energy consumption at a few cluster heads, while a larger number of clusters would increase communication overhead.
2. Efficient UAV-assisted data collection: Since the UAV follows a predefined path to collect data from helper nodes, having four clusters ensures optimal coverage without excessive flight distance. A different number of clusters could either increase the UAV's travel time and energy consumption or reduce its efficiency in gathering data.
3. Minimizing data transmission hops: The selected number of clusters allows for an optimal trade-off between the number of transmission hops and energy efficiency. Too few clusters would lead to long data transmission paths, while too many clusters would require more control overhead for managing additional CHs.  
These design choices ensure improved network lifetime, energy efficiency, and stable communication, as demonstrated by the simulation results.