## 2 Related Work
However, this approach does not account for dynamic energy variations over time, which may lead to inefficient cluster head selection in rapidly changing network conditions. The work in [19] introduces an energy-efficient routing protocol known as multi-threshold segmentation-based energy-efficient routing protocol (EERPMS) to enhance the rationality of cluster formation and cluster head (CH) selection. Although EERPMS improves CH selection, it does not incorporate adaptive mechanisms to dynamically adjust thresholds based on real-time network conditions, which could lead to suboptimal energy utilization. While the authors in [20] address the critical concern of energy conservation in WSNs by introducing the energy-saving fuzzy clustering routing algorithm (EFCR), EFCR still relies on predefined fuzzy rules, which may not be flexible enough to adapt to unpredictable network dynamics, thereby limiting its effectiveness in highly dynamic environments. Many of the existing clustering algorithms suffer from the drawback of employing fixed CH election methods in each clustering round, resulting in the continuous selection of unsuitable nodes as cluster heads. This, in turn, accelerates node energy depletion and leads to a shortened network lifespan. Prolonging the network lifetime of WSNs has been a focal point in the literature. Researchers have explored methods to maximize the lifespan of sensor nodes, including optimized energy harvesting techniques, duty cycling, and node replacement strategies. Extending network lifetime is essential for reducing maintenance costs and ensuring the longevity of WSN deployments. The work in [21] addresses charging behavior by introducing a charging scheduling scheme known as partial charge. The scheme strategically enables the partial charging of sensor nodes, minimizing overall node downtime and consequently extending the network's lifespan. However, the partial charge approach assumes a predictable energy demand pattern, which may not be applicable in scenarios where energy consumption varies unpredictably. While in [22], the authors explore the residual-energy awareness feature of sensor nodes in time-varying WSNs, presenting an analysis and modeling approach using a Markov chain. The Markov chain allows for the evaluation of state-transition probabilities (STP) concerning the energy levels of nodes with undetermined and deterministic residual energy. Despite its effectiveness in energy-aware modeling, the approach does not incorporate real-time feedback mechanisms to adjust network operations dynamically based on energy state changes. To reduce computational overhead, a spatial discretization scheme is introduced for constructing a charging route for the mobile charger. Subsequently, a path optimization scheme is devised to enhance charging utility [23]. However, the optimization scheme primarily focuses on charging efficiency and does not fully account for mobility constraints, which could affect the practicality of the approach in real-world deployments. Recent research has explored the integration of mobile ad hoc networks (MANETs) and IoTs-based WSNs to enhance connectivity and energy efficiency in heterogeneous networks. The MANET-based energy saving optimization (MANET-ESO) technique has been proposed in [24], which establishes trusted routes between sensors and gateway nodes, improving network reliability while reducing energy consumption. While MANET-ESO focuses on energy-efficient routing in heterogeneous networks, it does not incorporate UAV-assisted data collection or WPT, which are key features of the proposed scheme. Additionally, MANET-ESO primarily enhances trusted routing between sensors and gatewaysbut does not effectively address the hotspot problem caused by uneven energy consumption in multi-hop WSNs.