<p>Wireless Sensor Networks (WSNs) play a crucial role in modern monitoring and data-driven systems, particularly in areas such as environmental surveillance, industrial automation, and smart city infrastructure. However, their long-term operation is often hindered by limited energy resources, dynamically changing network topologies, and uneven traffic distribution that leads to premature node failures. Addressing these challenges requires adaptive mechanisms capable of responding to varying network conditions in real time. This work presents a sensitivity-analysis-driven framework based on Adaptive Mobile Networks (AMNs), where mobile sinks dynamically adjust their movement and scheduling behavior according to network state variations. A Transformer-assisted Proximal Policy Optimization (PPO) model is employed to learn adaptive scheduling and routing decisions with the objective of reducing makespan, balancing sink load, and improving network stability. The framework systematically examines the influence of key parameters–including node density, traffic intensity, and fault tolerance–on throughput and reliability under heterogeneous conditions. The proposed approach is evaluated using both synthetic scenarios and real-world sensor data, and its performance is benchmarked against widely used classical scheduling strategies such as Random, Round Robin, Weighted Round Robin, Min-Min, and Max-Min. The results indicate that adaptive mobile sink scheduling consistently achieves improved load distribution and sustains stable operation even under highly variable network conditions. To ensure reliability of the findings, bootstrap-based confidence intervals are used for statistical validation. The outcomes of this study demonstrate that combining adaptive mobility with reinforcement learning provides an effective pathway toward resilient, scalable, and energy-efficient WSN deployments, offering practical guidance for the design of future intelligent sensing systems.</p>