Adaptive Mobile Sink Scheduling with Proximal Policy Optimization in Wireless Sensor Networks

WSNs has emerged as one of the foundational technologies in real-time monitoring, communication, and decision-making fields in services like environmental monitoring, industry automation, and smart cities. Irrespective of their potential, WSNs have encountered consistent problems, such as, constraints to energy, changing topology, and inequal distribution of steep computational power. In mitigating such limitations, the present paper is made through a sensitivity analysis of the WSNs through Adaptive Mobile Networks (AMNs) that reconfigures sensor positioning and route configuration dynamically with respect to different traffic patterns and environmental changes to overcome the limitations previously mentioned. The presented framework uses a Transformer-enhanced Proximal Policy Optimization (PPO) agent that regards network routing policies as adaptive and is applied to enhance load balancing, reduce makespan, and increase network stability. The parameters that require sensitivity are the node density, traffic variability as well as the fault-tolerance parameter, all are systematically investigated to determine the impact they have on the variation of throughput and reliability. Both synthetical and real-world experiments are run and their performance compared against classical strategies in scheduling such as Random, Round Robin, Weighted Round Robin, Min-Min, and Max-Min, the experiments demonstrate that AMNs can provide a more balanced load distribution and remain stable even in the cases of variable network conditions. Moreover, the confidence intervals about bootstrap are also employed to provide the statistical robustness. This work highlights the significance of producing adaptive mobility on the development of resilient, scalable, and efficient in terms of energy usage WSNs, which provides useful information to the design of the next-generation sensor systems. \textbf{Keywords:} VSN (Velocity Sensor Network), AMPN (Adaptive Mobile Networks), Sensitivity Analysis, Resilience Application, Dynamic Load balancing, Proximal, Policy Optimization, Transformer, Network Reliability