Stochastic Petri Nets for Drone Performance Analysis in Mobile Edge Computing
DOI:
https://doi.org/10.5753/jisa.2026.6851Keywords:
Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs), Performance Evaluation, Stochastic Petri NetsAbstract
The global drone market has grown from US$4 billion in 2023 to a projected US$4.8 billion by 2029. Concomitant with this growth, integrating drones into Mobile Edge Computing (MEC) environments poses critical challenges in scalability, resource allocation, and latency-aware distributed processing, which directly affect overall system performance. Addressing these issues through physical prototyping is costly and complex, which motivates the use of analytical models that can predict system behavior under different conditions. In this work, we propose a performance evaluation approach based on Stochastic Petri Nets (SPNs) to model the admission, load balancing, and distributed task processing among drones acting as mobile edge nodes. The proposed model enables resource scaling and scalability analysis without physically implementing the architecture, reducing deployment risks and costs. Simulation results demonstrate that the model accurately captures key performance metrics, including Mean Response Time (MRT), throughput, and task drop rate, providing quantitative insights for designing efficient and resilient UAV-supported MEC systems.
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