Stochastic Petri Nets for Drone Surveillance: Modeling Availability and Reliability - Lins, Luan; Nascimento, Erick; Dantas, Jamilson; Araujo, Jean; Maciel, Paulo (Anais do Latin-American Symposium on Dependable Computing (LADC), 2024)
This paper presents a comprehensive approach to evaluate the availability and reliability of drone surveillance systems using Stochastic Petri Net (SPN) models. We propose an architecture incorporating redundancy in drones and batteries to enhance system resilience. Two SPN models are developed to assess availability and reliability metrics, considering factors such as battery charging and discharging times, drone failure rates, and repair times. Sensitivity analysis is conducted to identify critical components and their impact on system performance. Case studies demonstrate that battery redundancy significantly improves system availability and reliability, surpassing the impact of drone redundancy. For long-duration missions (30 hours), maintaining 15-20 redundant batteries ensures reliability above 80%. Additionally, optimizing battery charging time to less than 36 minutes and using batteries with discharge times exceeding 144 minutes substantially improves system reliability. These findings provide valuable guidelines for designing more robust and reliable drone surveillance systems. The study contributes to the field by offering a methodology for quantitative assessment of drone system dependability and identifying key areas for performance optimization.