Wireless communication networks based on IEEE 802.15.4 and ZigBee PRO constitute a critical component of smart grid infrastructures, where reliability and availability requirements exceed those typically assumed in low-power wireless deployments. Despite extensive analytical modeling, most existing studies rely on independence assumptions for packet errors and simplified abstractions of reassociation dynamics. This work presents stochastic reliability characterization grounded on real MAC-layer traffic capture from an operational IEEE 802.15.4/ZigBee PRO network. The methodology combines statistical hypothesis testing, first-order Markov modeling, spectral-gap analysis, large-deviation theory, renewal processes, and survival analysis of realignment intervals. Empirical results reject the hypothesis of independent frame errors and demonstrate significant temporal dependence with geometric mixing behavior. The estimated transition structure reveals burst-error persistence, inflating long-run variance relative to memoryless models. Furthermore, coordinator realignment intervals deviate from exponential behavior, exhibiting non-constant event rates consistent with regenerative dynamics. These findings indicate that effective communication reliability is governed not only by average frame error probability but also by dependence structure and regeneration mechanisms. The proposed probabilistic framework provides a rigorous and reproducible methodology for dependence-aware reliability assessment in smart grid communication systems.
