We envision a wireless sensor network (WSN) as an entity analogous to a biological population with individual nodes mapping to individual organisms and the network architecture mapping to the biological population. In particular, a mobile wireless sensor network consisting of hundreds or more of microchip-driven nodes is analogous to a flying insect population in several important aspects. The notions of lifetime, space distribution and environment apply to both domains. The interactions between individuals, either insects or WSN sensors, can be captured with evolutionary game theory models, in which individuals are the players and reliability are the fitness (payoff) of the players. The evolutionary stable strategy (ESS) forms the basis of network survivability. Furthermore, we introduce hybrid fault models into the proposed architecture with the notion of "Byzantine generals playing evolutionary games"[13]. This unified architecture makes it possible to dynamically (real-time) predict reliability, survivability, and fault tolerance capability of a WSN. On the node level, we introduce survival analysis to model lifetime, reliability and survivability of WSN nodes. By adopting survival analysis, rather than standard engineering reliability theory, we try to address four critical issues: (i) replacing unrealistic constant failure rates with general stochastic models, (ii) replacing unrealistic independent failures with shared frailty models that effectively address common risks or events-related dependence, and with multi-state modeling for common events dependence, (iii) harnessing the censoring-modeling mechanisms to assess the influences of unpredictable malicious events on reliability and survivability, and (iv) addressing spatial aspects of failures with spatial survival analysis, spatial frailty.