  Considering a Manhattan mobility model in vehicle-to-vehicle networks, this
work studies a power minimization problem subject to second-order statistical
constraints on latency and reliability, captured by a network-wide maximal data
queue length. We invoke results in extreme value theory to characterize
statistics of extreme events in terms of the maximal queue length.
Subsequently, leveraging Lyapunov stochastic optimization to deal with network
dynamics, we propose two queue-aware power allocation solutions. In contrast
with the baseline, our approaches achieve lower mean and variance of the
maximal queue length.
