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The operational lifetime of a wireless sensor network (WSN) for event detection is determined by the maximum time that the network is able to meet given detection requirements (DRs), i.e., the probabilities of detection and false alarm demanded by the application. In this paper, we address the problem of maximizing lifetime of such WSNs through optimizing quantization and routing of event measurements. In particular, we consider the task of monitoring multiple events and reporting the observations to a sink, whereby sensor nodes adapt their data generation rate and the data flow distribution in the network for the purpose of lifetime maximization. We make use of ultra-wideband (UWB) signaling at the physical layer, which is well-suited for event-detection WSNs, because of the low-energy consumption for data transmission and relative robustness to multiuser interference. Expressing the DRs as convex constraints in the optimization variables, we present a convex-optimization framework for lifetime maximization of event detection UWB-based WSNs. Furthermore, based on the dual decomposition approach, we propose a decentralized algorithm, which makes it possible to solve the lifetime-maximization problem in a distributed manner and thus shares the computational complexity among network nodes and is robust to artifacts like node failures. Numerical results show that the proposed joint adaptation of quantization and routing leads to significant improvements in network operational lifetime compared to benchmark approaches known from literature.