Mission-critical applications depend on networks that consistently meet strict end-to-end (e2e) latency bounds. In these systems, a network becomes effectively unavailable whenever delay exceeds the required deadline, making availability a question of delay compliance rather than simple uptime. This paper proposes a methodology for assessing delay-based availability using Extreme Value Theory (EVT). The contribution lies in the systematic integration and automation of established EVT techniques to enable reproducible and diagnostically validated tail analysis of latency data. The approach includes algorithmic selection of thresholds and block sizes, formal validation of approximate independence through declustering, and stability diagnostics for reliable tail modeling. We demonstrate the methodology on a publicly available latency dataset from a commercial 5G non-standalone (NSA) network, used strictly as a case-study example. Results show consistent tail-index estimates across both EVT methods, enabling extrapolation of delay-violation probabilities under conditions where EVT assumptions are verified. The proposed framework provides a principled foundation for evaluating delay-based availability in communication systems where rare extreme delays dominate reliability and sufficient measurement data are available.

5G networks offer significant advancements over its predecessor, 4G Long-Term Evolution (LTE). Low latency network access, a key requirement enabling near real-time responsiveness as required by applications such as autonomous driving, factory automation and virtual reality, is one of 5G's key features. In this paper, we present the results of a long-term measurement campaign of the uplink end-to-end (e2e) latency experienced by a 5G-capable device using a commercial sub-6Ghz 5G non-standalone (NSA) network. Our results show an average uplink e2e latency of 12ms, with a 95th percentile of 21ms. This compares favorably with an average uplink e2e latency of 35ms and a 95th percentile of 53ms using 4G LTE to reach the same destination. We also characterize and define, through real-world network parameters in the uplink data transmission process, an unexpected latency pattern that impacts the performance of latency-sensitive applications, even in 5G standalone (SA) networks, such as edge computing or ultra-reliable low-latency communication (URLLC)-a new class of applications targeted in 5G networks.

Wirelessly connected sensors are increasingly used in critical applications, e.g. firefighters or drone rescuing team, raising the need to quantify performance guarantees. In this work, we characterize the end-to-end delay on a Mobile Wireless Sensor Network (MWSN) with an analytical method based on Stochastic Network Calculus (SNC) with Moment Generating Functions (MGFs). The particularity of the studied scenario is that the network is composed of two segments: one with multiple nodes connected by a contention-based channel using the Distributed Coordination Function (DCF), and another consisting of a link prone to disconnection due to the mobility pattern of the nodes in the first segment. Numerical performance bounds are provided for an example application in which the effects of per-node offered load, scheduling algorithms and connectivity on the link to the sink are quantified for different network sizes.