Latency and reliability are essential parameters for enabling ultra-reliable low-latency communication (URLLC). Therefore, an approach for node identification that satisfies the requirements of latency and reliability for URLLC based on the formation of swarms by dragonflies, called dragonfly node identification algorithm (DNIA), is proposed. This method maps bio-natural systems and legacy communication into metrics of URLLC, i.e., latency and reliability, for node identification. A performance analysis demonstrates that the new paradigm for mapping the metrics, i.e., latency and reliability, in terms of nodes (food source) and noise (predators) provides another dimension for URLLC. A comparative analysis proves that DNIA demonstrates significant impact on the improvement of latency, reliability, packet loss rate, as well as throughput. The robustness and efficiency of the proposed DNIA is evaluated using statistical analysis, convergence rate analysis, Wilcoxon test, and analysis of variance on classical as well as modern IEEE congress on evolutionary computation 2014 benchmark functions. Moreover, simulation results show that DNIA outperforms other bioinspired optimization algorithms in terms of cumulative distributive function and average node identification errors. The conflicting objectives in the tradeoff between low latency and high reliability in URLLC is discussed on a Pareto front, which shows the improved and accurate approximation for DNIA on a true Pareto front. Further, DNIA is benchmarked against standard functions on the Pareto front, providing significantly superior results in terms of coverage as well as convergence.