A Response Time Aware Extension of the Flying Duck Formation Energy Optimization Algorithm for Cluster Head Selection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are deployed extensively in applications ranging from environmental monitoring and precision agriculture to industrial automation and smart healthcare. These networks consist of battery-powered nodes with limited energy, computation, and communication resources. Energy conservation is the dominant design constraint, since node batteries are typically non-rechargeable and field replacement is impractical.Wireless Sensor Networks (WSNs) rely heavily on clustering to conserve energy, with Cluster Head (CH) selection playing a critical role in extending network lifetime. Our previously published FDEOA algorithm, inspired by duck V-formation flight behavior, demonstrated significant improvements in network lifetime through swarm-based CH selection, but it did not account for message forwarding latency,a critical concern for time-sensitive IoT applications. This paper introduces RT-FDEOA (Response-Time-Aware FDEOA), which addresses this limitation by embedding an M/M/1 queuing-theoretic response time model directly into the CH selection fitness function. The resulting three-term fitness function penalizes candidate CHs with high predicted queue loads, thereby balancing traffic distribution across the network and reducing end-to-end latency. Extensive MATLAB simulations conducted across 30 Monte-Carlo runs show that RT-FDEOA achieves a 31.0% reduction in mean response time (12.9 ms vs. 18.7 ms for LEACH) while maintaining competitive network lifetime (Average Node Death = 1697 rounds, +26.6% over LEACH). These improvements are statistically validated using Wilcoxon signed-rank tests (p < 0.001), with large effect sizes (Cohen's d = 3.14 vs. LEACH). RT-FDEOA is the first WSN clustering protocol to unify bio-inspired swarm intelligence for CH selection with an analytical M/M/1 latency model, offering a practical, latency-aware solution for energy-efficient WSN deployments.