Intelligent Scheduling Mechanism for UWSNS using Mayfly-Lion Optimization and DSCN

Underwater Wireless Sensor Networks
(UWSNs) support long-term aquatic observation but are
afflicted with extreme resource scarcity, adverse channel
conditions, and extremely unpredictable traffic priorities.
To overcome these issues, we propose a hybrid intelligent
scheduler—Mayfly Lion Optimization-Based Shepherd
Convolution Neural Network (MLO-SCNN)—which
combines swarm-intelligence search and deep, contextaware learning. The envisioned framework initially
embeds node-level and queue-level features (delay
tolerance, residual energy, hop distance, packet criticality)
and supplies them to a Shepherd Convolutional Neural
Network whose attention-facilitated "shepherd" module
assigns priority to packets based on urgency. A Mayfly–
Lion cooperative meta-heuristic at the same time adapts
SCNN hyper-parameters and adjusts transmission queues
for optimal performance while minimizing a multiobjective cost involving energy, latency, and packet
delivery ratio (PDR), balancing exploration (Mayfly) and
exploitation (Lion). Large-scale NS-3 acoustic-channel
simulations involving 200 mobile sensor nodes reveal that
MLO-SCNN increases average PDR by 28.4 %, reduces
end-to-end latency by 17.6 %, and reduces per-node energy
expenditure by 22.1 % over state-of-the-art PSO-CNN and
EDF schedulers, while optimizing 34 % more quickly than
single-algorithm optimizers. Such improvements extend
network lifetime by 19 % and ensure QoS even in the
presence of bursty, priority-mixed traffic. The findings
prove that integrating deep learning with a two-phase
evolutionary search presents an adaptive, priorityconscious scheduling solution appropriate to the extreme
and dynamic underwater environment.