Adaptive caching reduces deadline misses and energy use in multi-uav networks
Adaptive Deadline- and Energy-Aware Caching Algorithms in Dynamic Multi-UAV Edge Networks
Machine LearningNetworking and Internet Architecture
Summary
When drones deliver content to places without ground networks, it’s important to store and share that content so it arrives on time without using too much energy. The authors created two methods to do this better by considering how urgent content is and how much energy drones use when communicating. Their advanced AI-based method learns and adapts to changing conditions, cutting late deliveries by about 60% and balancing drone energy use. This helps keep information flowing efficiently from drones in complex and shifting situations.
What this means in practice
- •For drone network operators: Optimize content storage across drones to reduce missed deadlines and balance energy use in dynamic delivery scenarios.
- •For edge computing engineers: Implement adaptive caching strategies in mobile edge networks using UAVs that respond effectively to changing user demands and energy constraints.
Authors
Jian Kim, Ki-Il Kim
Abstract
Content caching via unmanned aerial vehicles (UAVs) becomes one of the solutions to maintain content delivery when terrestrial infrastructure is unavailable or disrupted. However, existing caching approaches often focus primarily on cache-hit performance or average delivery delay, without jointly addressing spatially heterogeneous and time-varying demand, content-specific deadlines, and uneven communication-energy consumption across UAVs. To address these challenges, we formulate a dynamic deadline- and energy-aware cache placement and replacement problem that jointly considers deadline misses, UAV-side communication energy, and inter-UAV energy imbalance. To achieve this, we first develop deadline- and energy-aware UBAC (DE-UBAC), an interpretable hierarchical rule-based realization that incorporates deadline-aware prioritization, cooperative placement, energy-aware coordination, and cache-update cost. However, predefined priorities and thresholds limit its adaptability under dynamically coupled network conditions. We therefore propose GRU-assisted Deadline- and Energy-aware Proximal Policy Optimization (GRU-DEPPO), which combines local and global GRU-based temporal representations with a centralized PPO actor-critic for adaptive cache placement and replacement. Simulation results show that GRU-DEPPO achieves the lowest deadline-miss rate and total UAV-side communication energy among the considered schemes. Compared with DE-UBAC, GRU-DEPPO reduces the deadline-miss rate by approximately 61% and inter-UAV energy imbalance by approximately 40%.