Task-Offloading Optimization in Mobile Edge Computing for Smart Library Services
TL;DR
A preference-adaptive dueling double deep Q-network algorithm, termed PA-DDQN, is proposed by integrating preference conditioning, multi-head attention, a dueling architecture, and double Q-learning, demonstrating its effectiveness in enhancing service responsiveness, energy efficiency, and reliability in smart library MEC systems.
Abstract
With the rapid development of artificial intelligence and Internet of Things technologies, smart libraries increasingly require low-latency and energy-efficient computing support for heterogeneous services such as access control, intelligent recommendation, indoor navigation, and book localization. To address the limitations of cloud-only processing, this paper investigates task-offloading optimization in a cloud-assisted mobile edge computing environment for smart library services. A three-tier cloud–edge–device collaborative architecture is first established, and the task-offloading problem is formulated as a multi-objective optimization problem that jointly minimizes task-completion delay and user-side energy consumption under latency, resource-capacity, and coverage constraints. To solve the dynamic decision-making problem, a preference-adaptive dueling double deep Q-network algorithm, termed PA-DDQN, is proposed by integrating preference conditioning, multi-head attention, a dueling architecture, and double Q-learning. Simulation results show that PA-DDQN achieves better performance than fixed offloading strategies and representative reinforcement-learning baselines. Under the heaviest task load, PA-DDQN reduces the average task-completion delay by 23.1% and 31.0% compared with D3QN and DDQN, respectively, while reducing energy consumption by 5.8% and 9.9%. It also improves the task success rate by 14.8% and 21.7%, demonstrating its effectiveness in enhancing service responsiveness, energy efficiency, and reliability in smart library MEC systems.