Energy-Efficient Online Scheduling for Wireless Powered Mobile Edge Computing Networks

2026-03-09

AI Summary

This work addresses the challenge of jointly optimizing energy efficiency and latency in wireless-powered mobile edge computing networks, where energy transmission and computation offloading compete for limited resources. The authors propose an online scheduling framework based on Lyapunov optimization that transforms the stochastic joint scheduling problem into a per-time-slot deterministic optimization. A relaxation-and-adjustment strategy is devised to solve this efficiently. For the non-convex offloading subproblem, structural properties are exploited to reformulate it as a tractable assignment problem. By introducing the concept of marginal energy efficiency and deriving optimality conditions, the study establishes a theoretical trade-off between delay and energy consumption. Furthermore, a queue-balancing mechanism reduces latency without increasing energy expenditure. Simulations demonstrate that the proposed algorithm significantly improves energy efficiency, reduces delay across diverse scenarios, exhibits strong robustness, and offers rigorous theoretical performance guarantees.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enhance the computational capability of wireless devices (WDs). In WPMEC systems, WPT and computation offloading compete for limited wireless resources, which makes their joint scheduling particularly challenging. In this paper, we investigate the energy-efficient online scheduling problem for WPMEC networks with multiple WDs and multiple access points (APs). Based on Lyapunov optimization, we develop an online optimization framework that transforms the original stochastic problem into deterministic per-slot optimization problems. To reduce computational complexity, we introduce the concept of marginal energy efficiency and derive an associated optimality condition, based on which a relax-then-adjust approach is proposed to efficiently obtain feasible solutions. For the resulting non-convex computation offloading subproblem, we analyze the structural properties of its optimal solution and transform it into an assignment problem that can be solved efficiently. We further provide theoretical performance guarantees for both the per-slot and long-term solution, establishing a fundamental trade-off between latency and energy consumption. To improve practical performance, additional mechanisms are introduced to balance the magnitudes of different queues and reduce latency without increasing energy consumption. Extensive simulation results demonstrate the effectiveness and robustness of the proposed algorithm under various system settings.

Problem

• Wireless Powered Mobile Edge Computing
• Energy-Efficient Scheduling
• Computation Offloading
• Wireless Power Transfer
• Online Scheduling

Innovation

• Lyapunov optimization
• marginal energy efficiency
• relax-then-adjust
• computation offloading
• wireless powered MEC