Energy-Efficient Online Data Sensing and Processing in Wireless Powered Edge Computing Systems
Xian Li, Suzhi Bi, Yuan Zheng, and Hui Wang

TL;DR
This paper introduces an online energy-efficient data sensing and processing strategy for wireless powered MEC systems, optimizing long-term data rates under stochastic fading channels with low computational delay.
Contribution
It proposes the LEESE algorithm using Lyapunov optimization and BCD methods to efficiently control energy transfer and data processing without future channel knowledge.
Findings
Achieves 21.9% higher data sensing rate than benchmarks.
Provides sub-millisecond computation delay for real-time control.
Transforms the stochastic problem into per-slot convex optimization.
Abstract
This paper focuses on developing energy-efficient online data processing strategy of wireless powered MEC systems under stochastic fading channels. In particular, we consider a hybrid access point (HAP) transmitting RF energy to and processing the sensing data offloaded from multiple WDs. Under an average power constraint of the HAP, we aim to maximize the long-term average data sensing rate of the WDs while maintaining task data queue stability. We formulate the problem as a multi-stage stochastic optimization to control the energy transfer and task data processing in sequential time slots. Without the knowledge of future channel fading, it is very challenging to determine the sequential control actions that are tightly coupled by the battery and data buffer dynamics. To solve the problem, we propose an online algorithm named LEESE that applies the perturbed Lyapunov optimization…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Age of Information Optimization · IoT and Edge/Fog Computing
