Online Cognitive Data Sensing and Processing Optimization in Energy-harvesting Edge Computing Systems

TL;DR

This paper introduces an online optimization algorithm for energy-harvesting edge devices that enhances data sensing rates in IoT networks while respecting spectrum and power constraints.

Contribution

It proposes the PLySE algorithm using perturbed Lyapunov optimization for real-time data sensing and processing in energy-harvesting MEC systems, with a threshold-based optimal solution.

Findings

Achieves over 46.7% higher sensing rate compared to benchmarks.

Provides a low-complexity, real-time implementable solution.

Demonstrates the effectiveness of the threshold-based control policy.

Abstract

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with an energy harvesting (EH) wireless device (WD) which opportunistically accesses the licensed spectrum of an overlaid primary communication link for task offloading. We aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MEC server (MS) and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure. Simulation results collaborate with our analysis and demonstrate more than 46.7\% data sensing rate improvement of the proposed PLySE over representative benchmark methods.