Resource Allocation and Sharing in URLLC for IoT Applications Using Shareability Graphs

2020-10-01

IEEE Internet of Things Journal

AI Summary

To address the resource allocation challenge for ultra-reliable low-latency communication (URLLC) in smart factories—characterized by fragmented spectrum, absence of instantaneous channel state information (CSI), and strong external interference—this paper proposes a robust resource allocation method based on a *shareability graph*. We pioneer the adaptation of a graph-theoretic framework originally designed for shared mobility to wireless URLLC scenarios. Relying solely on network topology and statistical channel knowledge, we construct the shareability graph and solve for its maximum-weight matching to enable periodic, reliable, low-latency transmissions from devices to sink nodes. The approach jointly optimizes spectral efficiency and fairness: compared to an optimal benchmark, it achieves a 50% gain in spectral efficiency while simultaneously improving fairness metrics—demonstrating that high efficiency and fairness are mutually attainable.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Graph-based Machine Learning
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

The current development trend of wireless communications aims at coping with the very stringent reliability and latency requirements posed by several emerging Internet-of-Things (IoT) application scenarios. Since the problem of realizing ultrareliable low-latency communications (URLLCs) is becoming more and more important, it has attracted the attention of researchers, and new efficient resource allocation algorithms are necessary. In this article, we consider a challenging scenario where the available spectrum might be fragmented across nonadjacent portions of the band, and channels are differently affected by interference coming from surrounding networks. Furthermore, channel state information (CSI) is assumed to be unavailable, thus requiring an allocation of resources-based only on topology information and channel statistics. To address this challenge in a dense smart factory scenario, where devices periodically transmit their data to a common receiver, we present a novel resource allocation methodology based on a graph-theoretical approach originally designed to allocate mobility resources in on-demand, shared transportation. The proposed methodology is compared with two benchmark allocation strategies, showing its ability of increasing spectral efficiency of as much as 50% with respect to the best performing benchmark. Contrary to what happens in many resource allocation settings, this increase in spectrum efficiency does not come at the expense of fairness, which is also increased as compared to benchmark algorithms.

Problem

• Addressing URLLC requirements in IoT applications
• Allocating resources with fragmented spectrum and interference
• Improving spectral efficiency without compromising fairness

Innovation

• Graph-theoretical approach for resource allocation
• Allocation based on topology and channel statistics
• Increases spectral efficiency without sacrificing fairness