Location-Aware Wireless Resource Allocation in Industrial-Like Environment

Abstract

The advent of the fourth Industrial Revolution (Industry 4.0) requires wireless networked solutions to connect machines. However, the industrial environment is notorious for being averse to wireless communication, with traditional wireless resource mechanisms prone to errors because of metallic objects. In this work, we propose to exploit the knowledge of location to derive context information and dynamically allocate wireless resources in time and space to target devices. We exploit the spatial geometry of the Access Points (APs) and we introduce a statistical model that maps the user position's spatial distribution to an angle error distribution and derive a hypothesis test to declare if the link is under metallic blockage or not. In order to avoid changes to the client side and operate with a single interface radio, we use the same wireless network both for positioning and scheduling. We experimentally show that our system can localize four mobile robots deployed in a very harsh environment with metal obstacles and reflections. Context information applied to wireless resources protocol help increasing up to 40% of the network throughput in the above industrial-like scenario.

AoI-Minimal Power Adjustment in RF-EH-Powered Industrial IoT Networks: A Soft Actor-Critic-Based Method

“…1.1 Background W ITH its strong capacity in terms of sensing and transmission, the industrial Internet of Things (IIoT) is regarded as the key technology to facilitate Industry 4.0 [1], [2], [3], where numerous sensors, controllers, execution units and monitors are interconnected to establish smart systems for monitoring, collecting, exchanging and analyzing data for intelligent control. Conventionally, network latency and throughput were adopted as important performance indices to design IIoT systems for applications.…”