Research Directions and Modeling Guidelines for Industrial Internet of Things Applications

TL;DR

This paper addresses fragmentation in IIoT literature and standards by proposing a unified characterization of application domains, KPI terminology, and traffic types, along with a systematic modeling guideline. It harmonizes nomenclature across 3GPP and 5G-ACIA and introduces a per-AD KPI mapping to enable side-by-side comparisons and reproducible evaluations. The key contributions include a four-domain IIoT taxonomy (Motion control, Process Monitoring, Mobile Control Panels, Mobile Robots), a two-tier KPI framework (CSI and positioning), traffic-type classification, and practical guidelines for device counts, service areas, mobility, traffic and channel models. By outlining open research directions (e.g., THz/optical links, network-as-sensor, AI, massive access, security) and offering concrete modeling prescriptions, the paper provides a roadmap for interoperable, standards-aligned IIoT development with real-world applicability.

Abstract

The Industrial Internet of Things (IIoT) paradigm has emerged as a transformative force, revolutionizing industrial processes by integrating advanced wireless technologies into traditional procedures to enhance their efficiency. The importance of this paradigm shift has produced a massive, yet heterogeneous, proliferation of scientific contributions. However, these works lack a standardized and cohesive characterization of the IIoT framework coming from different entities, like the 3rd Generation Partnership Project (3GPP) or the 5G Alliance for Connected Industries and Automation (5G-ACIA), resulting in divergent perspectives and potentially hindering interoperability. To bridge this gap, this article offers a unified characterization of (i) the main IIoT application domains, (ii) their respective requirements, (iii) the principal technological gaps existing in the current literature, and, most importantly, (iv) we propose a systematic approach for assessing and addressing the identified research challenges. Therefore, this article serves as a roadmap for future research endeavors, promoting a unified vision of the IIoT paradigm and fostering collaborative efforts to advance the field.

Figures (2)

• Figure 1: Illustration of the main IIoT ADs: in green, an example of motion control system implemented in an assembly line (AD1); in blue, a plethora of sensors gathering data from the production plant (AD2); in red, mobile control panels handled by workers and controlling industrial machinery (AD3); in purple, fleets of mobile robots interacting with the factory plant and extending network coverage to outdoor remote areas (AD4).
• Figure 2: Experimental for 4. The focus of this is on mitigating risks associated with an Autonomous Mobile Robot (), which autonomously navigates industrial spaces and transports hazardous liquids. Although the is equipped with emergency systems (e.g., proximity sensors and lasers) that trigger abrupt stops to avoid collisions, sudden braking can lead to liquid spills, posing a threat to human operators. The block diagram of the considered solution is illustrated in a). The is equipped with a motion sensor, and the collected data are sent to a local board powered by a power bank. The board transmits the data to a local server via a private , consisting of a dedicated Radio Access Network () and Core Network (). The local server, housed within the same rack as the , hosts algorithms that process real-time motion data to predict potential spills. An example of a prediction is shown in b), where the x-axis acceleration (Ax in the legend) is plotted over time, with time discretized based on the 10 ms acquisition periodicity of the motion sensor. The yellow vertical bar indicates the instant of the anticipated -based prediction, while the gray label marks the actual moment of the spill. When a risk is detected, the system utilizes the connection to activate a custom-built gripper to secure the liquid (represented in our tests by a water bottle), thereby ensuring operator safety and maintaining operational efficiency. In c), the left image shows the result when the is inactive (i.e., the water bottle falls due to a sudden obstruction), while the right photo demonstrates how the proposed solution successfully prevents this outcome by accurately predicting the spill and activating the gripper in advance.