Citation: Abdullah, Y.; Movahedi, Z. QoS-Aware and Energy Data Management in Industrial IoT. Computers 2023, 12, 203. https://doi.org/10.3390/ computers12100203 Academic Editor: Sergio Correia Received: 19 August 2023 Revised: 19 September 2023 Accepted: 22 September 2023 Published: 10 October 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). computers Article QoS-Aware and Energy Data Management in Industrial IoT Yarob Abdullah and Zeinab Movahedi * School of Computer Engineering, University of Science and Technology of Iran, Tehran 1684613114, Iran; yarob.abdullah@comp.iust.ac.ir * Correspondence: zmovahedi@iust.ac.ir Abstract: Two crucial challenges in Industry 4.0 involve maintaining critical latency requirements for data access and ensuring efficient power consumption by field devices. Traditional centralized indus- trial networks that provide rudimentary data distribution capabilities may not be able to meet such stringent requirements. These requirements cannot be met later due to connection or node failures or extreme performance decadence. To address this problem, this paper focuses on resource-constrained networks of Internet of Things (IoT) systems, exploiting the presence of several more powerful nodes acting as distributed local data storage proxies for every IoT set. To increase the battery lifetime of the network, a number of nodes that are not included in data transmission or data storage are turned off. In this paper, we investigate the issue of maximizing network lifetime, and consider the restrictions on data access latency. For this purpose, data are cached distributively in proxy nodes, leading to a reduction in energy consumption and ultimately maximizing network lifetime. To address this problem, we introduce an energy-aware data management method (EDMM); with the goal of extending network lifetime, select IoT nodes are designated to save data distributively. Our proposed approach (1) makes sure that data access latency is underneath a specified threshold and (2) performs well with respect to network lifetime compared to an offline centralized heuristic algorithm. Keywords: data access latency; energy-aware data management; Industry 4.0; IoT; maximizing network lifetime; proxy node 1. Introduction One of the most important improvements in the recent technological universe is the IoT. The IoT involves connecting and integrating billions of smart devices and networks, such as wireless sensor networks (WSNs), to the internet. This creates networks that can share and interchange data to increase performance and, ultimately, individual interaction. IoT applications span a wide range of fields, including transportation, smart building control, energy management through smart meters, healthcare services, and home automation [1]. Industrial automation is currently undergoing a significant transformation, thanks to the advent of IoT technology in industrial applications. This transformation has be- come possible due to recent technological advancements that enable extensive and precise interconnectivity. Efforts to automate processes independently of continuous human in- tervention rely on the seamless flow of data between sensors, controllers, and actuators on a large scale. In recent times, the focus has been on developing and optimizing data interchange and distribution schemes within industrial structures. Data generated in this context are typically transmitted wirelessly to a central network controller. The controller then analyzes the received data and, when necessary, adjusts network pathways and data transfer mechanisms. This process not only optimizes resource allocation but also influences physical environments through actuator systems. In industrial networks, topologies and connectivity can vary due to connection or sensor node defeats. Additionally, highly dynamic situations, where connection efficiency differs significantly from central scheduling calculations, may result in sub-optimal effi- ciency and possibly lead to the construction of non-guaranteed application needs. These Computers 2023, 12, 203. https://doi.org/10.3390/computers12100203 https://www.mdpi.com/journal/computers