scholarly journals A Novel Synchronization Scheme Based on a Dynamic Superframe for an Industrial Internet of Things in Underground Mining

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 504 ◽  
Author(s):  
Aiping Tan ◽  
Yuhuai Peng ◽  
Xianli Su ◽  
Haibin Tong ◽  
Qingxu Deng
Keyword(s):  
Energy Consumption ◽  
Underground Mining ◽  
Clock Synchronization ◽  
Base Station ◽  
Industrial Internet Of Things ◽  
Hybrid Topology ◽  
Industrial Internet ◽  
Synchronization Scheme ◽  
Synchronization Accuracy ◽  
Dynamic Superframe

The Industrial Internet of Things (IIoT) has a wide range of applications, such as intelligent manufacturing, production process optimization, production equipment monitoring, etc. Due to the complex circumstance in underground mining, the performance of WSNs faces enormous challenges, such as data transmission delay, packet loss rate, and so on. The MAC (Media Access Control) protocol based on TDMA (Time Division Multiple Access) is an effective solution, but it needs to ensure the clock synchronization between the transmission nodes. As the key technology of IIoT, synchronization needs to consider the factors of tunnel structure, energy consumption, etc. Traditional synchronization methods, such as TPSN (Timing-sync Protocol for Sensor Networks), RBS (Reference Broadcast Synchronization), mainly focus on improving synchronization accuracy, ignoring the impact of the actual environment, cannot be directly applied to the IIoT in underground mining. In underground mining, there are two kinds of nodes: base-station node and sensor node, which have different topologies, so they constitute a hybrid topology. In this paper, according to hybrid topology of unground mining, a clock synchronization scheme based on a dynamic superframe is designed. In this scheme, the base-station and sensor have different synchronization methods, improving the TPSN and RBS algorithm, respectively, and adjusts the period of the superframe dynamically by estimating the clock offset. The synchronization scheme presented in this paper can reduce the network communication overhead and energy consumption, ensuring the synchronization accuracy. Based on theCC2530 (Asystem-on-chip solution for IEEE 802.15.4, Zigbee and RF4CE applications), the experiments are compared and analyzed, including synchronization accuracy, energy consumption, and robustness tests. Experimental results show that the synchronization accuracy of the proposed method is at least 11% higher than that of the existing methods, and the energy consumption can be reduced by approximately 13%. At the same time, the proposed method has better robustness.

scholarly journals Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 282 ◽  
Author(s):  
Adrian Korodi ◽  
Ruben Crisan ◽  
Andrei Nicolae ◽  
Ioan Silea
Keyword(s):  
Drinking Water ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Non Invasive ◽  
Data Accumulation ◽  
Industrial Internet ◽  
Reduce Energy Consumption ◽  
And Control

The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

A Robust Time Synchronization Scheme for Industrial Internet of Things

2018 ◽  
Vol 14 (8) ◽  
pp. 3570-3580 ◽  
Author(s):  
Tie Qiu ◽  
Yushuang Zhang ◽  
Daji Qiao ◽  
Xiaoyun Zhang ◽  
Mathew L. Wymore ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Time Synchronization ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Synchronization Scheme

scholarly journals Complete Automation of an Energy Consumption Reduction Strategy from a Water Treatment and Distribution Facility, Inside an Industrial Internet of Things-Compliant Proactive Historian Application

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2569
Author(s):  
Andrei Nicolae ◽  
Adrian Korodi ◽  
Ioan Silea
Keyword(s):  
Energy Consumption ◽  
Water Treatment ◽  
Internet Of Things ◽  
Information Exchange ◽  
Software Tool ◽  
Industrial Internet Of Things ◽  
External Interference ◽  
Industrial Landscape ◽  
Industrial Internet ◽  
Complete Automation

The Industrial Internet of Things and Industry 4.0 paradigms are steering the industrial landscape towards better connected entities, superior interoperability and information exchange, which lays the basis for developing more intelligent solutions that are already starting to bring numerous benefits. The current research aligns to this course, in an attempt to build an automated and autonomous software tool, capable of reducing the energy consumption of a water treatment and distribution facility, by optimizing the water sources usage. Based on several previous researches, the present paper details both the complete automation of the optimizing strategy inside a proactive historian application and the tests executed with the finished solution. Possessing the abilities to directly influence the monitored system in a non-invasive manner, and to link all the sequences of the algorithm automatically, the solution is now ready for long-term functioning without any external interference.

scholarly journals Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2122
Author(s):  
David Todoli-Ferrandis ◽  
Javier Silvestre-Blanes ◽  
Víctor Sempere-Payá
Keyword(s):  
Energy Consumption ◽  
Industry 4.0 ◽  
Smart Cities ◽  
Simulation Software ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Channel Interference ◽  
Class B ◽  
Rate Optimization ◽  
The Impact

The adoption of LoRaWAN as a technology for wireless deployments in many applications, such as smart cities or industry 4.0, still presents challenges such as energy consumption, robustness, or reduced throughput in harsh, noisy scenarios. Class B is a MAC mode that allows better performance in downlink traffic but has difficulties regarding scalability and its response to channel interference. This article introduces, via simulation software, the possibility of testing deployments, adding interference sources that model industrial scenarios, and proposes an adaptive data rate (ADR) mechanism to enhance the operation for downlink and class B devices, called DROB (downlink rate optimization for class B) to study the impact of these conditions in a network with detailed event characterization.

scholarly journals Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Qian You ◽  
Bing Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Particle Swarm ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Resource Constrained ◽  
Swarm Optimization ◽  
Industrial Internet ◽  
Task Offloading

AbstractAs a new form of computing based on the core technology of cloud computing and built on edge infrastructure, edge computing can handle computing-intensive and delay-sensitive tasks. In mobile edge computing (MEC) assisted by 5G technology, offloading computing tasks of edge devices to the edge servers in edge network can effectively reduce delay. Designing a reasonable task offloading strategy in a resource-constrained multi-user and multi-MEC system to meet users’ needs is a challenge issue. In industrial internet of things (IIoT) environment, considering the rapid increase of industrial edge devices and the heterogenous edge servers, a particle swarm optimization (PSO)-based task offloading strategy is proposed to offload tasks from resource-constrained edge devices to edge servers with energy efficiency and low delay style. A multi-objective optimization problem that considers time delay, energy consumption and task execution cost is proposed. The fitness function of the particle represents the total cost of offloading all tasks to different MEC servers. The offloading strategy based on PSO is compared with the genetic algorithm (GA) and the simulated annealing algorithm (SA) through simulation experiments. The experimental results show that the task offloading strategy based on PSO can reduce the delay of the MEC server, balance the energy consumption of the MEC server, and effectively realize the reasonable resource allocation.

Domestic Engineering - Industry 4.0 Technology Transition Problems

2019 ◽  
pp. 11-20
Author(s):  
E. N. Lapteva ◽  
O. V. Nasarochkina
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Technology Development ◽  
Engineering Industry ◽  
Problem Analysis ◽  
Industrial Internet Of Things ◽  
Technology Transition ◽  
Industrial Internet ◽  
Qualified Personnel ◽  
Virtual And Augmented Reality

The paper deals with problem analysis due to domestic engineering transition to the Industry 4.0 technology. It presents such innovative technologies as additive manufacturing (3D-printing), Industrial Internet of Things, total digitization of manufacturing (digital description of products and processes, virtual and augmented reality). Among the main highlighted problems the authors include a lack of unification and standardization at this stage of technology development; incompleteness of both domestic and international regulatory framework; shortage of qualified personnel.

An Integrated Legacy of Modbus Devices to Industrial Internet of Things: Approach for Smart Industries

2020 ◽  
Author(s):  
Karthik Muthineni
Keyword(s):  
Internet Of Things ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Industrial Internet Of Things ◽  
Intelligent Decision Making ◽  
Internet Of Everything ◽  
Industrial Internet ◽  
Intelligent Decision ◽  
Network Platform ◽  
Smart Factories

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.

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