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Klaina H, Picallo I, Lopez-Iturri P, Biurrun A, Alejos AV, Azpilicueta L, Socorro-Leránoz AB, Falcone F. IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop. Sensors (Basel) 2024; 24:712. [PMID: 38276403 PMCID: PMC10818594 DOI: 10.3390/s24020712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities.
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Affiliation(s)
- Hicham Klaina
- Electric, Electronic and Communication Engineering Department, Public University of Navarre, 31006 Pamplona, Spain; (H.K.); (I.P.); (P.L.-I.); (A.B.); (L.A.); (A.B.S.-L.)
| | - Imanol Picallo
- Electric, Electronic and Communication Engineering Department, Public University of Navarre, 31006 Pamplona, Spain; (H.K.); (I.P.); (P.L.-I.); (A.B.); (L.A.); (A.B.S.-L.)
| | - Peio Lopez-Iturri
- Electric, Electronic and Communication Engineering Department, Public University of Navarre, 31006 Pamplona, Spain; (H.K.); (I.P.); (P.L.-I.); (A.B.); (L.A.); (A.B.S.-L.)
- Institute of Smart Cities, Public University of Navarre, 31006 Pamplona, Spain
| | - Aitor Biurrun
- Electric, Electronic and Communication Engineering Department, Public University of Navarre, 31006 Pamplona, Spain; (H.K.); (I.P.); (P.L.-I.); (A.B.); (L.A.); (A.B.S.-L.)
| | - Ana V. Alejos
- Signal and Communications Theory Department, University of Vigo, 36310 Vigo, Spain;
| | - Leyre Azpilicueta
- Electric, Electronic and Communication Engineering Department, Public University of Navarre, 31006 Pamplona, Spain; (H.K.); (I.P.); (P.L.-I.); (A.B.); (L.A.); (A.B.S.-L.)
- Institute of Smart Cities, Public University of Navarre, 31006 Pamplona, Spain
| | - Abián B. Socorro-Leránoz
- Electric, Electronic and Communication Engineering Department, Public University of Navarre, 31006 Pamplona, Spain; (H.K.); (I.P.); (P.L.-I.); (A.B.); (L.A.); (A.B.S.-L.)
- Institute of Smart Cities, Public University of Navarre, 31006 Pamplona, Spain
| | - Francisco Falcone
- Electric, Electronic and Communication Engineering Department, Public University of Navarre, 31006 Pamplona, Spain; (H.K.); (I.P.); (P.L.-I.); (A.B.); (L.A.); (A.B.S.-L.)
- Institute of Smart Cities, Public University of Navarre, 31006 Pamplona, Spain
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Nuevo León, Mexico
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Javed S, Usman M, Sandin F, Liwicki M, Mokayed H. Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT. Sensors (Basel) 2023; 23:8427. [PMID: 37896522 PMCID: PMC10610665 DOI: 10.3390/s23208427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 09/28/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023]
Abstract
The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.
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Affiliation(s)
- Saleha Javed
- Machine Learning, Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden (M.L.)
| | - Muhammad Usman
- Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
| | - Fredrik Sandin
- Machine Learning, Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden (M.L.)
| | - Marcus Liwicki
- Machine Learning, Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden (M.L.)
| | - Hamam Mokayed
- Machine Learning, Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden (M.L.)
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Bin Mofidul R, Alam MM, Rahman MH, Jang YM. Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI. Sensors (Basel) 2022; 22:8980. [PMID: 36433575 PMCID: PMC9717730 DOI: 10.3390/s22228980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 06/16/2023]
Abstract
The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end.
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Asaithambi S, Ravi L, Kotb H, Milyani AH, Azhari AA, Nallusamy S, Varadarajan V, Vairavasundaram S. An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things. Sensors (Basel) 2022; 22:7917. [PMID: 36298266 PMCID: PMC9607010 DOI: 10.3390/s22207917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/11/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.
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Affiliation(s)
- Sasikumar Asaithambi
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600062, Tamil Nadu, India
| | - Logesh Ravi
- SENSE, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
- Data Engineering Research Group (DERG–SENSE), Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
| | - Hossam Kotb
- Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
| | - Ahmad H. Milyani
- Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | | | - Senthilkumar Nallusamy
- Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Karur 639113, Tamil Nadu, India
| | - Vijayakumar Varadarajan
- School of Computer Science and Engineering, University of New South Wales, Sydney 2052, Australia
- Ajeenkya DY Patil University, Pune 412105, Maharashtra, India
- Swiss School of Business Management, SSBM, 1213 Geneva, Switzerland
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Alharbi N, Mackenzie L, Pezaros D. Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy. Sensors (Basel) 2022; 22:7462. [PMID: 36236561 PMCID: PMC9570556 DOI: 10.3390/s22197462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/29/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.
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Affiliation(s)
- Nouf Alharbi
- School of Computing Science, University of Glasgow, Glasgow G12 8LT, UK
- School of Computing Science, Taibah University, Madinah 42353, Saudi Arabia
| | - Lewis Mackenzie
- School of Computing Science, University of Glasgow, Glasgow G12 8LT, UK
| | - Dimitrios Pezaros
- School of Computing Science, University of Glasgow, Glasgow G12 8LT, UK
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Yin Z, Xu F, Li Y, Fan C, Zhang F, Han G, Bi Y. A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing. Sensors (Basel) 2022; 22:s22041555. [PMID: 35214456 PMCID: PMC8880266 DOI: 10.3390/s22041555] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/05/2022] [Accepted: 02/15/2022] [Indexed: 11/23/2022]
Abstract
With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.
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Affiliation(s)
- Zhenyu Yin
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (F.X.); (Y.L.); (C.F.); (F.Z.)
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China
- Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software, Shenyang 110168, China
- Correspondence:
| | - Fulong Xu
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (F.X.); (Y.L.); (C.F.); (F.Z.)
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China
- Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software, Shenyang 110168, China
| | - Yue Li
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (F.X.); (Y.L.); (C.F.); (F.Z.)
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China
- Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software, Shenyang 110168, China
| | - Chao Fan
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (F.X.); (Y.L.); (C.F.); (F.Z.)
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China
- Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software, Shenyang 110168, China
| | - Feiqing Zhang
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (F.X.); (Y.L.); (C.F.); (F.Z.)
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China
- Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software, Shenyang 110168, China
| | - Guangjie Han
- College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;
- Changzhou Key Laboratory of Internet of Things Technology for Intelligent River and Lake, Changzhou 213022, China
| | - Yuanguo Bi
- School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China;
- Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang 110167, China
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Abstract
Nowadays, blockchain technology and industry has developed rapidly all over the world, which is inseparable from continuous innovation and improvement on smart contract technology. Therefore, by summarizing the working principle and application research status of blockchain smart contract, this paper analyzes the development and challenges of smart contract. Firstly, we introduce the model and operation principle of blockchain smart contract for the overall architecture, analyze the deployment process of smart contract with Ethereum, Hyperledger Fabric and EOSIO, and make a comparative analysis from the technical level. And taking Byteball, InterValue and IOTA platforms as examples, we introduce the deployment process and application potential for DAG-based blockchain smart contract. Additionally, we also summarize the application research of smart contract for international and Blockchain Oracle, and discuss its innovative application and development trend in the future. Secondly, we introduce the application status of smart contract with Ethereum and Hyperledger Fabric platforms from the aspects of financial transactions, Internet of things, medical applications, and supply chain, and further discuss EOS (enterprise operation system), Blockchain Oracle and other application fields. Furthermore, we introduce the application advantages and challenges to smart contract for industrial Internet from the fields of manufacturing, food industry, industrial Internet of things and industry 4.0. Finally, we discuss the challenges faced by smart contract with technical issues, analyzes the impact on large-scale applications and mining system on the sustainable development of smart contract, and looks forward to the future research direction of blockchain smart contract.
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Affiliation(s)
- Shi-Yi Lin
- Collage of Information Science and Electronic Technology, Jiamusi University, Jiamusi, 154007 Heilongjiang Province China
| | - Lei Zhang
- Collage of Information Science and Electronic Technology, Jiamusi University, Jiamusi, 154007 Heilongjiang Province China
| | - Jing Li
- Collage of Information Science and Electronic Technology, Jiamusi University, Jiamusi, 154007 Heilongjiang Province China
| | - Li-li Ji
- Science and Technology Department, Jiamusi University, Jiamusi, 154007 Heilongjiang Province China
| | - Yue Sun
- Collage of Information Science and Electronic Technology, Jiamusi University, Jiamusi, 154007 Heilongjiang Province China
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Ioana A, Korodi A. DDS and OPC UA Protocol Coexistence Solution in Real-Time and Industry 4.0 Context Using Non-Ideal Infrastructure. Sensors (Basel) 2021; 21:s21227760. [PMID: 34833838 PMCID: PMC8620391 DOI: 10.3390/s21227760] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/17/2021] [Accepted: 11/18/2021] [Indexed: 11/16/2022]
Abstract
Continuing the evolution towards Industry 4.0, the industrial communication protocols represent a significant topic of interest, as real-time data exchange between multiple devices constitute the pillar of Industrial Internet of Things (IIoT) scenarios. Although the legacy protocols are still persistent in the industry, the transition was initiated by the key Industry 4.0 facilitating protocol, the Open Platform Communication Unified Architecture (OPC UA). OPC UA has to reach the envisioned applicability, and it therefore has to consider coexistence with other emerging real-time oriented protocols in the production lines. The Data Distribution Service (DDS) will certainly be present in future architectures in some areas as robots, co-bots, and compact units. The current paper proposes a solution to evaluate the real-time coexistence of OPC UA and DDS protocols, functioning in parallel and in a gateway context. The purpose is to confirm the compatibility and feasibility between the two protocols alongside a general definition of criteria and expectations from an architectural point of view, pointing out advantages and disadvantages in a neutral manner, shaping a comprehensive view of the possibilities. The researched architecture is meant to comply with both performance comparison scenarios and interaction scenarios over a gateway application. Considering the industrial tendencies, the developed solution is applied using non-ideal infrastructures to provide a more feasible and faster applicability in the production lines.
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Latif S, Driss M, Boulila W, Huma ZE, Jamal SS, Idrees Z, Ahmad J. Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions. Sensors (Basel) 2021; 21:7518. [PMID: 34833594 PMCID: PMC8625089 DOI: 10.3390/s21227518] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 10/29/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022]
Abstract
The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.
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Affiliation(s)
- Shahid Latif
- School of Information Science and Engineering, Fudan University, Shanghai 200433, China; (S.L.); (Z.I.)
| | - Maha Driss
- Security Engineering Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia;
- RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba 2010, Tunisia;
| | - Wadii Boulila
- RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba 2010, Tunisia;
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia
| | - Zil e Huma
- Department of Electrical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan;
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi Arabia;
| | - Zeba Idrees
- School of Information Science and Engineering, Fudan University, Shanghai 200433, China; (S.L.); (Z.I.)
| | - Jawad Ahmad
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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Miśkowicz M. Unfairness of Random Access with Collision Avoidance in Industrial Internet of Things Networks. Sensors (Basel) 2021; 21:7135. [PMID: 34770441 DOI: 10.3390/s21217135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 10/19/2021] [Accepted: 10/23/2021] [Indexed: 11/25/2022]
Abstract
This paper is focused on the analysis of unfairness of random media access in Local Operating Networks (LON), which is one of the commercial platforms of the Industrial Internet of Things (IIoT). The unfairness in accessing the LON channel is introduced by a collision avoidance mechanism in the predictive p-persistent CSMA protocol adopted at the media access control layer. The study on the bandwidth share in predictive p-persistent CSMA calls for the analysis of multiple memoryless backoff. In this paper, it is shown that the channel access in LON systems is unfair in the short term for medium traffic load conditions, and in the long term for heavy loaded networks. Furthermore, it is explained that the average bandwidth allocated to a particular node is determined implicitly by the load scenario, while an actual node bandwidth fluctuates in time according to stochastic dynamics of the predictive p-persistent CSMA. Next, it is formally proven that the average bandwidth available to a node is a linear function of its backoff state and does not depend on backoff states of the other stations. Finally, it is demonstrated that possibly unfair bandwidth share in LON networks determined implicitly by load scenario is stable because, with lowering a fraction of actual network bandwidth accessible by a given station, the probability to decrease it in the future also drops.
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Benedick PL, Robert J, Le Traon Y. A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings. Sensors (Basel) 2021; 21:6195. [PMID: 34577398 DOI: 10.3390/s21186195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 11/16/2022]
Abstract
Artificial Intelligence (AI) is one of the hottest topics in our society, especially when it comes to solving data-analysis problems. Industry are conducting their digital shifts, and AI is becoming a cornerstone technology for making decisions out of the huge amount of (sensors-based) data available in the production floor. However, such technology may be disappointing when deployed in real conditions. Despite good theoretical performances and high accuracy when trained and tested in isolation, a Machine-Learning (M-L) model may provide degraded performances in real conditions. One reason may be fragility in treating properly unexpected or perturbed data. The objective of the paper is therefore to study the robustness of seven M-L and Deep-Learning (D-L) algorithms, when classifying univariate time-series under perturbations. A systematic approach is proposed for artificially injecting perturbations in the data and for evaluating the robustness of the models. This approach focuses on two perturbations that are likely to happen during data collection. Our experimental study, conducted on twenty sensors’ datasets from the public University of California Riverside (UCR) repository, shows a great disparity of the models’ robustness under data quality degradation. Those results are used to analyse whether the impact of such robustness can be predictable—thanks to decision trees—which would prevent us from testing all perturbations scenarios. Our study shows that building such a predictor is not straightforward and suggests that such a systematic approach needs to be used for evaluating AI models’ robustness.
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Krause J, Grüger H, Gebauer L, Zheng X, Knobbe J, Pügner T, Kicherer A, Gruna R, Längle T, Beyerer J. SmartSpectrometer-Embedded Optical Spectroscopy for Applications in Agriculture and Industry. Sensors (Basel) 2021; 21:s21134476. [PMID: 34208883 PMCID: PMC8271752 DOI: 10.3390/s21134476] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/17/2021] [Accepted: 06/23/2021] [Indexed: 11/16/2022]
Abstract
The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its interface is presented. The advantages of the SmartSpectrometer are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field.
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Affiliation(s)
- Julius Krause
- Fraunhofer IOSB, Karlsruhe, Institute of Optronics, System Technologies and Image Exploitation, 76131 Karlsruhe, Germany; (J.K.); (R.G.); (T.L.)
| | - Heinrich Grüger
- Fraunhofer IPMS, Institute for Photonic Microsystems, 01109 Dresden, Germany; (H.G.); (J.K.); (T.P.)
| | - Lucie Gebauer
- Julius Kühn-Institut, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany; (L.G.); (X.Z.); (A.K.)
| | - Xiaorong Zheng
- Julius Kühn-Institut, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany; (L.G.); (X.Z.); (A.K.)
| | - Jens Knobbe
- Fraunhofer IPMS, Institute for Photonic Microsystems, 01109 Dresden, Germany; (H.G.); (J.K.); (T.P.)
| | - Tino Pügner
- Fraunhofer IPMS, Institute for Photonic Microsystems, 01109 Dresden, Germany; (H.G.); (J.K.); (T.P.)
| | - Anna Kicherer
- Julius Kühn-Institut, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany; (L.G.); (X.Z.); (A.K.)
| | - Robin Gruna
- Fraunhofer IOSB, Karlsruhe, Institute of Optronics, System Technologies and Image Exploitation, 76131 Karlsruhe, Germany; (J.K.); (R.G.); (T.L.)
| | - Thomas Längle
- Fraunhofer IOSB, Karlsruhe, Institute of Optronics, System Technologies and Image Exploitation, 76131 Karlsruhe, Germany; (J.K.); (R.G.); (T.L.)
| | - Jürgen Beyerer
- Fraunhofer IOSB, Karlsruhe, Institute of Optronics, System Technologies and Image Exploitation, 76131 Karlsruhe, Germany; (J.K.); (R.G.); (T.L.)
- Vision and Fusion Laboratory (IES), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
- Correspondence:
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13
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Vera-Pérez J, Silvestre-Blanes J, Sempere-Payá V. TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks. Sensors (Basel) 2021; 21:s21113904. [PMID: 34198793 PMCID: PMC8201249 DOI: 10.3390/s21113904] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/25/2021] [Accepted: 06/03/2021] [Indexed: 12/01/2022]
Abstract
Wireless sensor networks (WSNs) play a key role in the ecosystem of the Industrial Internet of Things (IIoT) and the definition of today’s Industry 4.0. These WSNs have the ability to sensor large amounts of data, thanks to their easy scalability. WSNs allow the deployment of a large number of self-configuring nodes and the ability to automatically reorganize in case of any change in the topology. This huge sensorization capacity, together with its interoperability with IP-based networks, allows the systems of Industry 4.0 to be equipped with a powerful tool with which to digitalize a huge amount of variables in the different industrial processes. The IEEE 802.15.4e standard, together with the access mechanism to the Time Slotted Channel Hopping medium (TSCH) and the dynamic Routing Protocol for Low-Power and Lossy Networks (RPL), allow deployment of networks with the high levels of robustness and reliability necessary in industrial scenarios. However, these configurations have some disadvantages in the deployment and synchronization phases of the networks, since the time it takes to synchronize the nodes is penalized compared to other solutions in which access to the medium is done randomly and without channel hopping. This article proposes an analytical model to characterize the behavior of this type of network, based on TSCH and RPL during the phases of deployment along with synchronization and connection to the RPL network. Through this model, validated by simulation and real tests, it is possible to parameterize different configurations of a WSN network based on TSCH and RPL.
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Affiliation(s)
- Jose Vera-Pérez
- Instituto Tecnológico de Informática, 46022 Valencia, Spain
- Correspondence:
| | - Javier Silvestre-Blanes
- ITI and Departamento de Informática de Sistemas y Computadores (DISCA), Universitat Politècnica de València (UPV), 03801 Alcoy, Spain;
| | - Víctor Sempere-Payá
- ITI and Departamento de Comunicaciones (DCOM), Universitat Politècnica de València (UPV), 46022 Valencia, Spain;
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14
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Ungurean I, Gaitan NC. Software Architecture of a Fog Computing Node for Industrial Internet of Things. Sensors (Basel) 2021; 21:s21113715. [PMID: 34073598 PMCID: PMC8198567 DOI: 10.3390/s21113715] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/20/2021] [Accepted: 05/24/2021] [Indexed: 11/16/2022]
Abstract
In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.
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Affiliation(s)
- Ioan Ungurean
- Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
- MANSiD Integrated Center, Stefan cel Mare University, 720229 Suceava, Romania
- Correspondence: (I.U.); (N.C.G.)
| | - Nicoleta Cristina Gaitan
- Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
- MANSiD Integrated Center, Stefan cel Mare University, 720229 Suceava, Romania
- Correspondence: (I.U.); (N.C.G.)
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15
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Maseda FJ, López I, Martija I, Alkorta P, Garrido AJ, Garrido I. Sensors Data Analysis in Supervisory Control and Data Acquisition (SCADA) Systems to Foresee Failures with an Undetermined Origin. Sensors (Basel) 2021; 21:s21082762. [PMID: 33919787 PMCID: PMC8070775 DOI: 10.3390/s21082762] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/01/2021] [Accepted: 04/12/2021] [Indexed: 01/06/2023]
Abstract
This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.
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Affiliation(s)
- F. Javier Maseda
- Automatic Control Group (ACG), Institute of Research and Development of Processes, Faculty of Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain; (I.M.); (A.J.G.); (I.G.)
- Correspondence: ; Tel.: +34-94-6014354
| | - Iker López
- Intenance, RDT Company, 48100 Munguia, Spain;
| | - Itziar Martija
- Automatic Control Group (ACG), Institute of Research and Development of Processes, Faculty of Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain; (I.M.); (A.J.G.); (I.G.)
| | - Patxi Alkorta
- Engineering School of Gipuzkoa, University of the Basque Country (UPV/EHU), 20600 Eibar, Spain;
| | - Aitor J. Garrido
- Automatic Control Group (ACG), Institute of Research and Development of Processes, Faculty of Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain; (I.M.); (A.J.G.); (I.G.)
| | - Izaskun Garrido
- Automatic Control Group (ACG), Institute of Research and Development of Processes, Faculty of Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain; (I.M.); (A.J.G.); (I.G.)
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16
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Tanuska P, Spendla L, Kebisek M, Duris R, Stremy M. Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0. Sensors (Basel) 2021; 21:s21072376. [PMID: 33805557 PMCID: PMC8037397 DOI: 10.3390/s21072376] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/15/2021] [Accepted: 03/26/2021] [Indexed: 11/16/2022]
Abstract
One of the big problems of today's manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.
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17
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Bertoli A, Cervo A, Rosati CA, Fantuzzi C. Smart Node Networks Orchestration: A New E2E Approach for Analysis and Design for Agile 4.0 Implementation. Sensors (Basel) 2021; 21:1624. [PMID: 33652557 DOI: 10.3390/s21051624] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 02/21/2021] [Accepted: 02/22/2021] [Indexed: 11/16/2022]
Abstract
The field of cyber-physical systems is a growing IT research area that addresses the deep integration of computing, communication and process control, possibly with humans in the loop. The goal of such area is to define modelling, controlling and programming methodologies for designing and managing complex mechatronics systems, also called industrial agents. Our research topic mainly focuses on the area of data mining and analysis by means of multi-agent orchestration of intelligent sensor nodes using internet protocols, providing also web-based HMI visualizations for data interpretability and analysis. Thanks to the rapid spreading of IoT systems, supported by modern and efficient telecommunication infrastructures and new decentralized control paradigms, the field of service-oriented programming finds new application in wireless sensor networks and microservices paradigm: we adopted such paradigm in the implementation of two different industrial use cases. Indeed, we expect a concrete and deep use of such technologies with 5G spreading. In the article, we describe the common software architectural pattern in IoT applications we used for the distributed smart sensors, providing also design and implementation details. In the use case section, the prototypes developed as proof of concept and the KPIs used for the system validation are described to provide a concrete solution overview.
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18
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Ioana A, Burlacu C, Korodi A. Approaching OPC UA Publish-Subscribe in the Context of UDP-Based Multi-Channel Communication and Image Transmission. Sensors (Basel) 2021; 21:1296. [PMID: 33670272 PMCID: PMC7918240 DOI: 10.3390/s21041296] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 02/05/2021] [Accepted: 02/08/2021] [Indexed: 11/16/2022]
Abstract
The Open Platform Communication Unified Architecture (OPC UA) protocol is a key enabler of Industry 4.0 and Industrial Internet of Things (IIoT). OPC UA is already accepted by the industry and its presence is expected to reach more and more fields, applications, and hierarchical levels. Advances within the latest specifications are providing the opportunity to extend the capabilities and the applicability of the protocol, targeting better performances in terms of data volumes, speed, availability, footprint, and security. Continuing previous researches focusing on the publish-subscribe (pub/sub) mechanism and real-time constraints, the current study aims to consider higher data-volumes, approach the multi-channel User Datagram Protocol (UDP)-based communication, and analyze the robustness of the developed mechanism in the context of long-term data transmission. Consequently, the research proposes to extend the applicability of the OPC UA in the context of image transmission. Although highly needed, the image transmission after processing is currently beyond the reach of OPC UA or other legacy industrial protocols, being considered as a separate fraction in the industrial environment. The concept and developments are applied considering both the end-of-line industrial manufacturing process in the automotive sector and the car-to-infrastructure communication. Without special hardware constraints, the obtained results are proven to be appreciable, opening various future perspectives for image transmission using OPC UA.
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Affiliation(s)
| | | | - Adrian Korodi
- Department of Automation and Applied Informatics, Faculty of Automation and Computers, University Politehnica Timisoara, 300223 Timisoara, Romania; (A.I.); (C.B.)
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19
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Paolini G, Guermandi M, Masotti D, Shanawani M, Benassi F, Benini L, Costanzo A. RF-Powered Low-Energy Sensor Nodes for Predictive Maintenance in Electromagnetically Harsh Industrial Environments. Sensors (Basel) 2021; 21:E386. [PMID: 33429868 DOI: 10.3390/s21020386] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/03/2021] [Accepted: 01/05/2021] [Indexed: 02/05/2023]
Abstract
This work describes the design, implementation, and validation of a wireless sensor network for predictive maintenance and remote monitoring in metal-rich, electromagnetically harsh environments. Energy is provided wirelessly at 2.45 GHz employing a system of three co-located active antennas designed with a conformal shape such that it can power, on-demand, sensor nodes located in non-line-of-sight (NLOS) and difficult-to-reach positions. This allows for eliminating the periodic battery replacement of the customized sensor nodes, which are designed to be compact, low-power, and robust. A measurement campaign has been conducted in a real scenario, i.e., the engine compartment of a car, assuming the exploitation of the system in the automotive field. Our work demonstrates that a one radio-frequency (RF) source (illuminator) with a maximum effective isotropic radiated power (EIRP) of 27 dBm is capable of transferring the energy of 4.8 mJ required to fully charge the sensor node in less than 170 s, in the worst case of 112-cm distance between illuminator and node (NLOS). We also show how, in the worst case, the transferred power allows the node to operate every 60 s, where operation includes sampling accelerometer data for 1 s, extracting statistical information, transmitting a 20-byte payload, and receiving a 3-byte acknowledgment using the extremely robust Long Range (LoRa) communication technology. The energy requirement for an active cycle is between 1.45 and 1.65 mJ, while sleep mode current consumption is less than 150 nA, allowing for achieving the targeted battery-free operation with duty cycles as high as 1.7%.
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20
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AlMajed H, AlMogren A. A Secure and Efficient ECC-Based Scheme for Edge Computing and Internet of Things. Sensors (Basel) 2020; 20:E6158. [PMID: 33138018 PMCID: PMC7662995 DOI: 10.3390/s20216158] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/15/2020] [Accepted: 10/26/2020] [Indexed: 11/17/2022]
Abstract
Recent growth in the Internet of Things (IoT) has raised security concerns over the confidentiality of data exchanged between IoT devices and the edge. Many IoT systems adopt asymmetric cryptography to secure their data and communications. A drawback of asymmetric cryptography is the sizeable computation and space requirements. However, elliptic curve cryptography (ECC) is widely used in constrained environments for asymmetric cryptography due its superiority in generating a powerful encryption mechanism with small key sizes. ECC increases device performance and lowers power consumption, meaning it is suitable for diverse applications ranging from the IoT to wireless sensor network (WSN) devices. To ensure the confidentiality and security of data and communications, it is necessary to implement ECC robustly. A special area of focus in this regard is the mapping phase. This study's objective was to propose a tested and trusted scheme that offers authenticated encryption (AE) via enhancing the mapping phase of a plain text to an elliptic curve to resist several encryption attacks such as Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA). The proposed scheme also undertakes evaluation and analysis related to security requirements for specific encryption attributes. Finally, results from a comparison of the proposed scheme and other schemes are presented, evaluating each one's security characteristics and performance measurements. Our scheme is efficient in a way that makes so suitable to the IoT, and in particular to the Industrial IoT and the new Urbanization where the demands for services are huge.
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21
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Ioana A, Korodi A. Improving OPC UA Publish-Subscribe Mechanism over UDP with Synchronization Algorithm and Multithreading Broker Application. Sensors (Basel) 2020; 20:E5591. [PMID: 33003549 DOI: 10.3390/s20195591] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 09/26/2020] [Accepted: 09/27/2020] [Indexed: 11/16/2022]
Abstract
Communication protocols are evolving continuously as the interfacing and interoperability requirements are the foundation of Industry 4.0 and Industrial Internet of Things (IIoT), and the Open Platform Communication Unified Architecture (OPC UA) protocol is a major enabling technology. OPC UA was adopted by the industry, and research is continuously carried out to extend and to improve its capabilities, to fulfil the growing requirements of specific industries and hierarchical levels. Consistent issues that have to be approached are related to the latest specifications and the real-time context that could extend the applicability of the protocol and bring significant benefits in terms of speed, data volumes, footprint, security. The real-time context is essential in the automotive sector and it is highly developed within some specific protocols. The current work approaches first the conceptual analysis to improve the OPC UA interfacing using the Publish-Subscribe mechanism, focusing on real-time constraints and role distribution between entities, and considering some well-founded interfacing strategies from the automotive sector. The conceptual analysis is materialized into a solution that takes OPC UA Publish-Subscribe over User Datagram Protocol (UDP) mechanism to the next level by developing a synchronization algorithm and a multithreading broker application to obtain real time responsiveness and increased efficiency by lowering the publisher and the subscriber footprint and computational effort, reducing the difficulty of sending larger volumes of data for various subscribers and the charge on the network and services in terms of polling and filtering. The proof of concept is evaluated and the results prove the efficiency of the approach and the solution.
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Vermesan O, Bahr R, Ottella M, Serrano M, Karlsen T, Wahlstrøm T, Sand HE, Ashwathnarayan M, Gamba MT. Internet of Robotic Things Intelligent Connectivity and Platforms. Front Robot AI 2020; 7:104. [PMID: 33501271 PMCID: PMC7805974 DOI: 10.3389/frobt.2020.00104] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 07/02/2020] [Indexed: 11/27/2022] Open
Abstract
The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and "things" have evolved significantly. "Things" now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of "intelligent things" (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.
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Affiliation(s)
| | | | | | - Martin Serrano
- Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland
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Yang W, Li W, Cao Y, Luo Y, He L. Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory. Sensors (Basel) 2020; 20:s20164507. [PMID: 32806593 PMCID: PMC7472176 DOI: 10.3390/s20164507] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/06/2020] [Accepted: 08/06/2020] [Indexed: 11/16/2022]
Abstract
In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with a random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.
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Affiliation(s)
- Wenchao Yang
- School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China; (W.Y.); (Y.L.); (L.H.)
| | - Wenfeng Li
- School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China; (W.Y.); (Y.L.); (L.H.)
- Correspondence:
| | - Yulian Cao
- School of Aviation, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Yun Luo
- School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China; (W.Y.); (Y.L.); (L.H.)
| | - Lijun He
- School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China; (W.Y.); (Y.L.); (L.H.)
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24
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Minnetti E, Chiariotti P, Paone N, Garcia G, Vicente H, Violini L, Castellini P. A Smartphone Integrated Hand-Held Gap and Flush Measurement System for in Line Quality Control of Car Body Assembly. Sensors (Basel) 2020; 20:s20113300. [PMID: 32531962 PMCID: PMC7309121 DOI: 10.3390/s20113300] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 06/08/2020] [Accepted: 06/08/2020] [Indexed: 11/25/2022]
Abstract
This paper presents the design and the characterization of a portable laser triangulation measurement system for measuring gap and flush in the car body assembly process. Targeting Human in the Loop (HILT) operations in the manufacturing sector, and in line with the vision of human empowerment with Industry 4.0 technologies, the instrument embeds features to ease operators’ activity and compensate possible misuse that could affect the robustness and the quality of data acquired. The device is based on a smartphone integrated with a miniaturized laser triangulation system installed in a cover. The device embodies additional sensors and control systems in order to guarantee operators’ safety (switching on and off the laser line based on specific conditions), support operators during the measurement execution task, and optimize the image acquisition process for minimizing the uncertainty associated to the measurement. The smartphone performs on-board processing and allows Wi-Fi communication with the plant IT infrastructure. Compliance to Industry 4.0 requirements is guaranteed using OPC-UA (Open Platform Communications—Unified Architecture) communication protocol enabling the exchange of live data with the plant middleware. The smartphone provides also an advanced high-resolution color display and well proven and ergonomic human–machine interfaces, which have been fully exploited in the design. The paper introduces the system optical layout and then presents the algorithms implemented to realize the gap and flush measurement. The paper finally presents the calibration of the instrument and estimates its calibration uncertainty in laboratory conditions. Then it discusses how performance decays when the operator handles the instrument on a reference car body. Finally, it shows the analysis of uncertainty when the device is used on real car bodies of different colors in a production line. It is observed that the measurement uncertainty of the whole measurement chain (measurand + instrument + operator + uncontrolled environmental conditions) is larger than the instrument calibration uncertainty because the measurement process is affected by the operator and the variable conditions of the production line.
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Affiliation(s)
- Elisa Minnetti
- Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Via Brecce Bianche, 6013 Ancona, Italy; (E.M.); (N.P.); (L.V.); (P.C.)
| | - Paolo Chiariotti
- Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Via Brecce Bianche, 6013 Ancona, Italy; (E.M.); (N.P.); (L.V.); (P.C.)
- Correspondence:
| | - Nicola Paone
- Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Via Brecce Bianche, 6013 Ancona, Italy; (E.M.); (N.P.); (L.V.); (P.C.)
| | - Gisela Garcia
- Volkswagen Autoeuropa, 2954-024 Q.ta do Anjo, Portugal; (G.G.); (H.V.)
| | - Helder Vicente
- Volkswagen Autoeuropa, 2954-024 Q.ta do Anjo, Portugal; (G.G.); (H.V.)
| | - Luca Violini
- Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Via Brecce Bianche, 6013 Ancona, Italy; (E.M.); (N.P.); (L.V.); (P.C.)
| | - Paolo Castellini
- Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Via Brecce Bianche, 6013 Ancona, Italy; (E.M.); (N.P.); (L.V.); (P.C.)
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Lou P, Shi L, Zhang X, Xiao Z, Yan J. A Data-driven Adaptive Sampling Method Based on Edge Computing. Sensors (Basel) 2020; 20:E2174. [PMID: 32290534 DOI: 10.3390/s20082174] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 11/16/2022]
Abstract
The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and ignore the impact of changes of sampling objects during the data acquisition. For the problem of sampling distortion, edge data redundancy and energy consumption caused by constant sampling frequency of sensors in the IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper. The method uses the latest data collected by the sensors at the edge node for linear fitting and adjusts the next sampling frequency according to the linear median jitter sum and adaptive sampling strategy. An edge data acquisition platform is established to verify the validity of the method. According to the experimental results, the proposed method is more effective than other adaptive sampling methods. Compared with constant sampling frequency, the proposed method can reduce the edge data redundancy and energy consumption by more than 13.92% and 12.86%, respectively.
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Xu X, Zeng Z, Yang S, Shao H. A Novel Blockchain Framework for Industrial IoT Edge Computing. Sensors (Basel) 2020; 20:E2061. [PMID: 32272555 DOI: 10.3390/s20072061] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/21/2020] [Accepted: 03/28/2020] [Indexed: 12/03/2022]
Abstract
With the rapid development of industrial internet of thing (IIoT), the distributed topology of IIoT and resource constraints of edge computing conduct new challenges to traditional data storage, transmission, and security protection. A distributed trust and allocated ledger of blockchain technology are suitable for the distributed IIoT, which also becomes an effective method for edge computing applications. This paper proposes a resource constrained Layered Lightweight Blockchain Framework (LLBF) and implementation mechanism. The framework consists of a resource constrained layer (RCL) and a resource extended layer (REL) blockchain used in IIoT. We redesign the block structure and size to suit to IIoT edge computing devices. A lightweight consensus algorithm and a dynamic trust right algorithm is developed to improve the throughput of blockchain and reduce the number of transactions validated in new blocks respectively. Through a high throughput management to guarantee the transaction load balance of blockchain. Finally, we conducted kinds of blockchain simulation and performance experiments, the outcome indicated that the method have a good performance in IIoT edge application.
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Sun S, Zheng X, Gong B, García Paredes J, Ordieres-Meré J. Healthy Operator 4.0: A Human Cyber-Physical System Architecture for Smart Workplaces. Sensors (Basel) 2020; 20:E2011. [PMID: 32260123 PMCID: PMC7180548 DOI: 10.3390/s20072011] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 03/31/2020] [Accepted: 04/01/2020] [Indexed: 11/25/2022]
Abstract
Recent advances in technology have empowered the widespread application of cyber-physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to Vhuman-cyber-physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits.
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Affiliation(s)
- Shengjing Sun
- Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain; (S.S.); (X.Z.); (J.G.P.)
- Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Xiaochen Zheng
- Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain; (S.S.); (X.Z.); (J.G.P.)
- ICT for Sustainable Manufacturing, SCI-STI-DK, École polytechnique fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Bing Gong
- Jülich Supercomputing Center, Forschungszentrum Jülich GmbH, Wilhelm-Wohnen-Str, 52428 Jülich, Germany;
| | - Jorge García Paredes
- Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain; (S.S.); (X.Z.); (J.G.P.)
| | - Joaquín Ordieres-Meré
- Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain; (S.S.); (X.Z.); (J.G.P.)
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28
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Kaya MC, Saeedi Nikoo M, Schwartz ML, Oguztuzun H. Internet of Measurement Things Architecture: Proof of Concept with Scope of Accreditation. Sensors (Basel) 2020; 20:E503. [PMID: 31963169 DOI: 10.3390/s20020503] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 01/06/2020] [Accepted: 01/13/2020] [Indexed: 11/30/2022]
Abstract
Many industries, such as manufacturing, aviation, and power generation, employ sensitive measurement devices to be calibrated by certified experts. The diversity and sophistication of measurement devices and their calibration needs require networked and automated solutions. Internet of Measurement Things (IoMT) is an architectural framework that is based on the Industrial Internet of Things for the calibration industry. This architecture involves a layered model with a cloud-centric middle layer. In this article, the realization of this conceptual architecture is described. The applicability of the IoMT architecture in the calibration industry is shown through an editor application for Scope of Accreditation. The cloud side of the implementation is deployed to Microsoft Azure. The editor itself is created as a cloud service, and IoT Hub is used to collect data from calibration laboratories. By adapting the IoMT architecture to a commonly used cloud platform, considerable progress is achieved to encompass Metrology data and serve the majority of the stakeholders.
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29
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Tidrea A, Korodi A, Silea I. Cryptographic Considerations for Automation and SCADA Systems Using Trusted Platform Modules. Sensors (Basel) 2019; 19:s19194191. [PMID: 31569636 PMCID: PMC6806326 DOI: 10.3390/s19194191] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 09/19/2019] [Accepted: 09/25/2019] [Indexed: 11/16/2022]
Abstract
The increased number of cyber threats against the Supervisory Control and Data Acquisition (SCADA) and automation systems in the Industrial-Internet-of-Things (IIoT) and Industry 4.0 era has raised concerns in respect to the importance of securing critical infrastructures and manufacturing plants. The evolution towards interconnection and interoperability has expanded the vulnerabilities of these systems, especially in the context of the widely spread legacy standard protocols, by exposing the data to the outside network. After gaining access to the system data by launching a variety of attacks, an intruder can cause severe damage to the industrial process in place. Hence, this paper attempts to respond to the security issue caused by legacy structures using insecure communication protocols (e.g., Modbus TCP, DNP3, S7), presenting a different perspective focused on the capabilities of a trusted platform module (TPM). Furthermore, the intent is to assure the authenticity of the data transmitted between two entities on the same (horizontal interoperation) or different (vertical interoperation) hierarchical levels communicating through Modbus TCP protocol based on functionalities obtained by integrating trusted platform modules. From the experimental results perspective, the paper aims to show the advantages of integrating TPMs in automation/SCADA systems in terms of security. Two methods are proposed in order to assure the authenticity of the messages which are transmitted, respectively the study presents the measurements related to the increased time latency introduced due to the proposed concept.
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Affiliation(s)
- Alexandra Tidrea
- Department of Automation and Applied Informatics, Faculty of Automation and Computers, University Politehnica Timisoara, 300223 Timisoara, Romania.
| | - Adrian Korodi
- Department of Automation and Applied Informatics, Faculty of Automation and Computers, University Politehnica Timisoara, 300223 Timisoara, Romania.
| | - Ioan Silea
- Department of Automation and Applied Informatics, Faculty of Automation and Computers, University Politehnica Timisoara, 300223 Timisoara, Romania.
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30
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Han W, Tian Z, Shi W, Huang Z, Li S. Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things. Sensors (Basel) 2019; 19:E2804. [PMID: 31234500 DOI: 10.3390/s19122804] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Revised: 06/12/2019] [Accepted: 06/14/2019] [Indexed: 11/17/2022]
Abstract
. In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a complete set for processing, makes full use of the relationship among the objects, selects only one "representative" object for continuous monitoring, establishes certain constraints to ensure correctness, and reduces communication overheads by maintaining the overheads of constraints in exchange for a reduction in the number of monitored objects. Experiments on real data sets show that LP-DDAM can reduce communication overheads by approximately 70% when compared to an equivalent method that continuously monitors all objects under the same conditions.
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31
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Park B, Nah J, Choi JY, Yoon IJ, Park P. Transmission Scheduling Schemes of Industrial Wireless Sensors for Heterogeneous Multiple Control Systems. Sensors (Basel) 2018; 18:E4284. [PMID: 30563135 DOI: 10.3390/s18124284] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 11/30/2018] [Accepted: 12/01/2018] [Indexed: 11/30/2022]
Abstract
The transmission scheduling scheme of wireless networks for industrial control systems is a crucial design component since it directly affects the stability of networked control systems. In this paper, we propose a novel transmission scheduling framework to guarantee the stability of heterogeneous multiple control systems over unreliable wireless channels. Based on the explicit control stability conditions, a constrained optimization problem is proposed to maximize the minimum slack of the stability constraint for the heterogeneous control systems. We propose three transmission scheduling schemes, namely centralized stationary random access, distributed random access, and Lyapunov-based scheduling scheme, to solve the constrained optimization problem with a low computation cost. The three proposed transmission scheduling schemes were evaluated on heterogeneous multiple control systems with different link conditions. One interesting finding is that the proposed centralized Lyapunov-based approach provides almost ideal performance in the context of control stability. Furthermore, the distributed random access is still useful for the small number of links since it also reduces the operational overhead without significantly sacrificing the control performance.
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32
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Qi J, Wang Z, Xu B, Wu M, Gao Z, Sun Y. QoS-Driven Adaptive Trust Service Coordination in the Industrial Internet of Things. Sensors (Basel) 2018; 18:E2449. [PMID: 30060539 DOI: 10.3390/s18082449] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 07/25/2018] [Accepted: 07/26/2018] [Indexed: 11/17/2022]
Abstract
The adaptive coordination of trust services can provide highly dependable and personalized solutions for industrial requirements in the service-oriented industrial internet of things (IIoT) architecture to achieve efficient utilization of service resources. Although great progress has been made, trust service coordination still faces challenging problems such as trustless industry service, poor coordination, and quality of service (QoS) personalized demand. In this paper, we propose a QoS-driven and adaptive trust service coordination method to implement Pareto-efficient allocation of limited industrial service resources in the background of the IIoT. First, we established a Pareto-effective and adaptive industrial IoT trust service coordination model and introduced a blockchain-based adaptive trust evaluation mechanism to achieve trust evaluation of industrial services. Then, taking advantage of a large and complex search space for solution efficiency, we introduced and compared multi-objective gray-wolf algorithms with the particle swarm optimization (PSO) and dragonfly algorithms. The experimental results showed that by judging and blacklisting malicious raters quickly and accurately, our model can efficiently realize self-adaptive, personalized, and intelligent trust service coordination under the given constraints, improving not only the response time, but also the success rate in coordination.
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33
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Li X, Wang Q, Dai HN, Wang H. A Novel Friendly Jamming Scheme in Industrial Crowdsensing Networks against Eavesdropping Attack. Sensors (Basel) 2018; 18:E1938. [PMID: 29904003 DOI: 10.3390/s18061938] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 06/08/2018] [Accepted: 06/11/2018] [Indexed: 11/28/2022]
Abstract
Eavesdropping attack is one of the most serious threats in industrial crowdsensing networks. In this paper, we propose a novel anti-eavesdropping scheme by introducing friendly jammers to an industrial crowdsensing network. In particular, we establish a theoretical framework considering both the probability of eavesdropping attacks and the probability of successful transmission to evaluate the effectiveness of our scheme. Our framework takes into account various channel conditions such as path loss, Rayleigh fading, and the antenna type of friendly jammers. Our results show that using jammers in industrial crowdsensing networks can effectively reduce the eavesdropping risk while having no significant influence on legitimate communications.
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34
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Sun Y, Guo Y, Li S, Wu D, Wang B. Optimal Resource Allocation for NOMA-TDMA Scheme with α-Fairness in Industrial Internet of Things. Sensors (Basel) 2018; 18:s18051572. [PMID: 29762506 PMCID: PMC5982539 DOI: 10.3390/s18051572] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 04/27/2018] [Accepted: 04/29/2018] [Indexed: 11/16/2022]
Abstract
In this paper, a joint non-orthogonal multiple access and time division multiple access (NOMA-TDMA) scheme is proposed in Industrial Internet of Things (IIoT), which allowed multiple sensors to transmit in the same time-frequency resource block using NOMA. The user scheduling, time slot allocation, and power control are jointly optimized in order to maximize the system α-fair utility under transmit power constraint and minimum rate constraint. The optimization problem is nonconvex because of the fractional objective function and the nonconvex constraints. To deal with the original problem, we firstly convert the objective function in the optimization problem into a difference of two convex functions (D.C.) form, and then propose a NOMA-TDMA-DC algorithm to exploit the global optimum. Numerical results show that the NOMA-TDMA scheme significantly outperforms the traditional orthogonal multiple access scheme in terms of both spectral efficiency and user fairness.
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Affiliation(s)
- Yanjing Sun
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Yiyu Guo
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Song Li
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Dapeng Wu
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Bin Wang
- Department of IoT Engineering, Xi'an University of Technology, Xi'an 710048, China.
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Ji C, Shao Q, Sun J, Liu S, Pan L, Wu L, Yang C. Device Data Ingestion for Industrial Big Data Platforms with a Case Study. Sensors (Basel) 2016; 16:279. [PMID: 26927121 DOI: 10.3390/s16030279] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2016] [Revised: 02/15/2016] [Accepted: 02/18/2016] [Indexed: 12/04/2022]
Abstract
Despite having played a significant role in the Industry 4.0 era, the Internet of Things is currently faced with the challenge of how to ingest large-scale heterogeneous and multi-type device data. In response to this problem we present a heterogeneous device data ingestion model for an industrial big data platform. The model includes device templates and four strategies for data synchronization, data slicing, data splitting and data indexing, respectively. We can ingest device data from multiple sources with this heterogeneous device data ingestion model, which has been verified on our industrial big data platform. In addition, we present a case study on device data-based scenario analysis of industrial big data.
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