survey

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

Authors:

Dimitra Politaki,

Karl John Pedersen,

Abdillah Suyuthi,

Abhilash Anand,

Amina ZiegenbeinAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 14s

Article No.: 327, Pages 1 - 38

https://doi.org/10.1145/3593043

Published: 17 July 2023 Publication History

Abstract

The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.

Primary Studies

[1]

Mohamed Ahmed, Chantal Taconet, Mohamed Ould, Sophie Chabridon, and Amel Bouzeghoub. 2021. IoT data qualification for a logistic chain traceability smart contract. Sensors 21, 6 (2021).

[2]

Daniele Apiletti, Claudia Barberis, Tania Cerquitelli, Alberto Macii, Enrico Macii, Massimo Poncino, and Francesco Ventura. 2018. iSTEP, an integrated self-tuning engine for predictive maintenance in Industry 4.0. In ISPA/IUCC/BDCloud/SocialCom/SustainCom’18. 924–931.

[3]

Shelernaz Azimi and Claus Pahl. 2020. A layered quality framework for machine learning-driven data and information models. In ICEIS (1). 579–587.

[4]

Oluwaseun Bamgboye, Xiaodong Liu, and Peter Cruickshank. 2019. Semantic stream management framework for data consistency in smart spaces. In COMPSAC’19. 85–90.

[5]

Sebastian Buschjäger and Katharina Morik. 2018. Decision tree and random forest implementations for fast filtering of sensor data. IEEE Transactions on Circuits and Systems I: Regular Papers 65, 1 (2018), 209–222.

[6]

John Byabazaire, Gregory O’Hare, and Declan Delaney. 2020. Using trust as a measure to derive data quality in data shared IoT deployments. In ICCCN’20. 1–9.

[7]

Roberto Casado-Vara, Fernando de la Prieta, Javier Prieto, and Juan M. Corchado. 2018. Blockchain framework for IoT data quality via edge computing. In BlockSys’18. 19–24.

Digital Library

[8]

T. Cerquitelli, N. Nikolakis, P. Bethaz, S. Panicucci, F. Ventura, E. Macii, S. Andolina, A. Marguglio, K. Alexopoulos, P. Petrali, A. Pagani, P. van Wilgen, and M. Ippolito. 2020. Enabling predictive analytics for smart manufacturing through an IIoT platform. In AMEST’20. 179–184.

[9]

David Camilo Corrales, Juan Carlos Corrales, and Agapito Ledezma. 2018. How to address the data quality issues in regression models: A guided process for data cleaning. Symmetry 10, 4 (2018).

[10]

Yesheng Cui, Sami Kara, and Ka C. Chan. 2020. Monitoring and control of unstructured manufacturing big data. In IEEM’20. 928–932.

[11]

Volkan Dedeoglu, Raja Jurdak, Guntur D. Putra, Ali Dorri, and Salil S. Kanhere. 2019. A trust architecture for blockchain in IoT. In MobiQuitous’19. 190–199.

[12]

Dominik Flick, Sebastian Gellrich, Marc-André Filz, Li Ji, Sebastian Thiede, and Christoph Herrmann. 2019. Conceptual framework for manufacturing data preprocessing of diverse input sources. In INDIN’19. 1041–1046.

[13]

Ziqi Guo, Tingwen Bao, Wenlong Wu, Chao Jin, and Jay Lee. 2019. IAI DevOps: A systematic framework for prognostic model lifecycle management. In PHM-Qingdao’19. 1–6.

[14]

Liu Hui, Ye Xiaobo, Meng Zhijun, Zhou Lijuan, and Sun Zhong. 2019. An agricultural machinery operation monitoring system based on IoT. In DSIT’19. 225–229.

[15]

Dan Huru, Cătălin Leordeanu, Elena Apostol, Mariana Mocanu, and Valentin Cristea. 2018. BigClue analytics: A middleware component for modeling sensor data in IoT systems. In HPCC/SmartCity/DSS’18. 891–896.

[16]

Seunghwan Jeong, Gwangpyo Yoo, Minjong Yoo, Ikjun Yeom, and Honguk Woo. 2019. Resource-efficient sensor data management for autonomous systems using deep reinforcement learning. Sensors 19, 20 (2019).

[17]

Mohammad Ayoub Khan and Fahad Algarni. 2020. A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access 8 (2020), 122259–122269.

[18]

Tianxiang Kong, Tianliang Hu, Tingting Zhou, and Yingxin Ye. 2021. Data construction method for the applications of workshop digital twin system. Journal of Manufacturing Systems 58 (2021), 323–328.

[19]

Marina Krotofil, Jason Larsen, and Dieter Gollmann. 2015. The process matters: Ensuring data veracity in cyber-physical systems. In ASIA CCS’15. 133–144.

Digital Library

[20]

Daniel Kuemper, Thorben Iggena, Ralf Toenjes, and Elke Pulvermueller. 2018. Valid.IoT: A framework for sensor data quality analysis and interpolation. In MMSys’18. 294–303.

Digital Library

[21]

Thomas Küfner, Stefan Schönig, Richard Jasinski, and Andreas Ermer. 2021. Vertical data continuity with lean edge analytics for Industry 4.0 production. Computers in Industry 125 (2021), 103389.

[22]

Fei Li, Stefan Nastic, and Schahram Dustdar. 2012. Data quality observation in pervasive environments. In ICCSE’12. 602–609.

[23]

Wei-Tsung Lin, Fatih Bakir, Chandra Krintz, Rich Wolski, and Markus Mock. 2019. Data repair for distributed, event-based IoT applications. In DEBS’19. 139–150.

Digital Library

[24]

Chao Liu, Léopold Le Roux, Carolin Körner, Olivier Tabaste, Franck Lacan, and Samuel Bigot. 2020. Digital twin-enabled collaborative data management for metal additive manufacturing systems. Journal of Manufacturing Systems (2020).

[25]

Carina Mieth, Anne Meyer, and Michael Henke. 2019. Framework for the usage of data from real-time indoor localization systems to derive inputs for manufacturing simulation. Procedia CIRP 81 (2019), 868–873.

[26]

Fatma Mohammed, A. S. M. Kayes, Eric Pardede, and Wenny Rahayu. 2020. A framework for measuring IoT data quality based on freshness metrics. In TrustCom’20. 1242–1249.

[27]

Nwamaka U. Okafor, Yahia Alghorani, and Declan T. Delaney. 2020. Improving data quality of low-cost IoT sensors in environmental monitoring networks using data fusion and machine learning approach. ICT Express 6, 3 (2020), 220–228.

[28]

Michael S. Packianather, Nury Leon Munizaga, Soha Zouwail, and Mark Saunders. 2019. Development of soft computing tools and IoT for improving the performance assessment of analysers in a clinical laboratory. In SoSE’19. 158–163.

[29]

Stefano Proto, Francesco Ventura, Daniele Apiletti, Tania Cerquitelli, Elena Baralis, Enrico Macii, and Alberto Macii. 2019. PREMISES, a scalable data-driven service to predict alarms in slowly-degrading multi-cycle industrial processes. In BigDataCongress’19. 139–143.

[30]

Qinglin Qi and Fei Tao. 2018. Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6 (2018), 3585–3593.

[31]

Luke Russell, Felix Kwamena, and Rafik Goubran. 2019. Towards reliable IoT: Fog-based AI sensor validation. In IEEE Cloud Summit. 37–44.

[32]

Maribel Yasmina Santos, Jorge Oliveira e Sá, Carina Andrade, Francisca Vale Lima, Eduarda Costa, Carlos Costa, Bruno Martinho, and João Galvão. 2017. A big data system supporting Bosch Braga Industry 4.0 strategy. International Journal of Information Management 37, 6 (2017), 750–760.

Digital Library

[33]

Sunny Sanyal and Puning Zhang. 2018. Improving quality of data: IoT data aggregation using device-to-device communications. IEEE Access 6 (2018), 67830–67840.

[34]

E. Saranya and P. Bagavathi Sivakumar. 2020. Data-driven prognostics for run-to-failure data employing machine learning models. In ICICT’20. 528–533.

[35]

Mahmoud Reza Saybani, Teh Ying Wah, Amineh Amini, and Saeed Reza Aghabozorgi Sahaf Yazdi. 2011. Anomaly detection and prediction of sensors faults in a refinery using data mining techniques and fuzzy logic. Scientific Research and Essays 6, 27 (2011), 5685–5695.

[36]

Elena Seghezzi, Mirko Locatelli, Laura Pellegrini, Giulia Pattini, Giuseppe Martino Di Giuda, Lavinia Chiara Tagliabue, and Guido Boella. 2021. Towards an occupancy-oriented digital twin for facility management: Test campaign and sensors assessment. Applied Sciences 11, 7 (2021).

[37]

Devarshi Shah, Jin Wang, and Q. Peter He. 2020. Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning. Computers & Chemical Engineering 141 (2020), 106970.

[38]

Sabrina Sicari, Cinzia Cappiello, Francesco De Pellegrini, Daniele Miorandi, and Alberto Coen-Porisini. 2016. A security-and quality-aware system architecture for Internet of Things. Information Systems Frontiers 18, 4 (2016), 665–677.

Digital Library

[39]

Sabrina Sicari, Alessandra Rizzardi, Daniele Miorandi, Cinzia Cappiello, and Alberto Coen-Porisini. 2016. A secure and quality-aware prototypical architecture for the Internet of Things. Information Systems 58 (2016), 43–55.

[40]

Muhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani, and Jongtae Rhee. 2018. Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors 18, 9 (2018).

[41]

Chen-Khong Tham and Rajalaxmi Rajagopalan. 2020. Active learning for IoT data prioritization in edge nodes over wireless networks. In IECON’20. 4453–4458.

[42]

K. Villalobos, V. J. Ramírez-Durán, B. Diez, J. M. Blanco, A. Goñi, and A. Illarramendi. 2020. A three level hierarchical architecture for an efficient storage of Industry 4.0 data. Computers in Industry 121 (2020), 103257.

Digital Library

[43]

Kevin Villalobos, Jon Vadillo, Borja Diez, Borja Calvo, and Arantza Illarramendi. 2018. I4TSPS: A visual-interactive web system for industrial time-series pre-processing. In Big Data’18. 2012–2018.

[44]

Chang Wang, Yongxin Zhu, Weiwei Shi, Victor Chang, P. Vijayakumar, Bin Liu, Yishu Mao, Jiabao Wang, and Yiping Fan. 2018. A dependable time series analytic framework for cyber-physical systems of IoT-based smart grid. ACM Transactions on Cyber-Physical Systems 3, 1 (2018), 18 pages.

[45]

Wei Wei, Jun Yuan, and Ang Liu. 2020. Manufacturing data-driven process adaptive design method. Procedia CIRP 91 (2020), 728–734.

[46]

Sholom M. Weiss, Amit Dhurandhar, and Robert J. Baseman. 2013. Improving quality control by early prediction of manufacturing outcomes. In KDD’13. 1258–1266.

Digital Library

[47]

Leon Wu and Gail Kaiser. 2012. An autonomic reliability improvement system for cyber-physical systems. In HASE’12. 56–61.

[48]

Wenjin Yu, Tharam Dillon, Fahed Mostafa, Wenny Rahayu, and Yuehua Liu. 2019. Implementation of industrial cyber physical system: Challenges and solutions. In ICPS’19. 173–178.

[49]

Wenjin Yu, Tharam Dillon, Fahed Mostafa, Wenny Rahayu, and Yuehua Liu. 2020. A global manufacturing big data ecosystem for fault detection in predictive maintenance. IEEE Transactions on Industrial Informatics 16, 1 (2020), 183–192.

[50]

Alejandro Gabriel Villanueva Zacarias, Peter Reimann, and Bernhard Mitschang. 2018. A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing. Procedia CIRP 72 (2018), 153–158.

[51]

Werner Zellinger, Volkmar Wieser, Mohit Kumar, David Brunner, Natalia Shepeleva, Rafa Gálvez, Josef Langer, Lukas Fischer, and Bernhard Moser. 2021. Beyond federated learning: On confidentiality-critical machine learning applications in industry. In ISM’20. 734–743.

References

[1]

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. Tensorflow: A system for large-scale machine learning. In OSDI’16. 265–283.

[2]

Hervé Abdi and Lynne J. Williams. 2010. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2, 4 (2010), 433–459.

Digital Library

[3]

Rasheed Ahmad and Izzat Alsmadi. 2021. Machine learning approaches to IoT security: A systematic literature review. Internet of Things 14 (2021), 100365.

[4]

Shahriar Akter, Grace McCarthy, Shahriar Sajib, Katina Michael, Yogesh K. Dwivedi, John D’Ambra, and K. N. Shen. 2021. Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management 60 (2021), 102387.

Digital Library

[5]

Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Khalid Al-Ali, Xiaojiang Du, Ihsan Ali, and Mohsen Guizani. 2020. A survey of machine and deep learning methods for Internet of Things (IoT) security. IEEE Communications Surveys & Tutorials 22, 3 (2020), 1646–1685.

[6]

Iqbal Alam, Kashif Sharif, Fan Li, Zohaib Latif, Md Monjurul Karim, Sujit Biswas, Boubakr Nour, and Yu Wang. 2020. A survey of network virtualization techniques for Internet of Things using SDN and NFV. ACM Computing Surveys (CSUR) 53, 2 (2020), 1–40.

Digital Library

[7]

Ahmed Abdulhasan Alwan, Mihaela Anca Ciupala, Allan J. Brimicombe, Seyed Ali Ghorashi, Andres Baravalle, and Paolo Falcarin. 2022. Data quality challenges in large-scale cyber-physical systems: A systematic review. Information Systems 105 (2022).

Digital Library

[8]

Mahmoud Ammar, Giovanni Russello, and Bruno Crispo. 2018. Internet of Things: A survey on the security of IoT frameworks. Journal of Information Security and Applications 38 (2018), 8–27.

[9]

Hamidreza Arasteh, Vahid Hosseinnezhad, Vincenzo Loia, Aurelio Tommasetti, Orlando Troisi, Miadreza Shafie-khah, and Pierluigi Siano. 2016. IoT-based smart cities: A survey. In 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC’16). 1–6.

[10]

Parvaneh Asghari, Amir Masoud Rahmani, and Hamid Haj Seyyed Javadi. 2019. Internet of Things applications: A systematic review. Computer Networks 148 (2019), 241–261.

[11]

Moslem Azamfar, Xiang Li, and Jay Lee. 2020. Deep learning-based domain adaptation method for fault diagnosis in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing 33, 3 (2020), 445–453.

[12]

Maggi Bansal, Inderveer Chana, and Siobhán Clarke. 2020. A survey on IoT big data: Current status, 13 v’s challenges, and future directions. ACM Computing Surveys (CSUR) 53, 6 (2020), 1–59.

Digital Library

[13]

Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, and Michael Grossniklaus. 2009. C-SPARQL: SPARQL for continuous querying. In WWW’09. 1061–1062.

Digital Library

[14]

Dina Bitton and David J. DeWitt. 1983. Duplicate record elimination in large data files. ACM Transactions on Database Systems 8, 2 (1983), 255–265.

Digital Library

[15]

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander. 2000. LOF: Identifying density-based local outliers. In MOD’00. 93–104.

[16]

Michael Cafarella, Ihab F. Ilyas, Marcel Kornacker, Tim Kraska, and Christopher Ré. 2016. Dark data: Are we solving the right problems?. In 2016 IEEE 32nd International Conference on Data Engineering (ICDE’16). IEEE, 1444–1445.

[17]

Apache Camel. [n.d.]. https://camel.apache.org/.

[18]

Jeremy J. Carroll, Ian Dickinson, Chris Dollin, Dave Reynolds, Andy Seaborne, and Kevin Wilkinson. 2004. Jena: Implementing the semantic web recommendations. In WWW’04. 74–83.

[19]

Z. Berkay Celik, Earlence Fernandes, Eric Pauley, Gang Tan, and Patrick McDaniel. 2019. Program analysis of commodity IoT applications for security and privacy: Challenges and opportunities. ACM Computing Surveys (CSUR) 52, 4 (2019), 1–30.

Digital Library

[20]

Hong Chen. 2017. Applications of cyber-physical system: A literature review. Journal of Industrial Integration and Management 2, 03 (2017), 1750012.

[21]

Zhiyan Chen, Jinxin Liu, Yu Shen, Murat Simsek, Burak Kantarci, Hussein T. Mouftah, and Petar Djukic. 2022. Machine learning-enabled iot security: Open issues and challenges under advanced persistent threats. Comput. Surveys 55, 5 (2022), 1–37.

Digital Library

[22]

Lalit Chettri and Rabindranath Bera. 2019. A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet of Things Journal 7, 1 (2019), 16–32.

[23]

Angelo Corallo, Anna Maria Crespino, Vito Del Vecchio, Mariangela Lazoi, and Manuela Marra. 2021. Understanding and defining dark data for the manufacturing industry. IEEE Transactions on Engineering Management (2021).

[24]

Create-IoT. 2018. Deliverable D6.02 – Recommendations for Commonalities and Interoperability Profiles of IoT Platforms. https://european-iot-pilots.eu/wp-content/uploads/2018/11/D06_02_WP06_H2020_CREATE-IoT_Final.pdf.

[25]

Li Da Xu, Wu He, and Shancang Li. 2014. Internet of Things in industries: A survey. IEEE Transactions on Industrial Informatics 10, 4 (2014), 2233–2243.

[26]

Nilanjan Dey, Amira S. Ashour, Fuqian Shi, Simon James Fong, and João Manuel R. S. Tavares. 2018. Medical cyber-physical systems: A survey. Journal of Medical Systems 42 (2018), 1–13.

Digital Library

[27]

Jasenka Dizdarević, Francisco Carpio, Admela Jukan, and Xavi Masip-Bruin. 2019. A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration. ACM Computing Surveys (CSUR) 51, 6 (2019), 1–29.

Digital Library

[28]

Alan F. Dutka and Howard H. Hansen. 1991. Fundamentals of Data Normalization. Addison-Wesley Longman Publishing Co., Inc.

Digital Library

[29]

Olakunle Elijah, Tharek Abdul Rahman, Igbafe Orikumhi, Chee Yen Leow, and M. H. D. Nour Hindia. 2018. An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of Things Journal 5, 5 (2018), 3758–3773.

[30]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD’96, 226–231.

[31]

Alejandro Germán Frank, Lucas Santos Dalenogare, and Néstor Fabián Ayala. 2019. Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics 210 (2019), 15–26.

[32]

Gregory Gimpel and Allan Alter. 2021. Benefit from the Internet of Things right now by accessing dark data. IT Professional 23, 2 (2021), 45–49.

[33]

Jairo Giraldo, Esha Sarkar, Alvaro A. Cardenas, Michail Maniatakos, and Murat Kantarcioglu. 2017. Security and privacy in cyber-physical systems: A survey of surveys. IEEE Design & Test 34, 4 (2017), 7–17.

[34]

Jairo Giraldo, David Urbina, Alvaro Cardenas, Junia Valente, Mustafa Faisal, Justin Ruths, Nils Ole Tippenhauer, Henrik Sandberg, and Richard Candell. 2018. A survey of physics-based attack detection in cyber-physical systems. ACM Computing Surveys (CSUR) 51, 4 (2018), 1–36.

Digital Library

[35]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Machine learning basics. Deep Learning 1, 7 (2016), 98–164.

[36]

Emily Grantner. 2007. ISO 8000: A standard for data quality. Logistics Spectrum 41, 4 (2007).

[37]

Venkat N. Gudivada, Srini Ramaswamy, and Seshadri Srinivasan. 2018. Data management issues in cyber-physical systems. In Transportation Cyber-Physical Systems. 173–200.

[38]

Antonio Gulli and Sujit Pal. 2017. Deep Learning with Keras. Packt Publishing Ltd.

Digital Library

[39]

Volkan Gunes, Steffen Peter, Tony Givargis, and Frank Vahid. 2014. A survey on concepts, applications, and challenges in cyber-physical systems. KSII Transactions on Internet and Information Systems (TIIS) 8, 12 (2014), 4242–4268.

[40]

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009. The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter 11, 1 (2009), 10–18.

Digital Library

[41]

Muneeb Ul Hassan, Mubashir Husain Rehmani, and Jinjun Chen. 2019. Differential privacy techniques for cyber physical systems: A survey. IEEE Communications Surveys & Tutorials 22, 1 (2019), 746–789.

Digital Library

[42]

Mardiana binti Mohamad Noor and Wan Haslina Hassan. 2019. Current research on Internet of Things (IoT) security: A survey. Computer Networks 148 (2019), 283–294.

[43]

Vikas Hassija, Vinay Chamola, Vikas Saxena, Divyansh Jain, Pranav Goyal, and Biplab Sikdar. 2019. A survey on IoT security: Application areas, security threats, and solution architectures. IEEE Access 7 (2019), 82721–82743.

[44]

Abhishek Hazra, Mainak Adhikari, Tarachand Amgoth, and Satish Narayana Srirama. 2021. A comprehensive survey on interoperability for IIoT: Taxonomy, standards, and future directions. ACM Computing Surveys (CSUR) 55, 1 (2021), 1–35.

Digital Library

[45]

Abdulmalik Humayed, Jingqiang Lin, Fengjun Li, and Bo Luo. 2017. Cyber-physical systems security–A survey. IEEE Internet of Things Journal 4, 6 (2017), 1802–1831.

[46]

DNV International. 2017. Data Quality Assessment Framework: DNV Recommended Practice-RP-0497. DNV.

[47]

Juxtology. 2018. IoT: Architecture. https://www.m2mology.com/iot-transformation/iot-world-forum/.

[48]

Apache Kafka. [n.d.]. https://kafka.apache.org/.

[49]

Aimad Karkouch, Hajar Mousannif, Hassan Al Moatassime, and Thomas Noel. 2016. Data quality in Internet of Things: A state-of-the-art survey. Journal of Network and Computer Applications 73 (2016), 57–81.

Digital Library

[50]

Mostafa Haghi Kashani, Mona Madanipour, Mohammad Nikravan, Parvaneh Asghari, and Ebrahim Mahdipour. 2021. A systematic review of IoT in healthcare: Applications, techniques, and trends. Journal of Network and Computer Applications 192 (2021), 103164.

Digital Library

[51]

Hakan Kayan, Matthew Nunes, Omer Rana, Pete Burnap, and Charith Perera. 2022. Cybersecurity of industrial cyber-physical systems: A review. ACM Computing Surveys (CSUR) 54, 11s (2022), 1–35.

Digital Library

[52]

Samina Khalid, Tehmina Khalil, and Shamila Nasreen. 2014. A survey of feature selection and feature extraction techniques in machine learning. In 2014 Science and Information Conference. 372–378.

[53]

Barbara Ann Kitchenham and Stuart Charters. 2007. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE 2007-001. https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf.

[54]

Laphou Lao, Zecheng Li, Songlin Hou, Bin Xiao, Songtao Guo, and Yuanyuan Yang. 2020. A survey of IoT applications in blockchain systems: Architecture, consensus, and traffic modeling. ACM Computing Surveys (CSUR) 53, 1 (2020), 1–32.

Digital Library

[55]

Daniel D. Lee and H. Sebastian Seung. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755 (1999), 788–791.

[56]

Shancang Li, Li Da Xu, and Shanshan Zhao. 2018. 5G Internet of Things: A survey. Journal of Industrial Information Integration 10 (2018), 1–9.

[57]

Shancang Li, Li Da Xu, and Shanshan Zhao. 2015. The Internet of Things: A survey. Information Systems Frontiers 17 (2015), 243–259.

Digital Library

[58]

Yongxin Liao, Fernando Deschamps, Eduardo de Freitas Rocha Loures, and Luiz Felipe Pierin Ramos. 2017. Past, present and future of industry 4.0-a systematic literature review and research agenda proposal. International Journal of Production Research 55, 12 (2017), 3609–3629.

[59]

Caihua Liu, Patrick Nitschke, Susan P. Williams, and Didar Zowghi. 2020. Data quality and the Internet of Things. Computing 102, 2 (2020), 573–599.

Digital Library

[60]

Yang Lu. 2017. Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration 6 (2017), 1–10.

[61]

Yuriy Zacchia Lun, Alessandro D’Innocenzo, Francesco Smarra, Ivano Malavolta, and Maria Domenica Di Benedetto. 2019. State of the art of cyber-physical systems security: An automatic control perspective. Journal of Systems and Software 149 (2019), 174–216.

[62]

Yuan Luo, Ya Xiao, Long Cheng, Guojun Peng, and Danfeng Yao. 2021. Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities. ACM Computing Surveys (CSUR) 54, 5 (2021), 1–36.

Digital Library

[63]

Benjamin Maschler, Hannes Vietz, Nasser Jazdi, and Michael Weyrich. 2020. Continual learning of fault prediction for turbofan engines using deep learning with elastic weight consolidation. In ETFA’20. 959–966.

[64]

Sara N. Matheu, Jose L Hernandez-Ramos, Antonio F. Skarmeta, and Gianmarco Baldini. 2020. A survey of cybersecurity certification for the Internet of Things. ACM Computing Surveys (CSUR) 53, 6 (2020), 1–36.

Digital Library

[65]

MathWorks Matlab. [n.d.]. https://mathworks.com/products/matlab.html.

[66]

Francesca Meneghello, Matteo Calore, Daniel Zucchetto, Michele Polese, and Andrea Zanella. 2019. IoT: Internet of threats? A survey of practical security vulnerabilities in real IoT devices. IEEE Internet of Things Journal 6, 5 (2019), 8182–8201.

[67]

Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, et al. 2016. Mllib: Machine learning in apache spark. Journal of Machine Learning Research 17, 1 (2016), 1235–1241.

Digital Library

[68]

Robert Mitchell and Ing-Ray Chen. 2014. A survey of intrusion detection techniques for cyber-physical systems. ACM Computing Surveys (CSUR) 46, 4 (2014), 1–29.

Digital Library

[69]

Supavit Muangjaroen and Thaweesak Yingthawornsuk. 2012. A study of noise reduction in speech signal using fir filtering. In International Conference on Advances in Electrical and Electronics Engineering.

[70]

Bruce Jay Nelson. 1981. Remote Procedure Call. Carnegie Mellon University.

Digital Library

[71]

Nataliia Neshenko, Elias Bou-Harb, Jorge Crichigno, Georges Kaddoum, and Nasir Ghani. 2019. Demystifying IoT security: An exhaustive survey on IoT vulnerabilities and a first empirical look on internet-scale IoT exploitations. IEEE Communications Surveys & Tutorials 21, 3 (2019), 2702–2733.

[72]

Anne H. Ngu, Mario Gutierrez, Vangelis Metsis, Surya Nepal, and Quan Z. Sheng. 2016. IoT middleware: A survey on issues and enabling technologies. IEEE Internet of Things Journal 4, 1 (2016), 1–20.

[73]

Phu H. Nguyen, Shaukat Ali, and Tao Yue. 2017. Model-based security engineering for cyber-physical systems: A systematic mapping study. Information and Software Technology 83 (2017), 116–135.

Digital Library

[74]

Apache NiFi. [n.d.]. https://nifi.apache.org/.

[75]

K. Nose-Filho, A. D. P. Lotufo, and C. R. Minussi. 2011. Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter. In IEEE Trondheim PowerTech. 1–7.

[76]

Sophocles J. Orfanidis. 2016. Introduction to Signal Processing. Pearson Education, Inc.

[77]

Ercan Oztemel and Samet Gursev. 2020. Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing 31 (2020), 127–182.

Digital Library

[78]

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.

Digital Library

[79]

Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. 2015. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology 64 (2015), 1–18.

Digital Library

[80]

Apache Phoenix. [n.d.]. https://phoenix.apache.org/.

[81]

The Node.js platform. [n.d.]. https://nodejs.org/en/.

[82]

Ioannis Prapas, Behrouz Derakhshan, Alireza Rezaei Mahdiraji, and Volker Markl. 2021. Continuous training and deployment of deep learning models. Datenbank-Spektrum 21, 3 (2021), 203–212.

[83]

NMEA 0183 Protocol. [n.d.]. https://www.nmea.org/content/STANDARDS/NMEA_0183_Standard.

[84]

REST. [n.d.]. https://restfulapi.net/.

[85]

Manuel Sanchez, Ernesto Exposito, and Jose Aguilar. 2020. Industry 4.0: Survey from a system integration perspective. International Journal of Computer Integrated Manufacturing 33, 10-11 (2020), 1017–1041.

[86]

Sajjad Hussain Shah and Ilyas Yaqoob. 2016. A survey: Internet of Things (IOT) technologies, applications and challenges. 2016 IEEE Smart Energy Grid Engineering (SEGE) (2016), 381–385.

[87]

Dudley Shapere. 1964. The structure of scientific revolutions. The Philosophical Review 73, 3 (1964), 383–394.

[88]

Weiwei Shi, Yongxin Zhu, Jinkui Zhang, Xiang Tao, Gehao Sheng, Yong Lian, Guoxing Wang, and Yufeng Chen. 2015. Improving power grid monitoring data quality: An efficient machine learning framework for missing data prediction. In HPCC/CSS/ICESS’15. 417–422.

[89]

Jeffrey S. Simonoff. 2012. Smoothing Methods in Statistics. Springer Science & Business Media.

[90]

Eugene Siow, Thanassis Tiropanis, and Wendy Hall. 2018. Analytics for the Internet of Things: A survey. ACM Computing Surveys (CSUR) 51, 4 (2018), 1–36.

Digital Library

[91]

Apache Spark. [n.d.]. https://spark.apache.org/.

[92]

Muhammad Talha, Anas Abou El Kalam, and Nabil Elmarzouqi. 2019. Big data: Trade-off between data quality and data security. Procedia Computer Science 151 (2019), 916–922.

Digital Library

[93]

Hui Yie Teh, Andreas W. Kempa-Liehr, and Kevin I-Kai Wang. 2020. Sensor data quality: A systematic review. Journal of Big Data 7, 1 (2020), 11.

[94]

Nguyen Khoi Tran, Quan Z. Sheng, Muhammad Ali Babar, and Lina Yao. 2017. Searching the Web OF Things: State of the art, challenges, and solutions. ACM Computing Surveys (CSUR) 50, 4 (2017), 55.

[95]

Nazar Waheed, Xiangjian He, Muhammad Ikram, Muhammad Usman, Saad Sajid Hashmi, and Muhammad Usman. 2020. Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures. ACM Computing Surveys (CSUR) 53, 6 (2020), 1–37.

Digital Library

[96]

Richard Y. Wang and Diane M. Strong. 1996. Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems 12, 4 (1996), 5–33.

Digital Library

[97]

Xi Wang and Chen Wang. 2019. Time series data cleaning: A survey. IEEE Access 8 (2019), 1866–1881.

[98]

Y Richard Wang, Lisa M. Guarascio, and Richard Wang. 1991. Dimensions of data quality: Toward quality data by design. (1991).

[99]

Claes Wohlin. 2014. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In EASE’14. 38.

[100]

Rich Wolski, Chandra Krintz, Fatih Bakir, Gareth George, and Wei-Tsung Lin. 2019. CSPOT: Portable, multi-scale functions-as-a-service for IoT. In SEC’19. 236–249.

Digital Library

[101]

Hui Xiong, Gaurav Pandey, Michael Steinbach, and Vipin Kumar. 2006. Enhancing data analysis with noise removal. IEEE Transactions on Knowledge and Data Engineering 18, 3 (2006), 304–319.

Digital Library

[102]

Hansong Xu, Wei Yu, David Griffith, and Nada Golmie. 2018. A survey on industrial Internet of Things: A cyber-physical systems perspective. IEEE Access 6 (2018), 78238–78259.

[103]

Li Da Xu and Lian Duan. 2019. Big data for cyber physical systems in industry 4.0: A survey. Enterprise Information Systems 13, 2 (2019), 148–169.

[104]

Li Da Xu, Eric L. Xu, and Ling Li. 2018. Industry 4.0: state of the art and future trends. International Journal of Production Research 56, 8 (2018), 2941–2962.

[105]

Lina Zhang, Dongwon Jeong, and Sukhoon Lee. 2021. Data quality management in the Internet of Things. Sensors 21, 17 (2021), 5834.

[106]

Ting Zheng, Marco Ardolino, Andrea Bacchetti, and Marco Perona. 2021. The applications of Industry 4.0 technologies in manufacturing context: A systematic literature review. International Journal of Production Research 59, 6 (2021), 1922–1954.

[107]

Qingyi Zhu, Seng W. Loke, Rolando Trujillo-Rasua, Frank Jiang, and Yong Xiang. 2019. Applications of distributed ledger technologies to the internet of things: A survey. ACM Computing Surveys (CSUR) 52, 6 (2019), 1–34.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 14s

December 2023

1355 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3606253

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 17 July 2023

Online AM: 19 April 2023

Accepted: 13 April 2023

Revised: 31 March 2023

Received: 20 May 2022

Published in CSUR Volume 55, Issue 14s

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Idrees RMaiti A(2024)A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber ModelApplied Sciences10.3390/app1413590714:13(5907)Online publication date: 5-Jul-2024
https://doi.org/10.3390/app14135907
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Bena YIbrahim RMahmood J(2024)Current Challenges of Big Data Quality Management in Big Data Governance: A Literature ReviewAdvances in Intelligent Computing Techniques and Applications10.1007/978-3-031-59711-4_15(160-172)Online publication date: 30-Jun-2024
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